ARC environment classes

ARC Fitness

Factory

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ARCFitnessFactory

 ARCFitnessFactory ()

Initialize self. See help(type(self)) for accurate signature.

BaseARCFitness

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BaseARCFitness

 BaseARCFitness ()

Base class of an ARCFitness. This class is not used directly by developers, but defines the functionality common to all.

SumSquareOfDiff

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SumSquareOfDiff

 SumSquareOfDiff ()

A function that sums the square of the diffrences of two arrays.

AverageMaxOfDiff

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AverageMaxOfDiff

 AverageMaxOfDiff ()

A function that sums the square of the diffrences of two arrays.

Euclidean

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Euclidean

 Euclidean ()

A function that sums the square of the diffrences of two arrays.

# import random
# random.seed(1)
np.random.seed(1)
# Create a 2-dimensional 3 by 3 numpy array of random floats from 0 to 9
random_array = np.random.uniform(4, 5, (3, 3))
print(random_array)
array_of_fours = np.full((3, 3), 4)
print(array_of_fours)
euc = Euclidean()
metric = euc(array_of_fours, random_array,  ['cells'])
print(metric)
np.random.seed(2)
random_array = np.random.uniform(4.49, 4.5, (3, 3))
print(random_array)
metric = euc(array_of_fours, random_array,  ['cells'])
print(metric)

[[4.417022   4.72032449 4.00011437]
 [4.30233257 4.14675589 4.09233859]
 [4.18626021 4.34556073 4.39676747]]
[[4 4 4]
 [4 4 4]
 [4 4 4]]
1.0610244787055576
[[4.49435995 4.49025926 4.49549662]
 [4.49435322 4.49420368 4.49330335]
 [4.49204649 4.49619271 4.49299655]]
1.4810793133120121

ARCDataProcessor

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ARCDataProcessor

 ARCDataProcessor (config_dict, arc_dict)

Initialize self. See help(type(self)) for accurate signature.

# Example usage:
print('Example using dims only')
config_dict = {
    'control_set': ['dims'],
    'input_set': ['env'],
    'dataset': 'train',
    'index': 0
}

arc_dict = {
    'test': [{'input': [[7, 0, 7], [7, 0, 7], [7, 7, 0]]}],
    'train': [
        {'input': [[0, 7, 7], [7, 7, 7], [0, 7, 7]], 'output': [[0, 0, 0, 0, 7, 7, 0, 7, 7], [0, 0, 0, 7, 7, 7, 7, 7, 7], [0, 0, 0, 0, 7, 7, 0, 7, 7], [0, 7, 7, 0, 7, 7, 0, 7, 7], [7, 7, 7, 7, 7, 7, 7, 7, 7], [0, 7, 7, 0, 7, 7, 0, 7, 7], [0, 0, 0, 0, 7, 7, 0, 7, 7], [0, 0, 0, 7, 7, 7, 7, 7, 7], [0, 0, 0, 0, 7, 7, 0, 7, 7]]}
        # Add more entries as needed
    ]
}

gp = ARCDataProcessor(config_dict, arc_dict)
info = gp.create_info()
print(info)
ins = gp.get_env_inputs_names()
print('names', ins)
inds = gp.get_env_inputs_indexes()
print('indexes', inds)

state, info = gp.get_state()
print(info)
print('fitness', gp.fitness_function(), state)
print()

for i in range(-2,-4,-1):
    actions = [i]
    gp.apply_actions(actions)
    state, info = gp.get_state()
    print(info)
    # print(len(values))
    print('fitness', gp.fitness_function(), state)
    print()

Example using dims only
{'num_actions': 1, 'grid_shape': 'equal', 'dims': 1}
names ['IWE']
indexes [0]
{'num_actions': 1, 'grid_shape': 'equal', 'dims': 1}
fitness 72 {'inputs': {'dims': {'env': (3,)}}}

{'num_actions': 1, 'grid_shape': 'equal', 'dims': 1}
fitness 128 {'inputs': {'dims': {'env': (1,)}}}

{'num_actions': 1, 'grid_shape': 'equal', 'dims': 1}
fitness 128 {'inputs': {'dims': {'env': (1,)}}}

