refactoring: splitting conftest logic into submodules

test/conftest.py

@@ -1,36 +1,27 @@
-import json
 import logging
 import os
-import random
-from functools import partial
-from itertools import starmap
-from operator import itemgetter
 
 import fpdf
 import numpy as np
 import pytest
-from funcy import rcompose
 
-from image_prediction.exceptions import (
-    UnknownLabelFormat,
-)
-from image_prediction.image_extractor.extractor import ImageMetadataPair
 from image_prediction.info import Info
-from image_prediction.label_mapper.mappers.probability import ProbabilityMapperKeys
 from image_prediction.locations import TEST_DATA_DIR
 from image_prediction.pipeline import load_pipeline
 from image_prediction.utils import get_logger
 from test.utils.generation.pdf import add_image, pdf_stream
-from test.utils.label import map_labels
 
 pytest_plugins = [
     "test.fixtures.image",
+    "test.fixtures.image_metadata_pair",
     "test.fixtures.input",
     "test.fixtures.label",
     "test.fixtures.metadata",
     "test.fixtures.model",
     "test.fixtures.model_store",
     "test.fixtures.parameters",
+    "test.fixtures.pdf",
+    "test.fixtures.target",
 ]
 
 
@@ -43,97 +34,16 @@ def mute_logger():
     logger.setLevel(level)
 
 
-@pytest.fixture
-def expected_predictions_mapped(
-    label_format, batch_of_expected_string_labels, batch_of_expected_label_to_probability_mappings
-):
-    if label_format == "index":
-        return batch_of_expected_string_labels
-    elif label_format == "probability":
-        return batch_of_expected_label_to_probability_mappings
-    else:
-        raise UnknownLabelFormat(f"No label mapper for label format {label_format} was specified.")
-
-
-@pytest.fixture
-def expected_predictions(label_format, batch_of_expected_numeric_labels, batch_of_expected_probability_arrays):
-    if label_format == "index":
-        return batch_of_expected_numeric_labels
-    elif label_format == "probability":
-        return batch_of_expected_probability_arrays
-    else:
-        raise UnknownLabelFormat(f"No label mapper for label format {label_format} was specified.")
-
-
 @pytest.fixture
 def input_batch(batch_size, input_size):
     return np.random.random_sample(size=(batch_size, *input_size))
 
 
-@pytest.fixture(params=[0, 1, 2, 16, 32])
-def batch_size(request):
-    return request.param
-
-
-@pytest.fixture
-def batch_of_expected_string_labels(batch_of_expected_numeric_labels, classes):
-    return map_labels(batch_of_expected_numeric_labels, classes)
-
-
-@pytest.fixture
-def batch_of_expected_numeric_labels(batch_size, classes):
-    return random.choices(range(len(classes)), k=batch_size)
-
-
-@pytest.fixture
-def batch_of_expected_label_to_probability_mappings(batch_of_expected_probability_arrays, classes):
-    def map_probabilities(probabilities):
-        lbl2prob = dict(sorted(zip(classes, map(rounder, probabilities)), key=itemgetter(1), reverse=True))
-        most_likely = [*lbl2prob][0]
-        return {ProbabilityMapperKeys.LABEL: most_likely, ProbabilityMapperKeys.PROBABILITIES: lbl2prob}
-
-    rounder = rcompose(partial(np.round, decimals=4), float)
-    return list(map(map_probabilities, batch_of_expected_probability_arrays))
-
-
-@pytest.fixture
-def batch_of_expected_probability_arrays(batch_size, classes):
-    return [np.random.uniform(size=len(classes)) for _ in range(batch_size)]
-
-
-@pytest.fixture
-def output_batch_generator(expected_predictions):
-    return iter(expected_predictions)
-
-
-@pytest.fixture
-def metadata_plus_mapped_prediction(expected_predictions_mapped, metadata):
-    return [{"classification": epm, **mdt} for epm, mdt in zip(expected_predictions_mapped, metadata)]
-
-
-@pytest.fixture
-def metadata_formatted_plus_mapped_prediction_formatted(expected_predictions_mapped_and_formatted, metadata_formatted):
-    return [
-        {"classification": epm, **mdt}
-        for epm, mdt in zip(expected_predictions_mapped_and_formatted, metadata_formatted)
-    ]
-
-
-@pytest.fixture
-def expected_predictions_mapped_and_formatted(expected_predictions_mapped):
-    return [{k.value: v for k, v in epm.items()} for epm in expected_predictions_mapped]
-
-
 @pytest.fixture
 def info_label_map():
     return Info
 
 
-@pytest.fixture
-def image_metadata_pairs(images, metadata):
-    return list(starmap(ImageMetadataPair, zip(images, metadata)))
-
-
 @pytest.fixture
 def pdf(image_metadata_pairs):
     pdf = fpdf.FPDF(unit="pt")
@@ -150,12 +60,6 @@ def real_pdf():
         yield f.read()
 
