supermemo2 2.0.0

Implemented the SM-2 algorithm for spaced repetition learning.

SuperMemo2

A package that implemented the spaced repetition algorithm SM-2 for you to quickly calculate your next review date for whatever you are learning.

📌  Note: The algorithm SM-2 doesn't equal to the computer implementation SuperMemo2. In fact, the 3 earliest implementations (SuperMemo1, SuperMemo2 and SuperMemo3) all used algorithm SM-2. I didn't notice that when I first published the package on PyPI, and I can't change the package name.

📦  PyPI page

Table of Contents

• Motivation
• Installing and Supported Versions
• A Simple Example
• Features
• What is SM-2?
• Code Reference
• Testing
• Changelog
• Credits

Motivation

The goal was to have an efficient way to calculate the next review date for studying/learning. Removes the burden of remembering the algorithm, equations, and math from the users.

Installation and Supported Versions

Package Install

Install and upate the package using pip:

pip3 install -U supermemo2

To Play Around with the Code

Download the code:

git clone https://github.com/alankan886/SuperMemo2.git

Install dependencies to run the code:

pip3 install -r requirements.txt

supermemo2 supports Python 3+

A Simple Example

from supermemo2 import SMTwo

# first review
# using quality=4 as an example, read below for what each value from 0 to 5 represents
# review date would default to date.today() if not provided
review = SMTwo.first_review(4, "2021-3-14")
# review prints SMTwo(easiness=2.36, interval=1, repetitions=1, review_date=datetime.date(2021, 3, 15))

# second review
review = SMTwo(review.easiness, review.interval, review.repetitions).review(4, "2021-3-14")
# review prints similar to example above.

Features

📣  Calculates the review date of the task following the SM-2 algorithm.
📣  The first_review method to calculate the review date at ease without having to know the initial values.

What is SM-2?

🎥  If you are curious of what spaced repetition is, check this short video out.

📌  A longer but interactive article on spaced repetition learning.

📎  The SM-2 Algorithm

What are the "values"?

The values are the:

• Quality: The quality of recalling the answer from a scale of 0 to 5.
  • 5: perfect response.
  • 4: correct response after a hesitation.
  • 3: correct response recalled with serious difficulty.
  • 2: incorrect response; where the correct one seemed easy to recall.
  • 1: incorrect response; the correct one remembered.
  • 0: complete blackout.
• Easiness: The easiness factor, a multipler that affects the size of the interval, determine by the quality of the recall.
• Interval: The gap/space between your next review.
• Repetitions: The count of correct response (quality >= 3) you have in a row.

Code Reference

class supermemo2.SMTwo(easiness, interval, repetitions)

Parameters:

• easiness (float) - the easiness determines the interval.
• interval (int) - the interval between the latest review date and the next review date.
• repetitions (int) - the count of consecutive reviews with quality larger than 2.

first_review( quality, review_date=None, date_fmt=None )

      Static method that calcualtes the next review date for the first review without having to know the initial values, and returns a dictionary containing the new values.

Parameters:

• quality (int) - the recall quality of the review.
• review_date (str or datetime.date) - optional parameter, the date of the review.
• date_fmt (string) - optional parameter, the format of the review_date. Formats like year_mon_day, mon_day_year and day_mon_year.

Returns: dictionary containing values like quality, easiness, interval, repetitions and review_date.

Return Type: Dict

Usage:

from supermemo2 import SMTwo, mon_day_year
# using default date date.today()
SMTwo.first_review(3)

# providing string date in Year-Month-Day format
SMTwo.first_review(3, "2021-12-01")

# providing string date in Month-Day-Year format
SMTwo.first_review(3, "12-01-2021", mon_day_year)

# providing date object date
from datetime import date
d = date(2021, 12, 1)
SMTwo.first_review(3, d)

review( quality, review_date=None, date_fmt=None )

      Calcualtes the next review date based on previous values, and returns a dictionary containing the new values.

Parameters:

• quality (int) - the recall quality of the review.
• review_date (str or datetime.date) - optional parameter, the date of the review.
• date_fmt (string) - optional parameter, the format of the review_date. Formats like year_mon_day, mon_day_year and day_mon_year.

Returns: dictionary containing values like quality, easiness, interval, repetitions and review_date.

Return Type: Dict

Usage:

from supermemo2 import SMTwo, mon_day_year
# using previous values from first_review call
r = SMTwo.first_review(3)

# using default date date.today()
SMTwo(r.easiness, r.interval, r.repetitions).review(3)

# providing string date in Year-Month-Day format
SMTwo(r.easiness, r.interval, r.repetitions).review(3, "2021-12-01")

# providing string date in Month-Day-Year format
SMTwo(r.easiness, r.interval, r.repetitions).review(3, "12-01-2021", mon_day_year)

# providing date object date
from datetime import date
d = date(2021, 12, 1)
SMTwo(r.easiness, r.interval, r.repetitions).review(3, d)

Testing

Assuming you dowloaded the code and installed requirements.

Run the tests

pytest tests/

Check test coverages

pytest --cov

Check coverage on Codecov.

Changelog

2.0.0 (2021-03-28): Major changes/rebuild, Update recommended

• Rebuilt and simplfied the package.

1.0.3 (2021-01-30): Minor bug fix, Update recommended

• Re-evaluate the default date argument to first_review() on each call.

1.0.2 (2021-01-18): Major and Minor bug fix, Update recommended

• Add required attrs package version to setup.py.
• Allow users to access SMTwo model.
• Fix E-Factor calculation when q < 3.

1.0.1 (2021-01-02): Fix tests, update README and add Github actions, Update not required

• Add missing assertions to test_api.py.
• Update README badges and fix format.
• Add Github actions to run tests against Python versions 3.6 to 3.9 in different OS, and upload coverage to Codecov.

1.0.0 (2021-01-01): Complete rebuild, Update recommended

• Build a new SMTwo class using the attrs package.
• Provide API methods to quickly access the SMTwo class.
• Develop 100% coverage integration and unit tests in a TDD manner.
• Write new documentation.

0.1.0 (2020-07-14): Add tests, Update not required

• Add passing unit tests with a coverage of 100%.

0.0.4 (2020-07-10): Minor bug fix, Update recommended

• Fix interval calculation error when q < 3.

0.0.3 (2020-07-06): Documentation Update, Update not required

• Add new section about SM-2 in documentation, and fix some formats in README.

0.0.2 (2020-07-05): Refactor feature, Update recommended

• Refactor the supermemo2 algorithm code into a simpler structure, and remove unnecessary methods in the class.

0.0.1 (2020-07-02): Feature release

• Initial Release

Credits

1. pytest
2. The SM-2 Algorithm