No theory in Aleph SWI Prolog

Question

My question is pretty similar to Not getting a theory in Aleph for SWI Prolog

After trying to induce a theory, I end up getting only single atoms

My code(only a snippet of examples included for readability):

% Empty with aleph loaded
:- use_module(library(aleph)).

:- aleph.
:- if(current_predicate(use_rendering/1)).
:- use_rendering(prolog).
:- endif.

% Settings

% Language Bias and Definitions
:- modeh(1,like(+user_id,+restaurant_id)).

:- modeb(1,smoker(+user_id,-smoker)).
:- modeb(1,drink_level(+user_id,-drink_level)).
:- modeb(1,ambience(+user_id,-ambience)).
:- modeb(1,marital_status(+user_id,-marital_status)).
:- modeb(*,lteq(+age,#age)).
:- modeb(1,age(+user_id,-age)).
:- modeb(1,transport(+user_id,-transport)).
:- modeb(1,personality(+user_id,-personality)).
:- modeb(1,religion(+user_id,-religion)).
:- modeb(1,budget(+user_id,-budget)).
:- modeb(*,lteq(+distance,#distance)).
:- modeb(1,distance(+user_id, +restaurant_id,-distance)).
:- modeb(1,alcohol(+restaurant_id,-alcohol)).
:- modeb(1,smoking_area(+restaurant_id,-smoking_area)).
:- modeb(1,other_services(+restaurant_id,-other_services)).
:- modeb(1,price(+restaurant_id,-price)).
:- modeb(1,dress_code(+restaurant_id,-dress_code)).
:- modeb(1,accessibility(+restaurant_id,-accessibility)).
:- modeb(1,area(+restaurant_id,-area)).
:- modeb(1,rambience(+restaurant_id,-rambience)).
:- modeb(1,franchise(+restaurant_id,-franchise)).
:- modeb(1,parking_lot(+restaurant_id,-parking_lot)).

:- determination(like/2,smoker/2).
:- determination(like/2,drink_level/2).
:- determination(like/2,ambience/2).
:- determination(like/2,marital_status/2).
:- determination(like/2,age/2).
:- determination(like/2,transport/2).
:- determination(like/2,personality/2).
:- determination(like/2,religion/2).
:- determination(like/2,budget/2).
:- determination(like/2,distance/3).
:- determination(like/2,alcohol/2).
:- determination(like/2,smoking_area/2).
:- determination(like/2,other_services/2).
:- determination(like/2,price/2).
:- determination(like/2,dress_code/2).
:- determination(like/2,accessibility/2).
:- determination(like/2,area/2).
:- determination(like/2,rambience/2).
:- determination(like/2,franchise/2).
:- determination(like/2,parking_lot/2).
:- determination(like/2,lteq/2).

:-begin_bg.

% Background knowledge here

smoker(user_id1, no).
smoker(user_id10, no).
smoker(user_id100, yes).

drink_level(user_id1, abstemious).
drink_level(user_id10, social_drinker).
drink_level(user_id100, casual_drinker).

ambience(user_id1, family).
ambience(user_id10, family).
ambience(user_id100, friends).

age(user_id1, 33).
age(user_id10, 31).
age(user_id100, 37).

transport(user_id1, on_foot).
transport(user_id10, public).
transport(user_id100, car_owner).

marital_status(user_id1, single).
marital_status(user_id10, single).
marital_status(user_id100, single).

personality(user_id1, thrifty-protector).
personality(user_id10, hard-worker).
personality(user_id100, hunter-ostentatious).

religion(user_id1, none).
religion(user_id10, christian).
religion(user_id100, none).

budget(user_id1, medium).
budget(user_id10, medium).
budget(user_id100, medium).

distance(user_id70, restaurant_id37, 1.5).
distance(user_id5, restaurant_id75, 0.3).
distance(user_id87, restaurant_id62, 2.0).

alcohol(restaurant_id1, no_alcohol_served).
alcohol(restaurant_id10, wine_beer).
alcohol(restaurant_id100, no_alcohol_served).

smoking_area(restaurant_id1, none).
smoking_area(restaurant_id10, section).
smoking_area(restaurant_id100, permitted).

other_services(restaurant_id1, none).
other_services(restaurant_id10, none).
other_services(restaurant_id100, none).

price(restaurant_id1, low).
price(restaurant_id10, low).
price(restaurant_id100, medium).

dress_code(restaurant_id1, informal).
dress_code(restaurant_id10, informal).
dress_code(restaurant_id100, informal).

area(restaurant_id1, closed).
area(restaurant_id10, open).
area(restaurant_id100, closed).

rambience(restaurant_id1, familiar).
rambience(restaurant_id10, quiet).
rambience(restaurant_id100, familiar).

franchise(restaurant_id1, f).
franchise(restaurant_id10, f).
franchise(restaurant_id100, t).

parking_lot(restaurant_id1, none).
parking_lot(restaurant_id10, none).
parking_lot(restaurant_id100, yes).

lteq(X,Y):-
var(Y), !,
X = Y.
lteq(X,Y):-
number(X), number(Y),
X =< Y.
:- end_bg.

:-begin_in_pos.

% Positive individuals here

like(user_id3, restaurant_id1).
like(user_id5, restaurant_id1).
like(user_id6, restaurant_id1).

:-end_in_pos.

:-begin_in_neg.

% Negative individuals here
like(user_id1, restaurant_id1).
like(user_id2, restaurant_id1).
like(user_id4, restaurant_id1).

:-end_in_neg.

:-aleph_read_all.

The output:

[theory]

[Rule 1] [Pos cover = 1 Neg cover = 0]
like(user_id3,restaurant_id1).

[Rule 2] [Pos cover = 1 Neg cover = 0]
like(user_id5,restaurant_id1).

[Rule 3] [Pos cover = 1 Neg cover = 0]
like(user_id6,restaurant_id1).

...

[Training set performance]
            Actual
         +            -   
     +  447           0           447  
Pred 
     -   0           613          613  

        447          613         1060 

Accuracy = 1
[Training set summary] [[447,0,0,613]]
[time taken] [140.047400515]
[total clauses constructed] [87399]