def test_pfr_smooth_correlation_3d():
presented = [[[[10, 0, 10], [20, 0, 0], [30, 0, -10], [40, 0, -20]],
[[10, 0, 10], [20, 0, 0], [30, 0, -10], [40, 0, -20]]]]
recalled = [[[[20, 0, 0], [10, 0, 10], [40, 0, -20], [30, 0, -10]],
[[20, 0, 0], [40, 0, -20], [10, 0, 10]]]]
egg = Egg(pres=presented, rec=recalled)
egg.analyze('pfr', match='smooth', distance='correlation',
features='item').data.values
def test_acc():
presented = [[['cat', 'bat', 'hat', 'goat'],
['zoo', 'animal', 'zebra', 'horse']]]
recalled = [[['bat', 'cat', 'goat', 'hat'], ['animal', 'horse', 'zoo']]]
egg = Egg(pres=presented, rec=recalled)
assert np.allclose(
egg.analyze('accuracy').data.values,
[np.array([1.]), np.array([.75])])
def test_analysis_pfr():
presented = [[['cat', 'bat', 'hat', 'goat'],
['zoo', 'animal', 'zebra', 'horse']]]
recalled = [[['bat', 'cat', 'goat', 'hat'], ['animal', 'horse', 'zoo']]]
egg = Egg(pres=presented, rec=recalled)
assert np.array_equal(
egg.analyze('pfr').data.values,
[np.array([0., 1., 0., 0.]),
np.array([0., 1., 0., 0.])])
def test_spc_best_euclidean():
presented = [[[10, 20, 30, 40], [10, 20, 30, 40]]]
recalled = [[[20, 10, 40, 30], [20, 40, 10]]]
egg = Egg(pres=presented, rec=recalled)
assert np.allclose(
egg.analyze('spc', match='best', distance='euclidean',
features='item').data.values,
[np.array([1., 1., 1., 1.]),
np.array([1., 1., 0., 1.])])
def test_pfr_best_euclidean_3d_exception_item_specified():
presented = [[[[10, 0, 0], [20, 0, 0], [30, 0, 0], [40, 0, 0]],
[[10, 0, 0], [20, 0, 0], [30, 0, 0], [40, 0, 0]]]]
recalled = [[[[20, 0, 0], [10, 0, 0], [40, 0, 0], [30, 0, 0]],
[[20, 0, 0], [40, 0, 0], [10, 0, 0]]]]
egg = Egg(pres=presented, rec=recalled)
assert np.array_equal(
egg.analyze('pfr', match='best', distance='euclidean',
features='item').data.values,
[np.array([0., 1., 0., 0.]),
np.array([0., 1., 0., 0.])])
def test_pfr_best_euclidean_3d_exception_no_features():
presented = [[[[10, 0, 0], [20, 0, 0], [30, 0, 0], [40, 0, 0]],
[[10, 0, 0], [20, 0, 0], [30, 0, 0], [40, 0, 0]]]]
recalled = [[[[20, 0, 0], [10, 0, 0], [40, 0, 0], [30, 0, 0]],
[[20, 0, 0], [40, 0, 0], [10, 0, 0]]]]
egg = Egg(pres=presented, rec=recalled)
with pytest.raises(Exception):
assert np.array_equal(
egg.analyze('pfr', match='best', distance='euclidean').data.values,
[np.array([0., 1., 0., 0.]),
np.array([0., 1., 0., 0.])])
def test_analysis_acc_multisubj():
presented = [[['cat', 'bat', 'hat', 'goat'],
['zoo', 'animal', 'zebra', 'horse']],
[['cat', 'bat', 'hat', 'goat'],
['zoo', 'animal', 'zebra', 'horse']]]
recalled = [[['bat', 'cat', 'goat', 'hat'], ['animal', 'horse', 'zoo']],
[['bat', 'cat', 'goat', 'hat'], ['animal', 'horse', 'zoo']]]
multisubj_egg = Egg(pres=presented, rec=recalled)
assert np.array_equal(
multisubj_egg.analyze('accuracy').data.values,
np.array([[1.], [.75], [1.], [.75]]))
def test_lagcrp_exact():
# example from kahana lab lag-crp tutorial
presented = [[['1', '2', '3', '4', '5', '6', '7', '8']]]
recalled = [[['8', '7', '1', '2', '3', '5', '6', '4']]]
egg = Egg(pres=presented, rec=recalled)
assert np.allclose(egg.analyze('lagcrp').data.values,
np.array([[
0.0, 0.0, 0.5, 0.0, 0.0, 0.0, 0.333333, 0.333333,
np.nan, 0.75, 0.333333, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
]]),
equal_nan=True)
def test_acc_best_correlation_3d():
presented = [[[[10, 0, 10], [20, 0, 0], [30, 0, -10], [40, 0, -20]],
[[10, 0, 10], [20, 0, 0], [30, 0, -10], [40, 0, -20]]]]
recalled = [[[[20, 0, 0], [10, 0, 10], [40, 0, -20], [30, 0, -10]],
[[20, 0, 0], [40, 0, -20], [10, 0, 10]]]]
egg = Egg(pres=presented, rec=recalled)
assert np.array_equal(
egg.analyze('accuracy',
match='best',
distance='correlation',
features='item').data.values,
[np.array([1.]), np.array([.75])])
def test_analysis_spc_multisubj():
presented = [[['cat', 'bat', 'hat', 'goat'],
['zoo', 'animal', 'zebra', 'horse']],
[['cat', 'bat', 'hat', 'goat'],
['zoo', 'animal', 'zebra', 'horse']]]
recalled = [[['bat', 'cat', 'goat', 'hat'], ['animal', 'horse', 'zoo']],
[['bat', 'cat', 'goat', 'hat'], ['animal', 'horse', 'zoo']]]
multisubj_egg = Egg(pres=presented, rec=recalled)
assert np.allclose(
multisubj_egg.analyze('spc').data.values,
np.array([[1., 1., 1., 1.], [1., 1., 0., 1.], [1., 1., 1., 1.],
[1., 1., 0., 1.]]))
