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exercise_statistics.py
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exercise_statistics.py
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import math
import functools
import logging
from mapreduce import control
from mapreduce import operation as op
import request_handler
import models
import consts
# /admin/startnewexercisestatisticsmapreduce is called periodically by a cron job
class StartNewExerciseStatisticsMapReduce(request_handler.RequestHandler):
def get(self):
# Admin-only restriction is handled by /admin/* URL pattern
# so this can be called by a cron job.
# Start a new Mapper task for calling statistics_update_map
mapreduce_id = control.start_map(
name = "UpdateExerciseStatistics",
handler_spec = "exercise_statistics.statistics_update_map",
reader_spec = "mapreduce.input_readers.DatastoreInputReader",
reader_parameters = {"entity_kind": "models.Exercise"})
self.response.out.write("OK: " + str(mapreduce_id))
# statistics_update_map is called by a background MapReduce task.
# Each call updates the statistics for a single exercise.
def statistics_update_map(exercise):
# Get the last 5,000 correct problems for this exercise for analysis
query = models.ProblemLog.all()
query.filter('exercise =', exercise.name)
query.filter('correct = ', True)
query.order('-time_done')
problem_logs = query.fetch(consts.LATEST_PROBLEMS_FOR_EXERCISE_STATISTICS)
list_time_taken = []
for problem_log in problem_logs:
# Ignore outliers
if problem_log.time_taken > 3.0 and problem_log.time_taken < consts.MAX_WORKING_ON_PROBLEM_SECONDS:
list_time_taken.append(float(problem_log.time_taken))
if len(list_time_taken) <= consts.REQUIRED_PROBLEMS_FOR_EXERCISE_STATISTICS:
return
list_time_taken = sorted(list_time_taken)
# The smallest times are the fastest 10th percentile
fastest_percentile = percentile(list_time_taken, consts.FASTEST_EXERCISE_PERCENTILE)
fastest_percentile = min(consts.MAX_SECONDS_PER_FAST_PROBLEM, fastest_percentile)
fastest_percentile = max(consts.MIN_SECONDS_PER_FAST_PROBLEM, fastest_percentile)
exercise.seconds_per_fast_problem = fastest_percentile
yield op.db.Put(exercise)
# See http://code.activestate.com/recipes/511478-finding-the-percentile-of-the-values/
def percentile(N, percent, key=lambda x:x):
"""
Find the percentile of a list of values.
@parameter N - is a list of values. Note N MUST BE already sorted.
@parameter percent - a float value from 0.0 to 1.0.
@parameter key - optional key function to compute value from each element of N.
@return - the percentile of the values
"""
if not N:
return None
k = (len(N)-1) * percent
f = math.floor(k)
c = math.ceil(k)
if f == c:
return key(N[int(k)])
d0 = key(N[int(f)]) * (k-f)
d1 = key(N[int(c)]) * (c-k)
return d0+d1