JosephSBoyle

"""Library for generating latex commands for use as variables.

Example:

>>> generate_newcommand2(prefix="", name="logisticRegressionAccuracy", value=77.779)
\newcommand{logisticRegressionAccuracy}{77.8}

This is most useful if you dump the outputs into a file, e.g.:

```variables.tex

JosephSBoyle

-- Basic, bootstrappable neovim config for Python programming
-- place in `~/.config/nvim/`

local lazypath = vim.fn.stdpath("data") .. "/lazy/lazy.nvim"
if not vim.loop.fs_stat(lazypath) then
  vim.fn.system({
    "git", "clone", "--filter=blob:none",
    "https://github.com/folke/lazy.nvim.git", lazypath
  })
end

JosephSBoyle

from sklearn.metrics import recall_score

# Specificity is just recall for the negative class
#
# This is a valid scorer object and can be used as a `scorer` in, for example,
# sklearn.model_selection.cross_validate etc.

def specificity_score(y_true, y_pred):
    # assumes that your negative class has label `0`.
    return recall_score(y_true, y_pred, pos_label=0)

JosephSBoyle

import logging

# Set up the logger
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)

# Create a file handler and set its log level
from datetime import datetime
from pathlib import Path

JosephSBoyle

print(f"\nArray Shapes:\n{'':=>50}"); [print(f"{k:<20}: {str(type(v).__name__):<7}:",  tuple(v.shape)) for k, v in locals().items() if hasattr(v, "shape") and not hasattr(getattr(v, "shape", None), "__call__")]  # Exclude the 'numpy' import, the `shape` func. isn't relevant
# Array Shapes:
# ==================================================
# input_ids           : Tensor : (8, 1, 250)
# attention_mask      : Tensor : (8, 1, 250)
# labels              : Tensor : (8, 8921)
# input_ids1          : Tensor : (8, 250)
# x0                  : Tensor : (8, 250, 768)
# x1                  : Tensor : (8, 50, 251)
# x2                  : Tensor : (8, 251, 50)

JosephSBoyle

import functools
import hashlib
import inspect

from datasets import load_dataset


def cache_invariant(map_method):
    """Wrap the `map` method s.t it only caches the values if the `function` arg is the same."""
    @functools.wraps(map_method)