def _update_abilities(self, history, use_mean=True, num_steps=200,
ignore_analytics=False):
"""Take the history and update the ability estimate."""
# TODO(jace) - check to see if history has actually changed
# to avoid needless re-estimation
# If ignore_analytics is true, only learn from non-analytics cards
# This is to evaluate the quality of various models for predicting
# the analytics card.
if history and ignore_analytics:
history = [
h for h in history if h['metadata'] and
not h['metadata'].get('analytics')]
ex = lambda h: engine.ItemResponse(h).exercise
exercises = np.asarray([ex(h) for h in history])
exercises_ind = mirt_util.get_exercises_ind(
exercises, self.exercise_ind_dict)
is_correct = lambda h: engine.ItemResponse(h).correct
correct = np.asarray([is_correct(h) for h in history]).astype(int)
time_taken = lambda h: engine.ItemResponse(h).time_taken
time_taken = np.asarray([time_taken(h) for h in history]).astype(float)
# deal with out of range or bad values for the response time
time_taken[~np.isfinite(time_taken)] = 1.
time_taken[time_taken < 1.] = 1.
time_taken[time_taken > self.max_time_taken] = self.max_time_taken
log_time_taken = np.log(time_taken)
sample_abilities, _, mean_abilities, stdev = (
mirt_util.sample_abilities_diffusion(
self.theta, exercises_ind, correct, log_time_taken,
self.abilities, num_steps=num_steps))
self.abilities = mean_abilities # if use_mean else sample_abilities
self.abilities_stdev = stdev
def _update_abilities(self,
history,
use_mean=True,
num_steps=200,
ignore_analytics=False):
"""Take the history and update the ability estimate."""
# TODO(jace) - check to see if history has actually changed
# to avoid needless re-estimation
# If ignore_analytics is true, only learn from non-analytics cards
# This is to evaluate the quality of various models for predicting
# the analytics card.
if history and ignore_analytics:
history = [
h for h in history
if h['metadata'] and not h['metadata'].get('analytics')
]
ex = lambda h: engine.ItemResponse(h).exercise
exercises = np.asarray([ex(h) for h in history])
exercises_ind = mirt_util.get_exercises_ind(exercises,
self.exercise_ind_dict)
is_correct = lambda h: engine.ItemResponse(h).correct
correct = np.asarray([is_correct(h) for h in history]).astype(int)
time_taken = lambda h: engine.ItemResponse(h).time_taken
time_taken = np.asarray([time_taken(h) for h in history]).astype(float)
# deal with out of range or bad values for the response time
time_taken[~np.isfinite(time_taken)] = 1.
time_taken[time_taken < 1.] = 1.
time_taken[time_taken > self.max_time_taken] = self.max_time_taken
log_time_taken = np.log(time_taken)
sample_abilities, _, mean_abilities, stdev = (
mirt_util.sample_abilities_diffusion(self.theta,
exercises_ind,
correct,
log_time_taken,
self.abilities,
num_steps=num_steps))
self.abilities = mean_abilities # if use_mean else sample_abilities
self.abilities_stdev = stdev
def _update_abilities(self,
history,
use_mean=True,
num_steps=200,
ignore_analytics=False):
"""Take the history and update the ability estimate."""
# TODO(jace) - check to see if history has actually changed
# to avoid needless re-estimation
# If ignore_analytics is true, only learn from non-analytics cards
# This is to evaluate the quality of various models for predicting
# the analytics card.
state = mirt_util.UserState()
if history and ignore_analytics:
history = [
h for h in history
if h['metadata'] and not h['metadata'].get('analytics')
]
ex = lambda h: engine.ItemResponse(h).exercise
exercises = np.asarray([ex(h) for h in history])
state.exercise_ind = mirt_util.get_exercise_ind(
exercises, self.exercise_ind_dict)
is_correct = lambda h: engine.ItemResponse(h).correct
state.correct = np.asarray([is_correct(h)
for h in history]).astype(int)
time_taken = lambda h: engine.ItemResponse(h).time_taken
time_taken = np.asarray([time_taken(h) for h in history]).astype(float)
state.log_time_taken = mirt_util.get_normalized_time(time_taken)
state.abilities = self.abilities
sample_abilities, _, mean_abilities, stdev = (
mirt_util.sample_abilities_diffusion(self.theta,
state,
num_steps=num_steps))
self.abilities = mean_abilities # if use_mean else sample_abilities
self.abilities_stdev = stdev
def _update_abilities(self, history, use_mean=True, num_steps=200,
ignore_analytics=False):
"""Take the history and update the ability estimate."""
# TODO(jace) - check to see if history has actually changed
# to avoid needless re-estimation
# If ignore_analytics is true, only learn from non-analytics cards
# This is to evaluate the quality of various models for predicting
# the analytics card.
state = mirt_util.UserState()
if history and ignore_analytics:
history = [
h for h in history if h['metadata'] and
not h['metadata'].get('analytics')]
ex = lambda h: engine.ItemResponse(h).exercise
exercises = np.asarray([ex(h) for h in history])
state.exercise_ind = mirt_util.get_exercise_ind(
exercises, self.exercise_ind_dict)
is_correct = lambda h: engine.ItemResponse(h).correct
state.correct = np.asarray(
[is_correct(h) for h in history]).astype(int)
time_taken = lambda h: engine.ItemResponse(h).time_taken
time_taken = np.asarray([time_taken(h) for h in history]).astype(float)
state.log_time_taken = mirt_util.get_normalized_time(time_taken)
state.abilities = self.abilities
sample_abilities, _, mean_abilities, stdev = (
mirt_util.sample_abilities_diffusion(
self.theta, state, num_steps=num_steps))
self.abilities = mean_abilities # if use_mean else sample_abilities
self.abilities_stdev = stdev