 
-@pytest.fixture
-def real_expected_service_response():
-    with open(os.path.join(TEST_DATA_DIR, "f2dc689ca794fccb8cd38b95f2bf6ba9_predictions.json"), "r") as f:
-        yield json.load(f)
-
-
 @pytest.fixture
 def pipeline():
     pipeline = load_pipeline(verbose=False)

test/fixtures/image_metadata_pair.py

@@ -0,0 +1,10 @@
+from itertools import starmap
+
+import pytest
+
+from image_prediction.image_extractor.extractor import ImageMetadataPair
+
+
+@pytest.fixture
+def image_metadata_pairs(images, metadata):
+    return list(starmap(ImageMetadataPair, zip(images, metadata)))

test/fixtures/parameters.py

@@ -30,4 +30,9 @@ def height(request):
 
 @pytest.fixture(params=[10, 31])
 def width(request):
+    return request.param
+
+
+@pytest.fixture(params=[0, 1, 2, 16, 32])
+def batch_size(request):
     return request.param

test/fixtures/target.py

@@ -0,0 +1,91 @@
+import json
+import os
+import random
+from functools import partial
+from operator import itemgetter
+
+import numpy as np
+import pytest
+from funcy import rcompose
+
+from image_prediction.exceptions import UnknownLabelFormat
+from image_prediction.label_mapper.mappers.probability import ProbabilityMapperKeys
+from image_prediction.locations import TEST_DATA_DIR
+from test.utils.label import map_labels
+
+
+@pytest.fixture
+def expected_predictions_mapped(
+    label_format, batch_of_expected_string_labels, batch_of_expected_label_to_probability_mappings
+):
+    if label_format == "index":
+        return batch_of_expected_string_labels
+    elif label_format == "probability":
+        return batch_of_expected_label_to_probability_mappings
+    else:
+        raise UnknownLabelFormat(f"No label mapper for label format {label_format} was specified.")
+
+
+@pytest.fixture
+def expected_predictions(label_format, batch_of_expected_numeric_labels, batch_of_expected_probability_arrays):
+    if label_format == "index":
+        return batch_of_expected_numeric_labels
+    elif label_format == "probability":
+        return batch_of_expected_probability_arrays
+    else:
+        raise UnknownLabelFormat(f"No label mapper for label format {label_format} was specified.")
+
+
+@pytest.fixture
+def batch_of_expected_string_labels(batch_of_expected_numeric_labels, classes):
+    return map_labels(batch_of_expected_numeric_labels, classes)
+
+
+@pytest.fixture
+def batch_of_expected_numeric_labels(batch_size, classes):
+    return random.choices(range(len(classes)), k=batch_size)
+
+
+@pytest.fixture
+def batch_of_expected_label_to_probability_mappings(batch_of_expected_probability_arrays, classes):
+    def map_probabilities(probabilities):
+        lbl2prob = dict(sorted(zip(classes, map(rounder, probabilities)), key=itemgetter(1), reverse=True))
+        most_likely = [*lbl2prob][0]
+        return {ProbabilityMapperKeys.LABEL: most_likely, ProbabilityMapperKeys.PROBABILITIES: lbl2prob}
+
+    rounder = rcompose(partial(np.round, decimals=4), float)
+    return list(map(map_probabilities, batch_of_expected_probability_arrays))
+
+
+@pytest.fixture
+def batch_of_expected_probability_arrays(batch_size, classes):
+    return [np.random.uniform(size=len(classes)) for _ in range(batch_size)]
+
+
+@pytest.fixture
+def output_batch_generator(expected_predictions):
+    return iter(expected_predictions)
+
+
+@pytest.fixture
+def metadata_plus_mapped_prediction(expected_predictions_mapped, metadata):
+    return [{"classification": epm, **mdt} for epm, mdt in zip(expected_predictions_mapped, metadata)]
+
+
+@pytest.fixture
+def metadata_formatted_plus_mapped_prediction_formatted(expected_predictions_mapped_and_formatted, metadata_formatted):
+    return [
+        {"classification": epm, **mdt}
+        for epm, mdt in zip(expected_predictions_mapped_and_formatted, metadata_formatted)
+    ]
+
+
+@pytest.fixture
+def expected_predictions_mapped_and_formatted(expected_predictions_mapped):
+    return [{k.value: v for k, v in epm.items()} for epm in expected_predictions_mapped]
+
+
+@pytest.fixture
+def real_expected_service_response():
+    with open(os.path.join(TEST_DATA_DIR, "f2dc689ca794fccb8cd38b95f2bf6ba9_predictions.json"), "r") as f:
+        yield json.load(f)