def get_test_size_stats():
"""
Fetches the distribution of test sizes chosen by users, and the completion
statistics for each reported size.
"""
cursor = connection.cursor()
cursor.execute("""
SELECT n_items, COUNT(*), AVG(is_finished) FROM (
SELECT
ts.id AS test_set_id,
COUNT(*) AS n_items,
(ts.end_time IS NOT NULL) AS is_finished
FROM drill_testset_questions AS tsq
INNER JOIN drill_testset AS ts
ON tsq.testset_id = ts.id
GROUP BY ts.id
) as tmp
GROUP BY n_items
ORDER BY n_items ASC
""")
data = cursor.fetchall()
dist = FreqDist()
completion_rates = {}
for n_items, n_tests, completion_rate in data:
dist.inc(n_items, n_tests)
completion_rates[n_items] = completion_rate
results = []
for sample in sorted(dist.samples()):
results.append((sample, dist.freq(sample), completion_rates[sample]))
return results
def get_accuracy_by_pivot_type():
cursor = connection.cursor()
cursor.execute("""
SELECT
question.pivot,
SUM(chosen_option.is_correct) as n_correct,
COUNT(*) as n_responses
FROM (
SELECT mco.question_id, mco.is_correct
FROM drill_multiplechoiceresponse AS mcr
INNER JOIN drill_multiplechoiceoption AS mco
ON mcr.option_id = mco.id
) as chosen_option
INNER JOIN drill_question AS question
ON chosen_option.question_id = question.id
WHERE question.pivot_type = "w"
GROUP BY question.pivot
""")
raw_data = cursor.fetchall()
counts = {
'Hiragana': FreqDist(),
'Katakana': FreqDist(),
'Kanji': FreqDist()
}
complex_scripts = set([scripts.Script.Kanji, scripts.Script.Unknown])
only_katakana = set([scripts.Script.Katakana])
for word, n_correct, n_responses in raw_data:
scripts_found = scripts.script_types(word)
if scripts_found.intersection(complex_scripts):
dist = counts['Kanji']
elif scripts_found.intersection(only_katakana):
dist = counts['Katakana']
else:
dist = counts['Hiragana']
dist.inc(True, int(n_correct))
dist.inc(False, int(n_responses - n_correct))
keys = ('Hiragana', 'Katakana', 'Kanji')
data = [(key, counts[key].freq(True)) for key in keys]
average = FreqDist()
for key in keys:
average.inc(True, counts[key][True])
average.inc(False, counts[key][False])
data.append(('Average', average.freq(True)))
return data
def get_language_data(name):
"Fetches information about user first and second languages."
fields_needed = ['user_id']
if name in ['first', 'combined']:
fields_needed.append('first_language')
elif name in ['second', 'combined']:
fields_needed.append('second_languages')
else:
assert name == 'combined'
valid_users = set(u.id for u in User.objects.all()
if u.testset_set.exclude(end_time=None).count() > 0)
profiles = UserProfile.objects.filter(user__id__in=valid_users).values(
*fields_needed)
dist = FreqDist()
for profile in profiles:
if 'first_language' in profile:
lang = profile['first_language'].title()
dist.inc(lang)
if 'second_languages' in profile:
if profile['second_languages']:
for lang in profile['second_languages'].split(','):
lang = lang.strip().title()
if lang == 'Japanese':
continue
dist.inc(lang)
elif name != 'lang_combined':
dist.inc('None')
return dist
def get_pivot_response_stats(pivot_id, pivot_type):
"""
Given a particular pivot, generate a distribution of observed erroneous
responses for each type of plugin.
