mirror of
https://github.com/Farama-Foundation/Gymnasium.git
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eee9b28882
* add test showing mismatch in flattened space dtype and flattened point dtype * fix mismatch in flattened space dtype and flattened point dtype * fix typo * enhance test to detect when flattened dtype is incorrect * fix incorrect flattened dtype * remove inaccurate comment * change flatten to always use space.dtype * added testing for unflattened dtypes * fix unflatten dtypes * swtich flatten_space to use space.dtype for hardcoded space dtypes * fix failure in python 3.5
173 lines
8.1 KiB
Python
173 lines
8.1 KiB
Python
from collections import OrderedDict
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import numpy as np
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import pytest
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from gym.spaces import Box, Dict, Discrete, MultiBinary, MultiDiscrete, Tuple, utils
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@pytest.mark.parametrize(["space", "flatdim"], [
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(Discrete(3), 3),
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(Box(low=0., high=np.inf, shape=(2, 2)), 4),
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(Tuple([Discrete(5), Discrete(10)]), 15),
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(Tuple([Discrete(5), Box(low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float32)]), 7),
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(Tuple((Discrete(5), Discrete(2), Discrete(2))), 9),
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(MultiDiscrete([2, 2, 100]), 3),
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(MultiBinary(10), 10),
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(Dict({"position": Discrete(5),
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"velocity": Box(low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float32)}), 7),
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])
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def test_flatdim(space, flatdim):
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dim = utils.flatdim(space)
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assert dim == flatdim, "Expected {} to equal {}".format(dim, flatdim)
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@pytest.mark.parametrize("space", [
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Discrete(3),
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Box(low=0., high=np.inf, shape=(2, 2)),
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Tuple([Discrete(5), Discrete(10)]),
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Tuple([Discrete(5), Box(low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float32)]),
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Tuple((Discrete(5), Discrete(2), Discrete(2))),
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MultiDiscrete([2, 2, 100]),
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MultiBinary(10),
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Dict({"position": Discrete(5),
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"velocity": Box(low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float32)}),
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])
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def test_flatten_space_boxes(space):
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flat_space = utils.flatten_space(space)
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assert isinstance(flat_space, Box), "Expected {} to equal {}".format(type(flat_space), Box)
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flatdim = utils.flatdim(space)
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(single_dim, ) = flat_space.shape
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assert single_dim == flatdim, "Expected {} to equal {}".format(single_dim, flatdim)
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@pytest.mark.parametrize("space", [
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Discrete(3),
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Box(low=0., high=np.inf, shape=(2, 2)),
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Tuple([Discrete(5), Discrete(10)]),
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Tuple([Discrete(5), Box(low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float32)]),
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Tuple((Discrete(5), Discrete(2), Discrete(2))),
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MultiDiscrete([2, 2, 100]),
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MultiBinary(10),
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Dict({"position": Discrete(5),
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"velocity": Box(low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float32)}),
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])
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def test_flat_space_contains_flat_points(space):
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some_samples = [space.sample() for _ in range(10)]
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flattened_samples = [utils.flatten(space, sample) for sample in some_samples]
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flat_space = utils.flatten_space(space)
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for i, flat_sample in enumerate(flattened_samples):
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assert flat_sample in flat_space,\
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'Expected sample #{} {} to be in {}'.format(i, flat_sample, flat_space)
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@pytest.mark.parametrize("space", [
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Discrete(3),
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Box(low=0., high=np.inf, shape=(2, 2)),
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Tuple([Discrete(5), Discrete(10)]),
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Tuple([Discrete(5), Box(low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float32)]),
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Tuple((Discrete(5), Discrete(2), Discrete(2))),
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MultiDiscrete([2, 2, 100]),
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MultiBinary(10),
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Dict({"position": Discrete(5),
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"velocity": Box(low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float32)}),
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])
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def test_flatten_dim(space):
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sample = utils.flatten(space, space.sample())
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(single_dim, ) = sample.shape
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flatdim = utils.flatdim(space)
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assert single_dim == flatdim, "Expected {} to equal {}".format(single_dim, flatdim)
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@pytest.mark.parametrize("space", [
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Discrete(3),
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Box(low=0., high=np.inf, shape=(2, 2)),
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Tuple([Discrete(5), Discrete(10)]),
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Tuple([Discrete(5), Box(low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float32)]),
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Tuple((Discrete(5), Discrete(2), Discrete(2))),
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MultiDiscrete([2, 2, 100]),
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MultiBinary(10),
