arc-py - types and utilities for dealing with ARC Challenge in python

arc-py package provides types and utilities to handle the ARC Challenge.

It helps you convert the original .json files to numpy arrays, view them (and any edits or generation you might do) with matplotlib. It also suggests an interface for an agent competing in ARC Challenge, and offers evaluation function.

Install it by running pip install arc-py.

Accessing the data

Upon installing arc-py, you will get the original .json files locally. You can find the folders containing them as from arc import train_data_dir, eval_data_dir. However, if you are going to be using python, probably you will find it more convenient to get the data as 2D numpy arrays, grouped logically under two sets of problems, train and validation. These are available as from arc import train_problems, validation_problems.

Data Types

arc-py introduces a few types to structure the data in the challenge:

• arc.types.ArcProblem represent one task - made of demonstrations (prob.train_pairs) and tests (prob.test_pairs).
• arc.types.ArcIOPair is a unit of the demonstrations/tests making up a problem - it's the input grid (pair.x) and corresponding output grid (pair.y).
• arc.types.ArcGrid is an alias for np.ndarray. Specifically, an ArcGrid must be 2D, have dimensions between 1 to 30 each, and only have integers in range [0, 9] as it's elements. To verify some numpy array complies with this spec, a check function is offered: arc.types.verify_is_arc_grid.

Viewing data with matplotlib

arc-py provides few basics to view the grids with matplotlib (consistent with the web view in the original repo):

• arc.plot.plot_grid can plot a single 2D grid
• arc.types.ArcIOPair.plot() will show a input/output pair

import numpy as np
from matplotlib import pyplot as plt
from arc.plot import plot_grid


grid = np.zeros([4,4], dtype=np.uint8)

for i in range(4):
    grid[i,i] = 3

plot_grid(grid)
plt.show()

Constructing agents and evaluating results

Agent API

The concept for an agent is the following - it's the program you develop/train by looking on training problems, which then goes on to be scored on validation problems. Per the rules of original kaggle competition:

1. a problem can contain multiple test grids.
2. it's acceptable to make up to 3 guesses as to what the answer to a given test grid could be.

Since the agent must not be shown the answers to test grids, we give it following signature:

    def predict(
        self, demo_pairs: List[ArcIOPair], test_grids: List[ArcGrid]
    ) -> List[ArcPrediction]:

Here, ArcPrediction is a list[ArcGrid] - containing 1 to 3 guesses. First element of the output list corresponds to the first test grid, and so on (meaning for the return value result, you could match inputs and outputs with x, y in zip(test_grids, result)).

Evaluation

arc-py offers a class that can keep track of your accuracy and few auxillary target metrics: arc.evaluation.ArcEvaluationResult. It's best demonstrated with an example:

def evaluate_agent(
    agent: ArcAgent, problems: List[ArcProblem] = validation_problems
) -> ArcEvaluationResult:

    result = ArcEvaluationResult()
    for prob in problems:
        pred = agent.predict(prob.train_pairs, prob.test_inputs)
        result.add_answer(prob, pred)

    return result

Just print the ArcEvaluationResult object to see the results. Sample output:

ARC results for 400 problems. Stats:
Accuracy                 : 2.2%
Accuracy(at least one)   : 2.5%
Correct answer shape     : 52.4%

Examples

for an example of a project using arc-py, see https://github.com/ikamensh/solve_arc

View training examples:

from arc import train_problems, validation_problems, describe_task_group

describe_task_group(train_problems)
describe_task_group(validation_problems)

for n, task in enumerate(train_problems, start=1):
    for i, pair in enumerate(task.train_pairs, start=1):
        pair.plot(show=True, title=f"Task {n}: Demo {i}")

    for i, pair in enumerate(task.test_pairs, start=1):
        pair.plot(show=True, title=f"Task {n}: Test {i}")

Note: The ARC challenge is designed for the developer not knowing the test problems. If you know those problems, you will overfit to them. We recommend not to view the evaluation set.

View output of a random agent

from typing import List
import numpy as np
from arc.types import ArcIOPair, ArcGrid, ArcPrediction
from arc.agents import ArcAgent


class RandomAgent(ArcAgent):
    """Makes random predicitons. Low chance of success. """

    def predict(
            self, demo_pairs: List[ArcIOPair], test_grids: List[ArcGrid]
    ) -> List[ArcPrediction]:
        """We are allowed to make up to 3 guesses per challange rules. """
        outputs = []
        for tg in test_grids:
            out_shape = tg.shape
            out1 = np.random.randint(0, 9, out_shape)
            out2 = np.random.randint(0, 9, out_shape)
            out3 = np.random.randint(0, 9, out_shape)
            outputs.append([out1, out2, out3])
        return outputs


from arc import train_problems

p1 = next(iter(train_problems))  # problem #1
agent = RandomAgent()
outs = agent.predict(p1.train_pairs, p1.test_inputs)

for test_pair, predicitons in zip(p1.test_pairs, outs):
    for p in predicitons:
        prediction = ArcIOPair(test_pair.x, p)
        prediction.plot(show=True)

Evaluate the random agent

from arc.agents import RandomAgent, CheatingAgent
from arc.evaluation import evaluate_agent


agent = RandomAgent()
results = evaluate_agent(agent)
print(results)
assert results.accuracy < 0.5
assert results.accuracy_any < 0.5
assert results.shape_accuracy > 0  # random agent guesses the shape of some outputs correctly

ARC results for 400 problems. Stats:
Accuracy : 0.0%
Accuracy(at least one) : 0.0%
Correct answer shape : 67.5%