"""
cursor = connection.cursor()
cursor.execute("""
SELECT plugin_option.plugin_id, plugin_option.value
FROM drill_multiplechoiceresponse AS mcr
INNER JOIN (
SELECT pivot_qn.plugin_id, mco.id AS option_id, mco.value
FROM (
SELECT id, question_plugin_id AS plugin_id
FROM drill_question
WHERE pivot_type = "%(pivot_type)s"
AND pivot_id = %(pivot_id)d
) AS pivot_qn
INNER JOIN drill_multiplechoiceoption AS mco
ON mco.question_id = pivot_qn.id
) AS plugin_option
ON plugin_option.option_id = mcr.option_id
""" % {
'pivot_type': pivot_type,
'pivot_id': pivot_id
})
rows = cursor.fetchall()
dist_map = {}
plugin_ids_used = set(plugin_id for (plugin_id, error_value) in rows)
for plugin_id in plugin_ids_used:
dist_map[plugin_id] = FreqDist()
for plugin_id, error_value in rows:
dist_map[plugin_id].inc(error_value)
plugin_map = drill_models.QuestionPlugin.objects.in_bulk(dist_map.keys())
results = [(plugin_map[plugin_id].name, dist) \
for (plugin_id, dist) in dist_map.iteritems()]
combined_dist = FreqDist()
for name, dist in results:
combined_dist.inc(name, dist.N())
results[0:0] = [('By plugin type', combined_dist)]
return results
def get_mean_exposures_per_pivot():
"Returns the number of exposures each pivot received."
cursor = connection.cursor()
cursor.execute("""
SELECT pivot, pivot_type, COUNT(*) as n_exposures
FROM drill_question
GROUP BY CONCAT(pivot, "|", pivot_type)
""")
data = cursor.fetchall()
word_c = []
kanji_c = []
combined_c = []
kanji_inc_dist = FreqDist()
for pivot, pivot_type, n_exposures in data:
combined_c.append(n_exposures)
if pivot_type == 'k':
kanji_c.append(n_exposures)
kanji_inc_dist.inc(pivot, n_exposures)
elif pivot_type == 'w':
word_c.append(n_exposures)
for kanji in scripts.unique_kanji(pivot):
kanji_inc_dist.inc(kanji, n_exposures)
else:
raise ValueError('unknown pivot type: %s' % pivot_type)
return [
('Words', mean(word_c)),
('Kanji', mean(kanji_c)),
('Combined', mean(combined_c)),
('Kanji combined', mean(kanji_inc_dist.values())),
]
def get_language_data(name):
"Fetches information about user first and second languages."
fields_needed = ['user_id']
if name in ['first', 'combined']:
fields_needed.append('first_language')
elif name in ['second', 'combined']:
fields_needed.append('second_languages')
else:
assert name == 'combined'
valid_users = set(u.id for u in User.objects.all() if u.testset_set.exclude(
end_time=None).count() > 0)
profiles = UserProfile.objects.filter(user__id__in=valid_users).values(
*fields_needed)
dist = FreqDist()
for profile in profiles:
if 'first_language' in profile:
lang = profile['first_language'].title()
dist.inc(lang)
if 'second_languages' in profile:
if profile['second_languages']:
for lang in profile['second_languages'].split(','):
lang = lang.strip().title()
if lang == 'Japanese':
continue
dist.inc(lang)
elif name != 'lang_combined':
dist.inc('None')
return dist
def get_pivot_response_stats(pivot_id, pivot_type):
"""
Given a particular pivot, generate a distribution of observed erroneous
responses for each type of plugin.
"""
cursor = connection.cursor()
cursor.execute("""
SELECT plugin_option.plugin_id, plugin_option.value
FROM drill_multiplechoiceresponse AS mcr
INNER JOIN (
SELECT pivot_qn.plugin_id, mco.id AS option_id, mco.value
FROM (
SELECT id, question_plugin_id AS plugin_id
FROM drill_question
WHERE pivot_type = "%(pivot_type)s"
AND pivot_id = %(pivot_id)d
) AS pivot_qn
INNER JOIN drill_multiplechoiceoption AS mco
ON mco.question_id = pivot_qn.id
) AS plugin_option
ON plugin_option.option_id = mcr.option_id
""" % {'pivot_type': pivot_type, 'pivot_id': pivot_id})
rows = cursor.fetchall()
dist_map = {}
plugin_ids_used = set(plugin_id for (plugin_id, error_value) in rows)
for plugin_id in plugin_ids_used:
dist_map[plugin_id] = FreqDist()
for plugin_id, error_value in rows:
dist_map[plugin_id].inc(error_value)
plugin_map = drill_models.QuestionPlugin.objects.in_bulk(dist_map.keys())
results = [(plugin_map[plugin_id].name, dist) \
for (plugin_id, dist) in dist_map.iteritems()]
combined_dist = FreqDist()
for name, dist in results:
combined_dist.inc(name, dist.N())
results[0:0] = [('By plugin type', combined_dist)]
return results
def get_test_size_stats():
"""
Fetches the distribution of test sizes chosen by users, and the completion
statistics for each reported size.
