def test_pfa_coef_counts(self):
coef = pd.DataFrame(
columns=["factor", "intercept", "correct_coef", "incorrect_coef"],
data=[
("a", 0.11, 1.0, -1.0),
("b", 0.22, 2.0, -2.0),
])
actual = pfa_coef_counts(coef)
expected = array(
[[1.0, 1.0, 2.0], [0.0, -1.0, -2.0], [1.0, 0.11, 0.22]], )
assert_equal(actual, expected)
def test_pfa_prediction_m__works_with_all_skills_active(self):
coef_pd = pd.DataFrame(
columns=["factor", "intercept", "correct_coef", "incorrect_coef"],
data=[
("a", 9., 1., -1.),
("b", 7., 2., -2.),
])
coef = pfa_coef_counts(coef_pd)
data = array([[1., 12., 22.], [0., 11., 21.], [1., 1., 1.]], )
actual = pfa_prediction_m(data, coef)
expected = 9. + (12. * 1.) + (11. * -1.) + 7. + (22. * 2.) + (21. *
-2.)
assert_equal(actual, expected)
def test_pfa_dashboard__using_diff(self):
coef = pfa_coef_counts(
pd.DataFrame(columns=[
"factor", "intercept", "correct_coef", "incorrect_coef"
],
data=[
("diff1", 6., 1.1, -1.1),
("diff2", 7., 1.1, -1.1),
("a", 8., 2., -2.),
("b", 9., 3., -3.),
("c", 10., 4., -4.),
]))
data = array([[1., 12., 22., 32., 42., 52.],
[0., 11., 21., 31., 41., 51.], [1., 1., 1., 1., 1., 1.]],
) # This row shouldn't matter
actual = pfa_dashboard(data_counts=data,
coef_counts=coef,
num_diffs=2,
num_skills=3,
diff_ind=1)
expected = array([
pred(data,
coef,
num_diffs=2,
num_skills=3,
diff_ind=1,
skill_ind=0),
pred(data,
coef,
num_diffs=2,
num_skills=3,
diff_ind=1,
skill_ind=1),
pred(data,
coef,
num_diffs=2,
num_skills=3,
diff_ind=1,
skill_ind=2)
])
assert_equal(actual, expected)
np.set_printoptions(linewidth=200,
threshold=(full_history_length + 1) *
model_history_length *
feature_num) # unset with np.set_printoptions()
# output location
# run_dir_load = os.path.join('runs', f'run_embedded_l1-{pred_model_layer_1}_l2-{pred_model_layer_2}_e{pred_epochs}')
run_dir = os.path.join('dashboards', f'pfa_dashboard', 'pick1')
if not os.path.exists(run_dir):
os.makedirs(run_dir)
stdout_add_file(os.path.join(run_dir, 'log.txt'))
coef = pfa_coef_counts(pfa_coef())
# ================= VALIDATE ===============
print(f"loading validate data")
answer_counts_validate = read_numpy_3d_array_from_txt(
os.path.join('outputs', 'pick1',
f'answer_counts_validate_l{full_history_length}_s1.txt'))
# ========= Dashboard and current question
print(f"computing dashboard")
answer_counts_validate_dashboard = [
np.concatenate((pfa_dashboard(ac,
coef,
num_diffs=0,
num_skills=diff_and_skill_num,
diff_ind=-1), np.array(ac[2][1:])))
for ac in answer_counts_validate