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Testing Sequence Space Sampling Implementation in OpenAI Gym

A comprehensive test suite for validating the sequence sampling functionality in OpenAI Gym’s Sequence space. This test file ensures proper sequence generation with various length masks and validates error handling for invalid inputs.

Test Coverage Overview

The test suite provides thorough coverage of sequence sampling operations with different length specifications.

Integer-based length mask testing
NumPy array length mask validation
Error handling for negative lengths
Validation of multi-dimensional arrays
Type checking for length masks

Implementation Analysis

The testing approach utilizes pytest’s fixture and assertion mechanisms to verify sequence space behavior. The implementation follows a systematic pattern of testing positive cases followed by error conditions, using pytest’s raises context manager for exception validation.

Key patterns include:

Sequential length testing with range-based iterations
NumPy array integration for complex mask scenarios
Error message pattern matching using regex

Technical Details

Testing infrastructure leverages:

pytest for test execution and assertions
NumPy for array-based testing
gym.spaces module for sequence space implementation
Regular expressions for error message validation
Python’s built-in assertion mechanisms

Best Practices Demonstrated

The test suite exemplifies several testing best practices for space validation in Gym environments.

Comprehensive error case coverage
Clear test function documentation
Systematic test organization
Explicit error message validation
Proper type checking implementation

openai/gym

tests/spaces/test_sequence.py

            
import re

import numpy as np
import pytest

import gym.spaces


def test_sample():
    """Tests the sequence sampling works as expects and the errors are correctly raised."""
    space = gym.spaces.Sequence(gym.spaces.Box(0, 1))

    # Test integer mask length
    for length in range(4):
        sample = space.sample(mask=(length, None))
        assert sample in space
        assert len(sample) == length

    with pytest.raises(
        AssertionError,
        match=re.escape(
            "Expects the length mask to be greater than or equal to zero, actual value: -1"
        ),
    ):
        space.sample(mask=(-1, None))

    # Test np.array mask length
    sample = space.sample(mask=(np.array([5]), None))
    assert sample in space
    assert len(sample) == 5

    sample = space.sample(mask=(np.array([3, 4, 5]), None))
    assert sample in space
    assert len(sample) in [3, 4, 5]

    with pytest.raises(
        AssertionError,
        match=re.escape(
            "Expects the shape of the length mask to be 1-dimensional, actual shape: (2, 2)"
        ),
    ):
        space.sample(mask=(np.array([[2, 2], [2, 2]]), None))

    with pytest.raises(
        AssertionError,
        match=re.escape(
            "Expects all values in the length_mask to be greater than or equal to zero, actual values: [ 1  2 -1]"
        ),
    ):
        space.sample(mask=(np.array([1, 2, -1]), None))

    # Test with an invalid length
    with pytest.raises(
        TypeError,
        match=re.escape(
            "Expects the type of length_mask to an integer or a np.ndarray, actual type: <class 'str'>"
        ),
    ):
        space.sample(mask=("abc", None))