def _add_alternation_model(model_obj, code, root_node, first=False):
"""
Adds this alternation model to our current alternation tree. This
involves walking to each leaf node, then getting all candidates of
the model, and appending them as new nodes.
@param model_obj: An alternation model.
@type model_obj: AlternationModelI
@param code: A character code for the given alternation.
@type code: char
@param root_node: The root node of the entire tree.
@type root_node: TreeNode
"""
for kanji_node in consoleLog.withProgress(root_node.children.values()):
kanji = kanji_node.label
leaves = list(kanji_node.walk_leaves())
for reading_node in leaves:
reading = reading_node.label
candidates = model_obj.candidates(kanji, reading)
if not first and candidates == [(reading, 0.0)]:
# No changes
continue
for alt_reading, log_prob in candidates:
# Only tag changes with their alternation code.
if alt_reading == reading:
node_code = ''
else:
node_code = code
assert alt_reading not in reading_node.children
reading_node.add_child(
AltTreeNode(alt_reading, node_code, log_prob))
return
def _dump_responses(filename):
_log.log(filename + ' ', newLine=False)
with open(filename, 'w') as ostream:
users = User.objects.exclude(email__in=EXCLUDE_EMAILS)
for user in withProgress(users):
for response in drill_models.MultipleChoiceResponse.objects.filter(
user=user):
question = response.question
test_set = drill_models.TestSet.objects.get(questions=question)
record = {
'user_id': user.id,
'test_id': test_set.id,
'timestamp': response.timestamp.ctime(),
'pivot': question.pivot,
'pivot_type': question.pivot_type,
'question_type': question.question_type,
'is_adaptive': question.question_plugin.is_adaptive,
'distractors': [
o.value for o in \
question.multiplechoicequestion.options.all()
],
'correct_response': question.multiplechoicequestion.options.get(is_correct=True).value,
'user_response': response.option.value,
}
print >> ostream, simplejson.dumps(record)
def _add_alternation_model(model_obj, code, root_node, first=False):
"""
Adds this alternation model to our current alternation tree. This
involves walking to each leaf node, then getting all candidates of
the model, and appending them as new nodes.
@param model_obj: An alternation model.
@type model_obj: AlternationModelI
@param code: A character code for the given alternation.
@type code: char
@param root_node: The root node of the entire tree.
@type root_node: TreeNode
"""
for kanji_node in consoleLog.withProgress(root_node.children.values()):
kanji = kanji_node.label
leaves = list(kanji_node.walk_leaves())
for reading_node in leaves:
reading = reading_node.label
candidates = model_obj.candidates(kanji, reading)
if not first and candidates == [(reading, 0.0)]:
# No changes
continue
for alt_reading, log_prob in candidates:
# Only tag changes with their alternation code.
if alt_reading == reading:
node_code = ''
else:
node_code = code
assert alt_reading not in reading_node.children
reading_node.add_child(
AltTreeNode(alt_reading, node_code, log_prob))
return
def test_sample(m, n, method, iterations=1000):
dist = FreqDist()
for i in withProgress(xrange(iterations)):
for s in method(m, n):
dist.inc(s)
min_prob, min_v = min((dist.prob(k), k) for k in dist.iterkeys())
max_prob, max_v = max((dist.prob(k), k) for k in dist.iterkeys())
print "Min:", min_prob, min_v
print "Max:", max_prob, max_v
print "Diff:", abs(max_prob - min_prob)
def _build_graph(self, kanji_set):
metric = metrics.metric_library[_default_metric_name]
graph = threshold_graph.ThresholdGraph(settings.MAX_GRAPH_DEGREE)
ignore_set = set()
for kanji_a, kanji_b in consoleLog.withProgress(
iunique_pairs(kanji_set), 100):
if kanji_a in ignore_set or kanji_b in ignore_set:
continue
try:
weight = metric(kanji_a, kanji_b)
except DomainError, e:
kanji = e.message
ignore_set.add(kanji)
continue
graph.connect(kanji_a, kanji_b, weight)
def _dump_responses(filename):
_log.log(filename + " ", newLine=False)
with open(filename, "w") as ostream:
users = User.objects.exclude(email__in=EXCLUDE_EMAILS)
for user in withProgress(users):
for response in drill_models.MultipleChoiceResponse.objects.filter(user=user):
question = response.question
test_set = drill_models.TestSet.objects.get(questions=question)
record = {
"user_id": user.id,
"test_id": test_set.id,
"timestamp": response.timestamp.ctime(),
"pivot": question.pivot,
"pivot_type": question.pivot_type,
"question_type": question.question_type,
"is_adaptive": question.question_plugin.is_adaptive,
"distractors": [o.value for o in question.multiplechoicequestion.options.all()],
"correct_response": question.multiplechoicequestion.options.get(is_correct=True).value,
"user_response": response.option.value,
}
print >> ostream, simplejson.dumps(record)
def _pad_readings(self, prior_dist):
"""
Once the reading distribution has been copied over, we still have the
problem that there may not be enough erroneous readings to meet the
minimum number of distractors we wish to generate.
To circumvent this problem, we pad with random distractors.
"""
_log.log('Padding results ', newLine=False)
conditions = set(o['condition'] for o in \
prior_dist.density.all().values('condition'))
for (condition,) in consoleLog.withProgress(conditions):
exclude_set = set(
o.reading for o in \
lexicon_models.KanjiReading.objects.filter(
kanji__kanji=condition)
)
n_stored = prior_dist.density.filter(condition=condition).exclude(
symbol__in=exclude_set).count()
sub_dist = ProbDist.from_query_set(prior_dist.density.filter(
condition=condition))
exclude_set.update(sub_dist.keys())
n_needed = settings.MIN_TOTAL_DISTRACTORS - n_stored
min_prob = min(sub_dist.itervalues()) / 2
while n_needed > 0:
for row in lexicon_models.KanjiReadingProb.sample_n(n_needed):
if row.symbol not in exclude_set:
sub_dist[row.symbol] = min_prob
exclude_set.add(row.symbol)
n_needed -= 1
if n_needed == 0:
break
sub_dist.normalise()
sub_dist.save_to(prior_dist.density, condition=condition)
return
def simulate_search(output_file, strategy='greedy',
k=settings.N_NEIGHBOURS_RECALLED, error_rate=0.0):
"""
Simulate user searches on every query/target pair from the flashcard
dataset, using one of the available strategies. The resulting query paths
are dumped to the specified file.
"""
if strategy == 'greedy':
search_fn = _greedy_search
elif strategy == 'shortest':
search_fn = _breadth_first_search
elif strategy == 'random':
random.seed(123456789)
search_fn = _random_stumble
else:
raise ValueError(strategy)
traces = []
for query, target in withProgress(_load_search_examples()):
path = search_fn(query, target, k=k, error_rate=error_rate)
traces.append((query, target, path))
TraceFile.save(traces, output_file)
print 'Paths dumped to %s' % output_file
