test: string representation base classes

tests/popmon/base/test_module.py

@@ -1,32 +1,41 @@
 import numpy as np
+import pytest
 
 from popmon.base import Module
 
 
-def test_popmon_module():
-    class Scaler(Module):
-        _input_keys = ("input_key",)
-        _output_keys = ("output_key",)
+class Scaler(Module):
+    _input_keys = ("input_key",)
+    _output_keys = ("output_key",)
 
-        def __init__(self, input_key, output_key, mean, std):
-            super().__init__()
-            self.input_key = input_key
-            self.output_key = output_key
-            self.mean = mean
-            self.std = std
+    def __init__(self, input_key, output_key, mean, std):
+        super().__init__()
+        self.input_key = input_key
+        self.output_key = output_key
+        self.mean = mean
+        self.std = std
 
-        def transform(self, input_array: np.ndarray):
-            res = input_array - np.mean(input_array)
-            res = res / np.std(res)
-            res = res * self.std
-            res = res + self.mean
-            return res
+    def transform(self, input_array: np.ndarray):
+        res = input_array - np.mean(input_array)
+        res = res / np.std(res)
+        res = res * self.std
+        res = res + self.mean
+        return res
 
-    test_module = Scaler(input_key="x", output_key="scaled_x", mean=2.0, std=0.3)
 
+@pytest.fixture
+def test_module():
+    return Scaler(input_key="x", output_key="scaled_x", mean=2.0, std=0.3)
+
+
+def test_popmon_module(test_module):
     datastore = {"x": np.arange(10)}
     datastore = test_module.transform(datastore)
 
     assert "x" in datastore  # check if key 'x' is still in the datastore
     np.testing.assert_almost_equal(np.mean(datastore["scaled_x"]), 2.0, decimal=5)
     np.testing.assert_almost_equal(np.std(datastore["scaled_x"]), 0.3, decimal=5)
+
+
+def test_popmon_module_repr(test_module):
+    assert str(test_module) == "Scaler(input_key='x', output_key='scaled_x')"

tests/popmon/base/test_pipeline.py

@@ -1,6 +1,7 @@
 import logging
 
 import numpy as np
+import pytest
 
 from popmon.base import Module, Pipeline
 
@@ -65,16 +66,12 @@ def transform(self, input_array: np.ndarray, weights: np.ndarray):
         return result
 
 
-def test_popmon_pipeline():
+@pytest.fixture
+def test_pipeline():
     logger = logging.getLogger()
     logger.addHandler(logging.StreamHandler())
     logger.setLevel(logging.INFO)
 
-    datastore = {"x": np.array([7, 2, 7, 9, 6]), "weights": np.array([1, 1, 2, 1, 2])}
-    expected_result = np.sum(
-        np.power(np.log(datastore["x"]), 2) * datastore["weights"]
-    ) / np.sum(datastore["weights"])
-
     log_pow_pipeline = Pipeline(
         modules=[
             LogTransformer(input_key="x", output_key="log_x"),
@@ -92,5 +89,20 @@ def test_popmon_pipeline():
         ],
         logger=logger,
     )
+    return pipeline
+
+
+def test_popmon_pipeline(test_pipeline):
+    datastore = {"x": np.array([7, 2, 7, 9, 6]), "weights": np.array([1, 1, 2, 1, 2])}
+    expected_result = np.sum(
+        np.power(np.log(datastore["x"]), 2) * datastore["weights"]
+    ) / np.sum(datastore["weights"])
 
-    assert pipeline.transform(datastore)["res"] == expected_result
+    assert test_pipeline.transform(datastore)["res"] == expected_result
+
+
+def test_pipeline_repr(test_pipeline):
+    assert (
+        str(test_pipeline)
+        == """Pipeline: [\n\tPipeline: [\n\t\tLogTransformer(input_key='x', output_key='log_x')\n\t\tPowerTransformer(input_key='log_x', output_key='log_pow_x')\n\t]\n\tSumNormalizer(input_key='weights', output_key='norm_weights')\n\tWeightedSum(input_key='log_pow_x', weight_key='norm_weights', output_key='res')\n]"""
+    )