If I extend BaseAlosiAdaptiveEngine, then I get error

[bahadurvaibhav]

Example file updated
class PrototypeAdaptiveEngine(BaseAlosiAdaptiveEngine):
"""
Example demonstrating the subclassing of BaseAdaptiveEngine to implement an adaptive engine
"""
def init(self, W_p, W_r, W_d, W_c):
"""
Accepts weights W_p, W_r, W_d, W_c as input
"""
# placeholder data
self.Scores = np.array([
[1, 1, 0.5],
[1, 2, 0.9],
[2, 1, 1.0],
])
self.Mastery = np.array([
[0.1, 0.2],
[0.3, 0.5],
])
self.MasteryPrior = np.array([0.1, 0.1])
self.Guess = np.array([
[0.1, 0.2],
[0.3, 0.4],
[0.5, 0.6]
])
self.Slip = np.array([
[0.1, 0.2],
[0.3, 0.4],
[0.5, 0.6]
])
self.Transit = np.array([
[0.1, 0.2],
[0.3, 0.4],
[0.5, 0.6]
])
self.r_star = 0.0
self.L_star = 2.2
self.W_p = W_p
self.W_r = W_r
self.W_d = W_d
self.W_c = W_c

def get_guess(self, activity=None):
    if activity is not None:
        return self.Guess[activity]
    else:
        return self.Guess

def get_slip(self, activity=None):
    if activity is not None:
        return self.Slip[activity]
    else:
        return self.Slip

def get_transit(self, activity=None):
    if activity is not None:
        return self.Transit[activity]
    else:
        return self.Transit

def get_difficulty(self):
    return np.array([0.1, 0.5, 0.9])

def get_prereqs(self):
    return np.array([
        [0, 1],
        [0, 0]
    ])

def get_r_star(self):
    return self.r_star

def get_L_star(self):
    return self.L_star

def get_last_attempted_guess(self, learner):
    # placeholder
    return np.array([0.5, 0.3])

def get_last_attempted_slip(self, learner):
    # placeholder
    return np.array([0.5, 0.4])

def get_learner_mastery(self, learner):
    # placeholder
    return np.log([0.5, 0.7])

def get_mastery_prior(self):
    return self.MasteryPrior

def get_W_p(self):
    return self.W_p

def get_W_r(self):
    return self.W_r

def get_W_d(self):
    return self.W_d

def get_W_c(self):
    return self.W_c

def get_scores(self):
    return self.Scores

def save_score(self, learner, activity, score):
    self.Scores = np.vstack((self.Scores, [learner, activity, score]))

def update_learner_mastery(self, learner, new_mastery):
    self.Mastery[learner] = new_mastery

def update_guess(self, new_matrix):
    self.Guess = new_matrix

def update_slip(self, new_matrix):
    self.Slip = new_matrix

def update_transit(self, new_matrix):
    self.Transit = new_matrix

def get_last_attempted_relevance(self, learner):
    print('test')
    return 0

def get_learner_mastery(self, learner):
    print('test')
    return 0

example usage of subclass instance

create a subclass instance

engine = PrototypeAdaptiveEngine(5.0, 3.0, 1.0, 0.5)

use the recommend method

engine.recommend(learner=1)

use the bayesian update method based on a score

engine.update_from_score(learner=0, activity=0, score=0.5)

re-estimate the model parameters

engine.train()

---

Traceback (most recent call last):
File "C:/Apps/enactio_api/alosi/app.py", line 138, in
engine.recommend(learner=1)
File "C:\Users\bahad\AppData\Local\Programs\Python\Python38\lib\site-packages\alosi\engine.py", line 275, in recommend
scores = recommendation_score(**recommendation_params)
TypeError: recommendation_score() got an unexpected keyword argument 'last_attempted_relevance'