re-arc/utils.py

import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap, Normalize

from random import choice, randint, sample, shuffle, uniform

from dsl import *


global rng
rng = []


def unifint(
    diff_lb: float,
    diff_ub: float,
    bounds: Tuple[int, int]
) -> int:
    """
    diff_lb: lower bound for difficulty, must be in range [0, diff_ub]
    diff_ub: upper bound for difficulty, must be in range [diff_lb, 1]
    bounds: interval [a, b] determining the integer values that can be sampled
    """
    a, b = bounds
    d = uniform(diff_lb, diff_ub)
    global rng
    rng.append(d)
    return min(max(a, round(a + (b - a) * d)), b)


def is_grid(
    grid: Any
) -> bool:
    """
    returns True if and only if argument is a valid grid
    """
    if not isinstance(grid, tuple):
        return False
    if not len(grid) > 0:
        return False
    if not all(isinstance(r, tuple) for r in grid):
        return False
    if not all(0 < len(r) <= 30 for r in grid):
        return False
    if not len(set(len(r) for r in grid)) == 1:
        return False
    if not all(all(isinstance(x, int) for x in r) for r in grid):
        return False
    if not all(all(0 <= x <= 9 for x in r) for r in grid):
        return False
    return True


def strip_prefix(
    string: str,
    prefix: str
) -> str:
    """
    removes prefix
    """
    return string[len(prefix):]


def format_grid(
    grid: List[List[int]]
) -> Grid:
    """
    grid type casting
    """
    return tuple(tuple(row) for row in grid)


def format_example(
    example: dict
) -> dict:
    """
    example data type
    """
    return {
        'input': format_grid(example['input']),
        'output': format_grid(example['output'])
    }


def format_task(
    task: dict
) -> dict:
    """
    task data type
    """
    return {
        'train': [format_example(example) for example in task['train']],
        'test': [format_example(example) for example in task['test']]
    }


def plot_task(
    task: List[dict],
    title: str = None
) -> None:
    """
    displays a task
    """
    cmap = ListedColormap([
        '#000', '#0074D9', '#FF4136', '#2ECC40', '#FFDC00',
        '#AAAAAA', '#F012BE', '#FF851B', '#7FDBFF', '#870C25'
    ])
    norm = Normalize(vmin=0, vmax=9)
    args = {'cmap': cmap, 'norm': norm}
    height = 2
    width = len(task)
    figure_size = (width * 3, height * 3)
    figure, axes = plt.subplots(height, width, figsize=figure_size)
    for column, example in enumerate(task):
        axes[0, column].imshow(example['input'], **args)
        axes[1, column].imshow(example['output'], **args)
        axes[0, column].axis('off')
        axes[1, column].axis('off')
    if title is not None:
        figure.suptitle(title, fontsize=20)
    plt.subplots_adjust(wspace=0.1, hspace=0.1)
    plt.show()


def fix_bugs(
    dataset: dict
) -> None:
    """
    fixes bugs in the original ARC training dataset
    """
    dataset['a8d7556c']['train'][2]['output'] = fill(dataset['a8d7556c']['train'][2]['output'], 2, {(8, 12), (9, 12)})
    dataset['6cf79266']['train'][2]['output'] = fill(dataset['6cf79266']['train'][2]['output'], 1, {(6, 17), (7, 17), (8, 15), (8, 16), (8, 17)})
    dataset['469497ad']['train'][1]['output'] = fill(dataset['469497ad']['train'][1]['output'], 7, {(5, 12), (5, 13), (5, 14)})
    dataset['9edfc990']['train'][1]['output'] = fill(dataset['9edfc990']['train'][1]['output'], 1, {(6, 13)})
    dataset['e5062a87']['train'][1]['output'] = fill(dataset['e5062a87']['train'][1]['output'], 2, {(1, 3), (1, 4), (1, 5), (1, 6)})
    dataset['e5062a87']['train'][0]['output'] = fill(dataset['e5062a87']['train'][0]['output'], 2, {(5, 2), (6, 3), (3, 6), (4, 7)})