# Example usage:
print('Example using env inputs only')
config_dict = {
    'control_set': ['cells'],
    'input_set': ['env'],
    'dataset': 'train',
    'index': 0
}

gp = ARCDataProcessor(config_dict, arc_dict)
info = gp.create_info()
print(info)
ins = gp.get_env_inputs_names()
print('names', ins)
inds = gp.get_env_inputs_indexes()
print('indexes', inds)

state, info = gp.get_state()
print(info)
print('fitness', gp.fitness_function(), state)
print()

for i in range(-2,-4,-1):
    actions = [i for j in range(info['num_actions'])]
    print(actions)
    gp.apply_actions(actions)
    state, info = gp.get_state()
    print(info)
    # print(len(values))
    print('fitness', gp.fitness_function(), state)
    print()

Example using env inputs only
{'num_actions': 9, 'env': (3, 3)}
names ['IE001', 'IE002', 'IE003', 'IE004', 'IE005', 'IE006', 'IE007', 'IE008', 'IE009']
indexes [0, 1, 2, 3, 4, 5, 6, 7, 8]
{'num_actions': 9, 'env': (3, 3)}

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[52], line 21
     19 state, info = gp.get_state()
     20 print(info)
---> 21 print('fitness', gp.fitness_function(), state)
     22 print()
     24 for i in range(-2,-4,-1):

Cell In[50], line 225, in ARCDataProcessor.fitness_function(self)
    223 output_array = self.get_output(self.dataset)
    224 env_array = self.env
--> 225 return self.fitness_function_arrays(output_array, env_array)

Cell In[50], line 196, in ARCDataProcessor.fitness_function_arrays(self, output_array, env_array)
    193 def fitness_function_arrays(self, output_array, env_array):
--> 196     return self.fitness_function_type(output_array = output_array, env_array = env_array, control_set = self.control_set)

Cell In[46], line 12, in SumSquareOfDiff.__call__(self, output_array, env_array, control_set)
     10 element_metric = 0
     11 if 'cells' in control_set:
---> 12     diff = env_array[:output_array.shape[0], :output_array.shape[1]] - output_array
     13     diff = np.where(np.isnan(diff), 0, diff)
     14     diff = np.where(np.isnan(env_array[:output_array.shape[0], :output_array.shape[1]]), 10, diff)

ValueError: operands could not be broadcast together with shapes (3,3) (9,9) 

# Example usage:
print('Example using cells only')
config_dict = {
    'control_set': ['cells'],
    'input_set': ['env','inputs'],
    # 'input_set': ['env','inputs', 'outputs'],
    'dataset': 'train',
    'index': 0
}

gp = ARCDataProcessor(config_dict, arc_dict)
info = gp.create_info()
print(info)

ins = gp.get_env_inputs_names()
print('names', ins)
inds = gp.get_env_inputs_indexes()
print('indexes', inds)

state, info = gp.get_state()
print(info)
print('fitness', gp.fitness_function(), state)
print()

Example using cells only
{'num_actions': 9, 'env': (3, 3), 'inputs': (3, 3)}
names ['IE001', 'IE002', 'IE003', 'IE004', 'IE005', 'IE006', 'IE007', 'IE008', 'IE009', 'II001', 'II002', 'II003', 'II004', 'II005', 'II006', 'II007', 'II008', 'II009']
indexes [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]
{'num_actions': 9, 'env': (3, 3), 'inputs': (3, 3)}
fitness 2143.0 {'inputs': {'cells': {'env': array([[0., 7., 7.],
       [7., 7., 7.],
       [0., 7., 7.]], dtype=float32), 'inputs': array([[0, 7, 7],
       [7, 7, 7],
       [0, 7, 7]])}}}

ARCEnv

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ARCEnv

 ARCEnv (namespace='')

*The main OpenAI Gym class.

It encapsulates an environment with arbitrary behind-the-scenes dynamics. An environment can be partially or fully observed.

