def test_su_default(self, idadf):
     if len(idadf.columns) > 1:
         result = su(idadf, features = idadf.columns)
         assert(isinstance(result, pandas.core.frame.DataFrame))
         assert(len(result.columns) == len(idadf.columns))
         assert(len(result.index) == len(idadf.columns))
         result2 = su(idadf)
         assert(all(result == result2))
         result = result.fillna(0) # np.nan values are not equal when compared
         assert(all(result == result.T)) # symmetry
 def test_su_one_target_one_feature(self, idadf):
     if len(idadf.columns) > 1:
         result = su(idadf,
                     target=idadf.columns[0],
                     features=[idadf.columns[1]])
         assert (isinstance(result, float))
         result2 = su(idadf,
                      target=idadf.columns[1],
                      features=[idadf.columns[0]])
         assert (round(result, 3) == round(result2, 3))  # symmetry
 def test_su_valueError(self, idadf):
     if len(idadf.columns) > 0:
         with pytest.raises(
                 ValueError
         ):  # Cannot compute correlation coefficients of only one column (...), need at least 2
             su(idadf, features=idadf.columns[0])
         with pytest.raises(
                 ValueError
         ):  # The correlation value of two same columns is always maximal
             su(idadf, target=idadf.columns[0], features=idadf.columns[0])
 def test_su_default(self, idadf):
     if len(idadf.columns) > 1:
         result = su(idadf, features=idadf.columns)
         assert (isinstance(result, pandas.core.frame.DataFrame))
         assert (len(result.columns) == len(idadf.columns))
         assert (len(result.index) == len(idadf.columns))
         result2 = su(idadf)
         assert (all(result == result2))
         result = result.fillna(
             0)  # np.nan values are not equal when compared
         assert (all(result == result.T))  # symmetry
 def test_su_one_target_one_feature(self, idadf):
     if len(idadf.columns) > 1:
         result = su(idadf, target = idadf.columns[0], features=[idadf.columns[1]])
         assert(isinstance(result, float))
         result2 = su(idadf, target = idadf.columns[1], features=[idadf.columns[0]])
         assert(round(result,3) == round(result2,3)) # symmetry
 def test_su_multiple_target(self, idadf):
     if len(idadf.columns) > 1:
         result = su(idadf, target = [idadf.columns[0],idadf.columns[1]])
         assert(isinstance(result, pandas.core.frame.DataFrame))
         assert(len(result.columns) == 2)
         assert(len(result.index) == len(idadf.columns))
 def test_su_one_target(self, idadf):
     if len(idadf.columns) > 1:
         result = su(idadf, target = idadf.columns[0])
         assert(isinstance(result, pandas.core.series.Series))
         assert(len(result) == (len(idadf.columns)-1))
 def test_su_valueError(self, idadf):
     if len(idadf.columns) > 0: 
         with pytest.raises(ValueError): # Cannot compute correlation coefficients of only one column (...), need at least 2
             su(idadf, features = idadf.columns[0])
         with pytest.raises(ValueError): # The correlation value of two same columns is always maximal
             su(idadf, target= idadf.columns[0], features = idadf.columns[0])
 def test_su_multiple_target(self, idadf):
     if len(idadf.columns) > 1:
         result = su(idadf, target=[idadf.columns[0], idadf.columns[1]])
         assert (isinstance(result, pandas.core.frame.DataFrame))
         assert (len(result.columns) == 2)
         assert (len(result.index) == len(idadf.columns))
 def test_su_one_target(self, idadf):
     if len(idadf.columns) > 1:
         result = su(idadf, target=idadf.columns[0])
         assert (isinstance(result, pandas.core.series.Series))
         assert (len(result) == (len(idadf.columns) - 1))