def test_find_correlation_threshold_works():
x = range(1000)
noise = np.random.randn(1000)
y = [a + b for a, b in zip(x, noise)]
z = np.random.randn(1000)
df = pd.DataFrame(list(zip(x, y, z)), columns=["x", "y", "z"])
out = feature_selection.find_correlation(df, threshold=1.0)
assert len(out) == 0
def test_find_correlation_threshold_works():
x = range(1000)
noise = np.random.randn(1000)
y = [a + b for a, b in zip(x, noise)]
z = np.random.randn(1000)
df = pd.DataFrame(list(zip(x, y, z)), columns=["x", "y", "z"])
out = feature_selection.find_correlation(df, threshold=1.0)
assert len(out) == 0
def test_find_correlation_large_n():
x = range(100000)
noise = np.random.randn(100000)
y = [a + b for a, b in zip(x, noise)]
z = np.random.randn(100000)
df = pd.DataFrame(list(zip(x, y, z)), columns=["x", "y", "z"])
out = feature_selection.find_correlation(df)
assert len(out) == 1
assert out[0] == ["x"] or ["y"]
assert out[0] != ["z"]
def test_find_correlation_multiple_correlated():
x = range(1000)
noise = np.random.randn(1000)
y = [a + b for a, b in zip(x, noise)]
xx = [a + b for a, b in zip(x, noise)]
z = np.random.randn(1000)
df = pd.DataFrame(list(zip(x, xx, y, z)), columns=["x", "xx", "y", "z"])
out = feature_selection.find_correlation(df)
assert len(out) == 2
assert "z" not in out
def test_find_correlation_large_n():
x = range(100000)
noise = np.random.randn(100000)
y = [a + b for a, b in zip(x, noise)]
z = np.random.randn(100000)
df = pd.DataFrame(list(zip(x, y, z)), columns=["x", "y", "z"])
out = feature_selection.find_correlation(df)
assert len(out) == 1
assert out[0] == ["x"] or ["y"]
assert out[0] != ["z"]
def test_find_correlation_multiple_correlated():
x = range(1000)
noise = np.random.randn(1000)
y = [a + b for a, b in zip(x, noise)]
xx = [a + b for a, b in zip(x, noise)]
z = np.random.randn(1000)
df = pd.DataFrame(list(zip(x, xx, y, z)), columns=["x", "xx", "y", "z"])
out = feature_selection.find_correlation(df)
assert len(out) == 2
assert "z" not in out