"""
cursor = connection.cursor()
cursor.execute("""
SELECT n_items, COUNT(*), AVG(is_finished) FROM (
SELECT
ts.id AS test_set_id,
COUNT(*) AS n_items,
(ts.end_time IS NOT NULL) AS is_finished
FROM drill_testset_questions AS tsq
INNER JOIN drill_testset AS ts
ON tsq.testset_id = ts.id
GROUP BY ts.id
) as tmp
GROUP BY n_items
ORDER BY n_items ASC
""")
data = cursor.fetchall()
dist = FreqDist()
completion_rates = {}
for n_items, n_tests, completion_rate in data:
dist.inc(n_items, n_tests)
completion_rates[n_items] = completion_rate
results = []
for sample in sorted(dist.samples()):
results.append((sample, dist.freq(sample), completion_rates[sample]))
return results
def get_mean_exposures_per_pivot():
"Returns the number of exposures each pivot received."
cursor = connection.cursor()
cursor.execute("""
SELECT pivot, pivot_type, COUNT(*) as n_exposures
FROM drill_question
GROUP BY CONCAT(pivot, "|", pivot_type)
""")
data = cursor.fetchall()
word_c = []
kanji_c = []
combined_c = []
kanji_inc_dist = FreqDist()
for pivot, pivot_type, n_exposures in data:
combined_c.append(n_exposures)
if pivot_type == 'k':
kanji_c.append(n_exposures)
kanji_inc_dist.inc(pivot, n_exposures)
elif pivot_type == 'w':
word_c.append(n_exposures)
for kanji in scripts.unique_kanji(pivot):
kanji_inc_dist.inc(kanji, n_exposures)
else:
raise ValueError('unknown pivot type: %s' % pivot_type)
return [
('Words', mean(word_c)),
('Kanji', mean(kanji_c)),
('Combined', mean(combined_c)),
('Kanji combined', mean(kanji_inc_dist.values())),
]
def get_accuracy_by_pivot_type():
cursor = connection.cursor()
cursor.execute("""
SELECT
question.pivot,
SUM(chosen_option.is_correct) as n_correct,
COUNT(*) as n_responses
FROM (
SELECT mco.question_id, mco.is_correct
FROM drill_multiplechoiceresponse AS mcr
INNER JOIN drill_multiplechoiceoption AS mco
ON mcr.option_id = mco.id
) as chosen_option
INNER JOIN drill_question AS question
ON chosen_option.question_id = question.id
WHERE question.pivot_type = "w"
GROUP BY question.pivot
""")
raw_data = cursor.fetchall()
counts = {'Hiragana': FreqDist(), 'Katakana': FreqDist(), 'Kanji':
FreqDist()}
complex_scripts = set([scripts.Script.Kanji, scripts.Script.Unknown])
only_katakana = set([scripts.Script.Katakana])
for word, n_correct, n_responses in raw_data:
scripts_found = scripts.script_types(word)
if scripts_found.intersection(complex_scripts):
dist = counts['Kanji']
elif scripts_found.intersection(only_katakana):
dist = counts['Katakana']
else:
dist = counts['Hiragana']
dist.inc(True, int(n_correct))
dist.inc(False, int(n_responses - n_correct))
keys = ('Hiragana', 'Katakana', 'Kanji')
data = [(key, counts[key].freq(True)) for key in keys]
average = FreqDist()
for key in keys:
average.inc(True, counts[key][True])
average.inc(False, counts[key][False])
data.append(('Average', average.freq(True)))
return data
