Error margin is set up 0.55 and 5, but assert_approx_equals accepts maximum of relative/absolute error, which means the fixed tests accept +-0.55, and the random tests will accept everything.

It also means no training is neccessary to pass the kata: see this

    @test.it("Test Neural Network Initialization")
    def _():
        nn = SimpleNeuralNetwork(3, 4, 2)
        test.assert_equals(len(nn.hidden_weights), 3)
        test.assert_equals(len(nn.hidden_weights[0]), 4)
        test.assert_equals(len(nn.output_weights), 4)
        test.assert_equals(len(nn.output_weights[0]), 2)

    @test.it("Test Forward Pass")
    def _():
        nn = SimpleNeuralNetwork(3, 4, 2)
        inputs = np.random.rand(1, 3)
        output = nn.forward_pass(inputs)
        test.assert_equals(output.shape, (1, 2))

These tests are testing the specific implementation details that was not mentioned in the description at all.

Meanwhile, calculate_loss is not tested at all. So it's very unclear what is actually meant to be tested.

No random tests. Also percentage is always 100 in the current tests, so the grade_percentage method is not properly tested.

Trivial kata, a simple class inheritance with some properties and string formatting has been done before.

Trivial kata, finding an item in a list (in a list) has been done before

obstacles in random tests are in a completely different format: in sample test it is

[
    { x1: 1, y1: 1, x2: 3, y2: 3, reduction: 20 }
]

But in random tests it is something like

[
  {
    obstacles: [
      { x1: 1, y1: 3, x2: 6, y2: 5, reduction: 40 },
      { x1: 2, y1: 2, x2: 6, y2: 9, reduction: 32 }
    ],
    source: { x: 1, y: 1 },
    reports: [
      { x: 0, y: 0, noiseLevel: 0 },
      { x: 1, y: 0, noiseLevel: 0 },
      { x: 2, y: 0, noiseLevel: 0 },
      { x: 3, y: 0, noiseLevel: 0 },
      ...
    ]
  },
  ...
]

All noise levels are non-negative whole numbers.

In actual tests:

should calculate the correct noise level at a point

The noise level calculation is incorrect: expected 0 to equal 33.333333333333336

33.333333333333336 is not a integer.

The description also never describes how noiseLevelAtPoint should calculate its value. Assuming inverse square law and 100 initial noise level the noise level would be 100 / 2, not 100 / 3.

The tests are coupling noiseLevelAtPoint and locateNoisyStudent: noiseLevelAtPoint is only tested in actual tests, but generateReports depends on noiseLevelAtPoint, so if it is incorrectly implemented locateNoisyStudent will receive invalid input, making sample tests useless. This is a very inappropriate design for testing.