def test_stats():
filename = os.path.join(get_test_data_path(), "OpenFace_Test.csv")
openface = Fex(filename=filename, sampling_freq=30, detector="OpenFace")
openface = openface.read_file()
aus = openface.aus()
aus.sessions = range(len(aus))
y = aus[[i for i in aus.columns if "_r" in i]]
X = pd.DataFrame(aus.sessions)
b, t, p, df, res = aus.regress(X, y, mode="ols", fit_intercept=True)
assert b.shape == (2, 17)
assert res.mean().mean() < 1
clf = openface.predict(X=["AU02_c"], y="AU04_c")
assert clf.coef_ < 0
clf = openface.predict(X=openface[["AU02_c"]], y=openface["AU04_c"])
assert clf.coef_ < 0
t, p = openface[["AU02_c"]].ttest_1samp()
assert t > 0
a = openface.aus().assign(input="0")
b = openface.aus().apply(lambda x: x + np.random.rand(100)).assign(
input="1")
doubled = pd.concat([a, b])
doubled.sessions = doubled['input']
t, p = doubled.ttest_ind(col="AU12_r", sessions=("0", "1"))
assert (t < 0)
frame = np.concatenate([
np.array(range(int(len(doubled) / 2))),
np.array(range(int(len(doubled) / 2)))
])
assert (doubled.assign(frame=frame).isc(col="AU04_r").iloc[0, 0] == 1)
def test_stats():
filename = os.path.join(get_test_data_path(), "OpenFace_Test.csv")
openface = Fex(filename=filename, sampling_freq=30, detector="OpenFace")
openface = openface.read_file()
aus = openface.aus()
aus.sessions = range(len(aus))
y = aus[[i for i in aus.columns if "_r" in i]]
X = pd.DataFrame(aus.sessions)
b, t, p, df, res = aus.regress(X, y, mode="ols", fit_intercept=True)
assert b.shape == (2, 17)
assert res.mean().mean() < 1
clf = openface.predict(X=["AU02_c"], y="AU04_c")
assert clf.coef_ < 0
clf = openface.predict(X=openface[["AU02_c"]], y=openface["AU04_c"])
assert clf.coef_ < 0
t, p = openface[["AU02_c"]].ttest()
assert t > 0