serhii.net

• DataMeasurementsTool - a Hugging Face Space by huggingface
• Introducing the Data Measurements Tool: an Interactive Tool for Looking at Datasets

Really nice, and the blog post introducing it has a lot of general info about datasets that I found very interesting.

From python - How do I type hint a method with the type of the enclosing class? - Stack Overflow:

If you have a classmethod and want to annotate the return value as that same class you’re now defining, you can actually do the logical thing!

from __future__ import annotations

class Whatever:
	# ...
	@classmethod what(cls) -> Whatever:
		return cls()

Kohen’s Kappa

• sklearn.metrics.cohen_kappa_score — scikit-learn 1.1.1 documentation
• Extremely clear explanation: Cohen’s Kappa Statistic - Statistics How To
• Cohen’s Kappa. Understanding Cohen’s Kappa coefficient | by Kurtis Pykes | Towards Data Science
• Important bits:
  • It includes the likelihood that a correct guess happens randomly
  • Only 2 annotators
  • Compares classification on the same items - so as input it gets y_1 and y_2 of the same length with matching annos

It started with writing type hints for a complex dict, which led me to TypedDict, slowly went into “why can’t I just do a dataclass as with the rest”.

Found two libraries:

• pydantic
  • Really nice esp. for validation
  • MyClass(**somedict)
  • Couldn’t find an easy way to parse nested dataclasses from the same dict
• Dataclass Wizard
  • Basically a wrapper to read/save (nested!) dataclasses from dicts / json.
  • Does one thing and does it well!
  • When dumping, converted my field_names to camelcase fieldNames, one can disable that from settings: Extending from Meta — Dataclass Wizard 0.22.1 documentation

TIL another bit I won’t ever use: 21. for/else — Python Tips 0.1 documentation

This exists:

for a in whatveer:
	a.whatever()
else:
	print("Whatever is empty!")

Found it after having a wrong indentation of an else that put it inside the for loop.

Found at least three:

• portion · PyPI
  • Seems to be more about operations
• GitHub - brentp/interlap: fast, pure-python interval overlap testing
  • More focused on querying
• intervaltree · PyPI
  • Too about querying

Spent hours tracking down a bug that boiled down to:

A if args.sth.lower == "a" else B

Guess what - args.sth.lower is a callable, and will never be equal to a string. So args.sth.lower == "a" is always False.

Of course I needed args.sth.lower().

Python sets have two kinds of methods:

• a.intersection(b) which returns the intersection
• a.intersection_update(b) which updates a by removing elements not found in b.

It calls the function-like ones (that return the result) operators, as opposed to the ‘update_’ ones.

(Built-in Types — Python 3.10.5 documentation)

Previously: 220622-1744 Directory structure for python research-y projects, 220105-1142 Order of directories inside a python project

Datasets.

HF has recommendations about how to Structure your repository, where/how to put .csv/.json files in various splits/shards/configurations.

These dataset structures are also ones that can be easily loaded with load_dataset(), despite being CSV/JSON files.

Filenames containing ’train’ are considered part of the train split, same for ’test’ and ‘valid’

And indeed I could without issues create a Dataset through ds = datasets.load_dataset(my_directory_with_jsons).

Given an argument -l, I needed to pass multiple values to it.

python - How can I pass a list as a command-line argument with argparse? - Stack Overflow is an extremely detailed answer with all options, but the TL;DR is:

1. nargs:

parser.add_argument('-l','--list', nargs='+', help='<Required> Set flag', required=True)
# Use like:
# python arg.py -l 1234 2345 3456 4567

2. append:

parser.add_argument('-l','--list', action='append', help='<Required> Set flag', required=True)
# Use like:
# python arg.py -l 1234 -l 2345 -l 3456 -l 4567

Details about values for nargs:

# This is the correct way to handle accepting multiple arguments.
# '+' == 1 or more.
# '*' == 0 or more.
# '?' == 0 or 1.
# An int is an explicit number of arguments to accept.
parser.add_argument('--nargs', nargs='+')

Related, a couple of days ago used nargs to allow an empty value (explicitly passing -o without an argument that becomes a None) while still providing a default value that’s used if -o is omitted completely:

    parser.add_argument(
        "--output-dir",
        "-o",
        help="Target directory for the converted .json files. (%(default)s)",
        type=Path,
        default=DEFAULT_OUTPUT_DIR,
        nargs="?",  
    )