student.py
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# python standard library
import random
from datetime import datetime
import logging
# third party libraries
import networkx as nx
# setup logger for this module
logger = logging.getLogger(__name__)
# ----------------------------------------------------------------------------
# kowledge state of each student....??
# Contains:
# state - dict of topics with state of unlocked topics
# deps - access to dependency graph shared between students
# topic_sequence - list with the order of recommended topics
# ----------------------------------------------------------------------------
class StudentState(object):
# =======================================================================
# methods that update state
# =======================================================================
def __init__(self, deps, factory, state={}):
self.deps = deps # shared dependency graph
self.factory = factory # question factory
self.state = state # {'topic': {'level': 0.5, 'date': datetime}, ...}
self.update_topic_levels() # applies forgetting factor
self.unlock_topics() # whose dependencies have been completed
self.topic_sequence = self.recommend_topic_sequence() # ['ref1', ...]
self.current_topic = None
# ------------------------------------------------------------------------
# Updates the proficiency levels of the topics, with forgetting factor
# FIXME no dependencies are considered yet...
# ------------------------------------------------------------------------
def update_topic_levels(self):
now = datetime.now()
for tref, s in self.state.items():
dt = now - s['date']
s['level'] *= 0.98 ** dt.days # forgetting factor
# ------------------------------------------------------------------------
# Unlock topics whose dependencies are satisfied (> min_level)
# ------------------------------------------------------------------------
def unlock_topics(self):
for topic in self.deps.nodes():
if topic not in self.state: # if locked
pred = self.deps.predecessors(topic)
min_level = self.deps.node[topic]['min_level']
if all(d in self.state and self.state[d]['level'] > min_level
for d in pred): # all deps are greater than min_level
self.state[topic] = {
'level': 0.0, # unlocked
'date': datetime.now()
}
logger.debug(f'[unlock_topics] Unlocked "{topic}".')
# else: # lock this topic if deps do not satisfy min_level
# del self.state[topic]
# ------------------------------------------------------------------------
# Start a new topic.
# questions: list of generated questions to do in the topic
# current_question: the current question to be presented
# ------------------------------------------------------------------------
async def start_topic(self, topic):
logger.debug(f'[start_topic] topic "{topic}"')
if self.current_topic == topic:
logger.info('Restarting current topic is not allowed.')
return
# do not allow locked topics
if self.is_locked(topic):
logger.debug(f'[start_topic] topic "{topic}" is locked')
return
# starting new topic
self.current_topic = topic
self.correct_answers = 0
self.wrong_answers = 0
t = self.deps.node[topic]
k = t['choose']
if t['shuffle_questions']:
questions = random.sample(t['questions'], k=k)
else:
questions = t['questions'][:k]
logger.debug(f'[start_topic] questions: {", ".join(questions)}')
# synchronous
# self.questions = [self.factory[ref].generate()
# for ref in questions]
# asynchronous:
self.questions = [await self.factory[ref].generate_async()
for ref in questions]
n = len(self.questions)
logger.debug(f'[start_topic] generated {n} questions')
# get first question
self.next_question()
# ------------------------------------------------------------------------
# The topic has finished and there are no more questions.
# The topic level is updated in state and unlocks are performed.
# The current topic is unchanged.
# ------------------------------------------------------------------------
def finish_topic(self):
logger.debug(f'[finish_topic] current_topic {self.current_topic}')
self.state[self.current_topic] = {
'date': datetime.now(),
'level': self.correct_answers / (self.correct_answers +
self.wrong_answers)
}
# self.current_topic = None
self.unlock_topics()
# ------------------------------------------------------------------------
# corrects current question with provided answer.
# implements the logic:
# - if answer ok, goes to next question
# - if wrong, counts number of tries. If exceeded, moves on.
# ------------------------------------------------------------------------
async def check_answer(self, answer):
logger.debug('[check_answer]')
q = self.current_question
q['answer'] = answer
q['finish_time'] = datetime.now()
await q.correct_async()
logger.debug(f'[check_answer] Grade {q["grade"]:.2} in {q["ref"]}')
if q['grade'] > 0.999:
self.correct_answers += 1
self.next_question()
action = 'right'
else:
self.wrong_answers += 1
self.current_question['tries'] -= 1
if self.current_question['tries'] > 0:
action = 'try_again'
else:
action = 'wrong'
if self.current_question['append_wrong']:
logger.debug('[check_answer] Wrong, append new instance')
self.questions.append(self.factory[q['ref']].generate())
self.next_question()
# returns corrected question (not new one) which might include comments
return q, action
# ------------------------------------------------------------------------
# Move to next question
# ------------------------------------------------------------------------
def next_question(self):
try:
self.current_question = self.questions.pop(0)
except IndexError:
self.current_question = None
self.finish_topic()
else:
self.current_question['start_time'] = datetime.now()
default_maxtries = self.deps.nodes[self.current_topic]['max_tries']
maxtries = self.current_question.get('max_tries', default_maxtries)
self.current_question['tries'] = maxtries
logger.debug(f'[next_question] "{self.current_question["ref"]}"')
return self.current_question # question or None
# ========================================================================
# pure functions of the state (no side effects)
# ========================================================================
def topic_has_finished(self):
return self.current_question is None
# ------------------------------------------------------------------------
# compute recommended sequence of topics ['a', 'b', ...]
# ------------------------------------------------------------------------
def recommend_topic_sequence(self, target=None):
tt = list(nx.topological_sort(self.deps))
unlocked = [t for t in tt if t in self.state]
locked = [t for t in tt if t not in unlocked]
return unlocked + locked
# ------------------------------------------------------------------------
def get_current_question(self):
return self.current_question
# ------------------------------------------------------------------------
def get_current_topic(self):
return self.current_topic
# ------------------------------------------------------------------------
def is_locked(self, topic):
return topic not in self.state
# ------------------------------------------------------------------------
# Return list of {ref: 'xpto', name: 'long name', leve: 0.5}
# Levels are in the interval [0, 1] if unlocked or None if locked.
# Topics unlocked but not yet done have level 0.0.
# ------------------------------------------------------------------------
def get_knowledge_state(self):
return [{
'ref': ref,
'type': self.deps.nodes[ref]['type'],
'name': self.deps.nodes[ref]['name'],
'level': self.state[ref]['level'] if ref in self.state else None
} for ref in self.topic_sequence]
# ------------------------------------------------------------------------
def get_topic_progress(self):
return self.correct_answers / (1 + self.correct_answers +
len(self.questions))
# ------------------------------------------------------------------------
def get_topic_level(self, topic):
return self.state[topic]['level']
# ------------------------------------------------------------------------
def get_topic_date(self, topic):
return self.state[topic]['date']
# ------------------------------------------------------------------------
# Recommends a topic to practice/learn from the state.
# ------------------------------------------------------------------------
# def get_recommended_topic(self): # FIXME untested
# return min(self.state.items(), key=lambda x: x[1]['level'])[0]