def test_lagcrp_best_euclidean():
presented = [[[10, 20, 30, 40, 50, 60, 70, 80]]]
recalled = [[[81, 71, 11, 21, 31, 51, 61, 41]]]
egg = Egg(pres=presented, rec=recalled)
assert np.allclose(egg.analyze('lagcrp',
match='best',
distance='euclidean',
features='item').data.values,
np.array([[
0.0, 0.0, 0.5, 0.0, 0.0, 0.0, 0.333333, 0.333333,
np.nan, 0.75, 0.333333, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
]]),
equal_nan=True)
def test_lagcrp_exact_3d():
presented = [[[[10, 0, 0], [20, 0, 0], [30, 0, 0], [40, 0, 0], [50, 0, 0],
[60, 0, 0], [70, 0, 0], [80, 0, 0]]]]
recalled = [[[[80, 0, 0], [70, 0, 0], [10, 0, 0], [20, 0, 0], [30, 0, 0],
[50, 0, 0], [60, 0, 0], [40, 0, 0]]]]
egg = Egg(pres=presented, rec=recalled)
assert np.allclose(egg.analyze('lagcrp', match='best',
features='item').data.values,
np.array([[
0.0, 0.0, 0.5, 0.0, 0.0, 0.0, 0.333333, 0.333333,
np.nan, 0.75, 0.333333, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
]]),
equal_nan=True)
def test_acc_best_euclidean_3d_features_not_set():
presented = [[[{
'item': i,
'feature1': [i * 10, 0, 0]
} for i in range(1, 5)] for i in range(2)]]
recalled = [[[{
'item': i,
'feature1': [i * 10, 0, 0]
} for i in [2, 1, 4, 3]],
[{
'item': i,
'feature1': [i * 10, 0, 0]
} for i in [2, 4, 1]]]]
egg = Egg(pres=presented, rec=recalled)
assert np.array_equal(
egg.analyze('pfr', match='best', distance='euclidean').data.values,
[np.array([0., 1., 0., 0.]),
np.array([0., 1., 0., 0.])])
def test_acc_best_euclidean():
presented = [[[{
'item': i,
'feature1': i * 10
} for i in range(1, 5)] for i in range(2)]]
recalled = [[[{
'item': i,
'feature1': i * 10
} for i in [2, 1, 4, 3]],
[{
'item': i,
'feature1': i * 10
} for i in [2, 4, 1]]]]
egg = Egg(pres=presented, rec=recalled)
assert np.array_equal(
egg.analyze('accuracy',
match='best',
distance='euclidean',
features=['feature1']).data.values,
[np.array([1.]), np.array([.75])])
def test_spc_best_euclidean():
presented = [[[{
'item': i,
'feature1': i * 10
} for i in range(1, 5)] for i in range(2)]]
recalled = [[[{
'item': i,
'feature1': i * 10
} for i in [2, 1, 4, 3]],
[{
'item': i,
'feature1': i * 10
} for i in [2, 4, 1]]]]
egg = Egg(pres=presented, rec=recalled)
assert np.allclose(
egg.analyze('spc',
match='best',
distance='euclidean',
features='feature1').data.values,
[np.array([1., 1., 1., 1.]),
np.array([1., 1., 0., 1.])])
def test_spc_best_euclidean_3d_2features():
presented = [[[{
'item': i,
'feature1': [i * 10, 0, 0],
'feature2': [i * 10, 0, 0]
} for i in range(1, 5)] for i in range(2)]]
recalled = [[[{
'item': i,
'feature1': [i * 10, 0, 0],
'feature2': [i * 10, 0, 0]
} for i in [2, 1, 4, 3]],
[{
'item': i,
'feature1': [i * 10, 0, 0],
'feature2': [i * 10, 0, 0]
} for i in [2, 4, 1]]]]
egg = Egg(pres=presented, rec=recalled)
assert np.array_equal(
egg.analyze('spc',
match='best',
distance='euclidean',
features=['feature1', 'feature2']).data.values,
[np.array([1., 1., 1., 1.]),
np.array([1., 1., 0., 1.])])