The main API methods that users of this class need to know are:

• :meth:step - Takes a step in the environment using an action returning the next observation, reward, if the environment terminated and observation information.
• :meth:reset - Resets the environment to an initial state, returning the initial observation and observation information.
• :meth:render - Renders the environment observation with modes depending on the output
• :meth:close - Closes the environment, important for rendering where pygame is imported

And set the following attributes:

• :attr:action_space - The Space object corresponding to valid actions
• :attr:observation_space - The Space object corresponding to valid observations
• :attr:reward_range - A tuple corresponding to the minimum and maximum possible rewards
• :attr:spec - An environment spec that contains the information used to initialise the environment from gym.make
• :attr:metadata - The metadata of the environment, i.e. render modes
• :attr:np_random - The random number generator for the environment

Note: a default reward range set to :math:(-\infty,+\infty) already exists. Set it if you want a narrower range.*

Example

import pygame

# Example usage:
if os.name == 'nt': 
    props = {'dir': 'C:\\packages\\arc-prize-2024', 'file_prefix':'arc-agi_training_', 'code':'007bbfb7', 'control_set': ['dims'], 'input_set': ['env'],'dataset': 'train'}
    file_name = os.path.join(props['dir'], props['file_prefix']) + 'challenges.json' 
    challenges_manager = ChallengesDataManager(file_name)
    data = challenges_manager.get_data_for_key(props['code'])
    print(data)
    # arc_dict={}
    # arc_dict['data'] = data
    arc_env = ARCEnv()
    arc_env.initialise(props, data)
    arc_env.render()
    #    print(state, fitness, done)
    # print(arc_env.dimensions)
    for i in range(6):
        state, fitness, done, info = arc_env.step([1])
        # print(state, fitness, done)
        print(state, fitness, done, info)
        arc_env.render()
        sleep(1)

{'test': [{'input': [[7, 0, 7], [7, 0, 7], [7, 7, 0]]}], 'train': [{'input': [[0, 7, 7], [7, 7, 7], [0, 7, 7]], 'output': [[0, 0, 0, 0, 7, 7, 0, 7, 7], [0, 0, 0, 7, 7, 7, 7, 7, 7], [0, 0, 0, 0, 7, 7, 0, 7, 7], [0, 7, 7, 0, 7, 7, 0, 7, 7], [7, 7, 7, 7, 7, 7, 7, 7, 7], [0, 7, 7, 0, 7, 7, 0, 7, 7], [0, 0, 0, 0, 7, 7, 0, 7, 7], [0, 0, 0, 7, 7, 7, 7, 7, 7], [0, 0, 0, 0, 7, 7, 0, 7, 7]]}, {'input': [[4, 0, 4], [0, 0, 0], [0, 4, 0]], 'output': [[4, 0, 4, 0, 0, 0, 4, 0, 4], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 0, 0, 0, 0, 0, 4, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 4, 0, 4, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 4, 0, 0, 0, 0]]}, {'input': [[0, 0, 0], [0, 0, 2], [2, 0, 2]], 'output': [[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 2, 0, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 2], [2, 0, 2, 0, 0, 0, 2, 0, 2]]}, {'input': [[6, 6, 0], [6, 0, 0], [0, 6, 6]], 'output': [[6, 6, 0, 6, 6, 0, 0, 0, 0], [6, 0, 0, 6, 0, 0, 0, 0, 0], [0, 6, 6, 0, 6, 6, 0, 0, 0], [6, 6, 0, 0, 0, 0, 0, 0, 0], [6, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 6, 0, 0, 0, 0, 0, 0], [0, 0, 0, 6, 6, 0, 6, 6, 0], [0, 0, 0, 6, 0, 0, 6, 0, 0], [0, 0, 0, 0, 6, 6, 0, 6, 6]]}, {'input': [[2, 2, 2], [0, 0, 0], [0, 2, 2]], 'output': [[2, 2, 2, 2, 2, 2, 2, 2, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 2, 0, 2, 2, 0, 2, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 2, 2, 2, 2, 2], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 2, 2, 0, 2, 2]]}]}
{'inputs': {'dims': {'env': (4,)}}} 50 True {'num_actions': 1, 'grid_shape': 'equal', 'dims': 1}
{'inputs': {'dims': {'env': (5,)}}} 32 True {'num_actions': 1, 'grid_shape': 'equal', 'dims': 1}
{'inputs': {'dims': {'env': (6,)}}} 18 True {'num_actions': 1, 'grid_shape': 'equal', 'dims': 1}
{'inputs': {'dims': {'env': (7,)}}} 8 True {'num_actions': 1, 'grid_shape': 'equal', 'dims': 1}
{'inputs': {'dims': {'env': (8,)}}} 2 True {'num_actions': 1, 'grid_shape': 'equal', 'dims': 1}
{'inputs': {'dims': {'env': (9,)}}} 0 True {'num_actions': 1, 'grid_shape': 'equal', 'dims': 1}

sleep(2)
arc_env.close()