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Dict({"position": Discrete(5),
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"velocity": Box(low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float32)}),
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])
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def test_flatten_roundtripping(space):
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some_samples = [space.sample() for _ in range(10)]
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flattened_samples = [utils.flatten(space, sample) for sample in some_samples]
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roundtripped_samples = [utils.unflatten(space, sample) for sample in flattened_samples]
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for i, (original, roundtripped) in enumerate(zip(some_samples, roundtripped_samples)):
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assert compare_nested(original, roundtripped), \
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'Expected sample #{} {} to equal {}'.format(i, original, roundtripped)
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def compare_nested(left, right):
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if isinstance(left, np.ndarray) and isinstance(right, np.ndarray):
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return np.allclose(left, right)
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elif isinstance(left, OrderedDict) and isinstance(right, OrderedDict):
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res = len(left) == len(right)
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for ((left_key, left_value), (right_key, right_value)) in zip(left.items(), right.items()):
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if not res:
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return False
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res = left_key == right_key and compare_nested(left_value, right_value)
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return res
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elif isinstance(left, (tuple, list)) and isinstance(right, (tuple, list)):
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res = len(left) == len(right)
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for (x, y) in zip(left, right):
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if not res:
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return False
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res = compare_nested(x, y)
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return res
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else:
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return left == right
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'''
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Expecteded flattened types are based off:
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1. The type that the space is hardcoded as(ie. multi_discrete=np.int64, discrete=np.int64, multi_binary=np.int8)
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2. The type that the space is instantiated with(ie. box=np.float32 by default unless instantiated with a different type)
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3. The smallest type that the composite space(tuple, dict) can be represented as. In flatten, this is determined
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internally by numpy when np.concatenate is called.
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'''
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@pytest.mark.parametrize(["original_space", "expected_flattened_dtype"], [
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(Discrete(3), np.int64),
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(Box(low=0., high=np.inf, shape=(2, 2)), np.float32),
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(Box(low=0., high=np.inf, shape=(2, 2), dtype=np.float16), np.float16),
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(Tuple([Discrete(5), Discrete(10)]), np.int64),
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(Tuple([Discrete(5), Box(low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float32)]), np.float64),
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(Tuple((Discrete(5), Discrete(2), Discrete(2))), np.int64),
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(MultiDiscrete([2, 2, 100]), np.int64),
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(MultiBinary(10), np.int8),
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(Dict({"position": Discrete(5),
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"velocity": Box(low=np.array([0, 0]), high=np.array([1, 5]), dtype=np.float16)}), np.float64),
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])
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def test_dtypes(original_space, expected_flattened_dtype):
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flattened_space = utils.flatten_space(original_space)
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original_sample = original_space.sample()
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flattened_sample = utils.flatten(original_space, original_sample)
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unflattened_sample = utils.unflatten(original_space, flattened_sample)
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assert flattened_space.contains(flattened_sample), "Expected flattened_space to contain flattened_sample"
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assert flattened_space.dtype == expected_flattened_dtype, "Expected flattened_space's dtype to equal " \
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"{}".format(expected_flattened_dtype)
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assert flattened_sample.dtype == flattened_space.dtype, "Expected flattened_space's dtype to equal " \
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"flattened_sample's dtype "
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compare_sample_types(original_space, original_sample, unflattened_sample)
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def compare_sample_types(original_space, original_sample, unflattened_sample):
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if isinstance(original_space, Discrete):
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assert isinstance(unflattened_sample, int), "Expected unflattened_sample to be an int. unflattened_sample: " \
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"{} original_sample: {}".format(unflattened_sample, original_sample)
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elif isinstance(original_space, Tuple):
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for index in range(len(original_space)):
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compare_sample_types(original_space.spaces[index], original_sample[index], unflattened_sample[index])
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elif isinstance(original_space, Dict):
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for key, space in original_space.spaces.items():
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compare_sample_types(space, original_sample[key], unflattened_sample[key])
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else:
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assert unflattened_sample.dtype == original_sample.dtype, "Expected unflattened_sample's dtype to equal " \
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"original_sample's dtype. unflattened_sample: " \
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"{} original_sample: {}".format(unflattened_sample,
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original_sample)
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