def test_r_square_random_arrays_finite_values(self, y_true, y_pred):
r_square_value = r_square(y_true, y_pred)
expected_r_square = self._correct_r_squared(y_true, y_pred)
print(y_true)
print(y_pred)
assert expected_r_square == r_square_value
def test_r_square_dataframe(self):
y_true = pd.DataFrame([0, 1, 2, 3, 4, 5])
y_pred = pd.DataFrame([-1, 4, 5, 10, 4, 1])
r_square_value = np.round(r_square(y_true, y_pred), decimals=2)
expected_r_square = -3.8
assert expected_r_square == r_square_value
def test_r_square_array(self):
y_true = np.array([0, 1, 2, 3, 4, 5])
y_pred = np.array([-1, 4, 5, 10, 4, 1])
r_square_value = np.round(r_square(y_true, y_pred), decimals=2)
expected_r_square = -3.8
assert expected_r_square == r_square_value
def test_r_square_list(self):
y_true = [0, 1, 2, 3, 4, 5]
y_pred = [-1, 4, 5, 10, 4, 1]
r_square_value = np.round(r_square(y_true, y_pred), decimals=2)
expected_r_square = -3.8
assert expected_r_square == r_square_value
def test_r_square_ss_tot_zero(self):
# The test checks for 'if' condition
# to check if ss_res is certain value and ss_tot are all Zeros
y_true = np.array([0, 0, 0, 0, 0, 0])
y_pred = np.array([1, 1, 1, 1, 1, 1])
r_square_value = np.round(r_square(y_true, y_pred), decimals=2)
expected_r_square = 0.0
assert expected_r_square == r_square_value
def test_r_square_all_zero_values(self):
# The test checks for all zero inputs covering the 'if' condition
# to check if both ss_res and ss_tot are all Zeros
y_true = np.array([0, 0, 0, 0, 0, 0])
y_pred = np.array([0, 0, 0, 0, 0, 0])
r_square_value = np.round(r_square(y_true, y_pred), decimals=2)
expected_r_square = 1.0
assert expected_r_square == r_square_value
def test_r_square_ss_res_ss_tot_infinity(self):
# The test checks for 'if' condition
# to check if both ss_res and ss_tot are Infinite
y_true = [
0.00000000e000,
0.00000000e000,
9.81351055e200,
9.81351055e200,
9.81351055e200,
9.81351055e200,
9.81351055e200,
9.81351055e200,
9.81351055e200,
9.81351055e200,
9.81351055e200,
9.81351055e200,
9.81351055e200,
9.81351055e200,
9.81351055e200,
9.81351055e200,
9.81351055e200,
9.81351055e200,
9.81351055e200,
9.81351055e200,
9.81351055e200,
9.81351055e200,
9.81351055e200,
9.81351055e200,
9.81351055e200,
9.81351055e200,
9.81351055e200,
9.81351055e200,
9.81351055e200,
9.81351055e200,
]
y_pred = [
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
]
r_square_value = np.round(r_square(y_true, y_pred), decimals=2)
expected_r_square = np.NINF
assert expected_r_square == r_square_value
def test_infinite_values(self):
y_true = np.random.random(4)
y_pred = [0, np.inf, 2, 3]
with pytest.raises(ValueError):
r_square(y_true, y_pred)
def test_nan_values(self):
y_true = [np.nan, 1, 2, 3]
y_pred = np.random.random(4)
with pytest.raises(ValueError):
r_square(y_true, y_pred)
def test_wrong_vector_length(self):
y_true = np.random.random(5)
y_pred = np.random.random(4)
with pytest.raises(ValueError):
r_square(y_true, y_pred)