unittests enforcing i/o of classifier methods

tests/test_classify.py

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+from datetime import datetime
+from types import SimpleNamespace
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from stages.classify import BaseClassifier, WhaleClassifier, InferenceClient
+from unittest.mock import patch
+
+
+@pytest.fixture
+def example_config():
+    return SimpleNamespace(
+        general = SimpleNamespace(
+            show_plots=True,
+        ),
+        audio = SimpleNamespace(source_sample_rate=16_000),
+        classify = SimpleNamespace(
+            batch_duration=30, # seconds
+            hydrophone_sensitivity=-168.8,
+            model_sample_rate=10_000,
+            model_url="http://127.0.0.1:5000/predict",
+            plot_scores=True,
+            plot_path_template="data/plots/results/{year}/{month:02}/{plot_name}.png",
+            med_filter_size=3,
+        ),
+    )
+
+@pytest.fixture
+def example_input_row_small():
+    audio = np.array([0., 1., 2., 3., 4., 5., 4., 3., 2., 1.]) # bigger than max_duration
+    start_time = datetime(2024, 7, 7, 23, 8, 0)
+    end_time = datetime(2024, 7, 7, 23, 18, 0)
+    encounter_ids = ["encounter1", "encounter2"]
+    yield  audio, start_time, end_time, encounter_ids
+
+@pytest.fixture
+def example_input_row_large():
+    audio = np.array([0, 1, 2, 3, 4, 5, 4, 3, 2, 1]*16_500*60).astype(np.float32) # bigger than max_duration
+    start_time = datetime(2024, 7, 7, 23, 8, 0)
+    end_time = datetime(2024, 7, 7, 23, 18, 0)
+    encounter_ids = ["encounter1", "encounter2"]
+    yield  audio, start_time, end_time, encounter_ids
+
+
+@pytest.fixture
+def example_grouped_outputs():
+    return {
+        "20240707T230800-231800_encounter1_encounter2": [0.3, 0.7, 0.2, 0.6]
+    }
+
+
+def test_preprocess_small(example_config, example_input_row_small):
+    # Arrange
+    input_row = example_input_row_small
+
+    expected = (
+        "20240707T230800-231800_encounter1_encounter2",
+        # the resampled array 
+        np.array([[0.12715314],[1.56746149],[3.21961117],[4.73285103],[3.70353985],[1.95126152],[0.]])
+    )
+
+    # Act
+    actual_generator = BaseClassifier(example_config)._preprocess(input_row)
+    actual = next(actual_generator)
+
+    # Assert
+    assert expected[0] == actual[0]
+    assert expected[1].shape == actual[1].shape
+    np.testing.assert_almost_equal(expected[1], actual[1], decimal=6)
+
+
+def test_preprocess_large(example_config, example_input_row_large):
+    """
+    Instead of writing out the expected array, just check for length and shape
+    """
+
+    # Arrange
+    input_row = example_input_row_large
+    batch_duration      = example_config.classify.batch_duration
+    model_sample_rate   = example_config.classify.model_sample_rate
+
+    expected = (
+        "20240707T230800-231800_encounter1_encounter2",
+        # large inputs yield fixed sized batches
+        np.zeros((batch_duration*model_sample_rate, 1))
+    )
+
+    # Act
+    actual_generator = BaseClassifier(example_config)._preprocess(input_row)
+    actual = next(actual_generator)
+
+    # Assert
+    assert expected[0] == actual[0]
+    assert expected[1].shape == actual[1].shape
+
+
+def test_postprocess(example_config, example_input_row_small, example_grouped_outputs):
+    # Arrange
+    input_row = example_input_row_small
+
+    expected = (
+        np.array([0., 1., 2., 3., 4., 5., 4., 3., 2., 1.]), # audio
+        datetime(2024, 7, 7, 23, 8, 0),                     # start_time
+        datetime(2024, 7, 7, 23, 18, 0),                    # end_time
+        ["encounter1", "encounter2"],                       # encounter_ids
+        [0.3, 0.7, 0.2, 0.6]                                # scores
+    )
+
+    # Act
+    actual = WhaleClassifier(example_config)._postprocess(input_row, example_grouped_outputs)
+
+    # Assert
+    assert len(expected) == len(actual)
+    for e, a in zip(expected, actual):
+        if isinstance(e, np.ndarray):
+            np.testing.assert_almost_equal(e, a, decimal=6)
+        else:
+            assert e == a
+
+
+def test_resample(example_config):
+    # Arrange
+    audio = np.array([0., 1., 2., 3., 4., 5., 4., 3., 2., 1.])
+    example_config.audio.source_sample_rate = 16_000
+    example_config.classify.model_sample_rate = 10_000
+
+    expected = np.array([0.127153, 1.567461, 3.219611, 4.732851, 3.70354 , 1.951262, 0.])
+
+    # Act
+    actual = BaseClassifier(example_config)._resample(audio)
+
+    # Assert
+    np.testing.assert_almost_equal(expected, actual, decimal=6)
+
+
+def test_resample_same_rate(example_config):
+    # Arrange
+    audio = np.array([0., 1., 2., 3., 4., 5., 4., 3., 2., 1.])
+    example_config.audio.source_sample_rate = 10_000
+    example_config.classify.model_sample_rate = 10_000
+
+    expected = np.array([0., 1., 2., 3., 4., 5., 4., 3., 2., 1.])
+
+    # Act
+    actual = BaseClassifier(example_config)._resample(audio)
+
+    # Assert
+    np.testing.assert_almost_equal(expected, actual, decimal=6)
+
+
+@patch('stages.classify.requests.post')
+def test_process(mock_post, example_config):
+    # Arrange
+    mock_response = SimpleNamespace(
+        json=lambda: {"key": "cool_looking_key", "predictions": [0.3, 0.7, 0.2, 0.6]},
+        status_code=200,
+        raise_for_status=lambda: None
+    )
+    mock_post.return_value = mock_response
+
+    input_element = (
+        "cool_looking_key",  # key 
+        np.array([[0.12715314],[1.56746149],[3.21961117],[4.73285103],[3.70353985],[1.95126152],[0.]])  # batch
+    )
+
+    expected = (
+        "cool_looking_key",             # key
+        np.array([0.3, 0.7, 0.2, 0.6])  # scores
+    )
+
+    # Act
+    actual_genreator = InferenceClient(example_config).process(input_element)
+    actual = next(actual_genreator) 
+
+    # Assert
+    assert expected[0] == actual[0]
+    np.testing.assert_almost_equal(expected[1], actual[1], decimal=6)
+