Hypothesis

Hypothesis ist eine Bibliothek, mit der ihr Tests schreiben könnt, die aus einer Quelle von Beispielen :term:` parametrisiert <Parameter> werden. Anschließend werden einfache und verständliche Beispiele generiert, die dazu verwendet werden können, eure Tests fehlschlagen zu lassen und Fehler mit wenig Aufwand zu finden.

1. Installiert Hypothesis:

   Linux/macOS

   $ python -m pip install hypothesis
   

   Windows

   C:> python -m pip install hypothesis
   

   Alternativ kann Hypothesis auch mit Erweiterungen installiert werden, z.B.:

   Linux/macOS

   $ python -m pip install hypothesis[numpy,pandas]
   

   Windows

   C:> python -m pip install hypothesis[numpy,pandas]
   

2. Schreibt einen Test:

   1. Importe:

      import pytest
      from hypothesis import given
      from hypothesis.strategies import floats, lists
      

   2. Testen:

      @given(lists(floats(allow_nan=False, allow_infinity=False), min_size=1))
      def test_mean(ls):
          mean = sum(ls) / len(ls)
          assert min(ls) <= mean <= max(ls)
      

3. Test durchführen:

   Linux/macOS

   $ python -m pytest test_hypothesis.py
   ============================= test session starts ==============================
   platform darwin -- Python 3.13.0, pytest-8.3.3, pluggy-1.5.0
   rootdir: /Users/veit/cusy/trn/python-basics/docs/test
   plugins: hypothesis-6.114.1
   collected 1 item
   
   test_hypothesis.py F                                                     [100%]
   
   =================================== FAILURES ===================================
   __________________________________ test_mean ___________________________________
   
       @given(lists(floats(allow_nan=False, allow_infinity=False), min_size=1))
   >   def test_mean(ls):
   
   test_hypothesis.py:6:
   _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
   
   ls = [9.9792015476736e+291, 1.7976931348623157e+308]
   
       @given(lists(floats(allow_nan=False, allow_infinity=False), min_size=1))
       def test_mean(ls):
           mean = sum(ls) / len(ls)
   >       assert min(ls) <= mean <= max(ls)
   E       assert inf <= 1.7976931348623157e+308
   E        +  where 1.7976931348623157e+308 = max([9.9792015476736e+291, 1.7976931348623157e+308])
   
   test_hypothesis.py:8: AssertionError
   ---------------------------------- Hypothesis ----------------------------------
   Falsifying example: test_mean(
       ls=[9.9792015476736e+291, 1.7976931348623157e+308],
   )
   =========================== short test summary info ============================
   FAILED test_hypothesis.py::test_mean - assert inf <= 1.7976931348623157e+308
   ============================== 1 failed in 0.44s ===============================
   

   Windows

   C:> python -m pytest test_hypothesis.py
   ============================= test session starts ==============================
   platform win32 -- Python 3.13.0, pytest-8.3.3, pluggy-1.5.0
   rootdir: C:\Users\veit\python-basics\docs\test
   plugins: plugins: hypothesis-6.114.1
   collected 1 item
   
   test_hypothesis.py F                                                     [100%]
   
   =================================== FAILURES ===================================
   __________________________________ test_mean ___________________________________
   
       @given(lists(floats(allow_nan=False, allow_infinity=False), min_size=1))
   >   def test_mean(ls):
   
   test_hypothesis.py:6:
   _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
   
   ls = [9.9792015476736e+291, 1.7976931348623157e+308]
   
       @given(lists(floats(allow_nan=False, allow_infinity=False), min_size=1))
       def test_mean(ls):
           mean = sum(ls) / len(ls)
   >       assert min(ls) <= mean <= max(ls)
   E       assert inf <= 1.7976931348623157e+308
   E        +  where 1.7976931348623157e+308 = max([9.9792015476736e+291, 1.7976931348623157e+308])
   
   test_hypothesis.py:8: AssertionError
   ---------------------------------- Hypothesis ----------------------------------
   Falsifying example: test_mean(
       ls=[9.9792015476736e+291, 1.7976931348623157e+308],
   )
   =========================== short test summary info ============================
   FAILED test_hypothesis.py::test_mean - assert inf <= 1.7976931348623157e+308
   ============================== 1 failed in 0.44s ===============================
   

Siehe auch

Hypothesis for the Scientific Stack