knowledge.py
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# python standard library
import random
from datetime import datetime
import logging
# libraries
import networkx as nx
# this project
# import questions
# setup logger for this module
logger = logging.getLogger(__name__)
# ----------------------------------------------------------------------------
# kowledge state of each student....??
# ----------------------------------------------------------------------------
class StudentKnowledge(object):
# =======================================================================
# methods that update state
# =======================================================================
def __init__(self, deps, state={}):
# graph with topic dependencies shared between all students
self.deps = deps
# state only contains information on unlocked topics
# {'topic_id': {'level':0.5, 'date': datetime}, ...}
self.state = state
# update state level based on the elapsed time (no dependencies... FIXME)
now = datetime.now()
for s in state.values():
dt = now - s['date']
s['level'] *= 0.975 ** dt.days
# compute recommended sequence of topics ['a', 'b', ...]
self.topic_sequence = list(nx.topological_sort(self.deps))
# ------------------------------------------------------------------------
# Unlock topics whose dependencies are satisfied (> min_level)
# ------------------------------------------------------------------------
def unlock_topics(self):
min_level = 0.01 # minimum level to unlock
for topic in self.topic_sequence:
if topic not in self.state: # if locked
pred = self.deps.predecessors(topic)
if all(d in self.state and self.state[d]['level'] > min_level for d in pred): # dependencies done
self.state[topic] = {
'level': 0.0,
'date': datetime.now()
}
logger.debug(f'unlocked {topic}')
# ------------------------------------------------------------------------
# Recommends a topic to practice/learn from the state.
# FIXME untested
# ------------------------------------------------------------------------
def recommended_topic(self):
return min(self.state.items(), key=lambda x: x[1]['level'])[0]
# if not topic:
# for topic in self.topic_sequence:
# unlocked = topic in self.state
# needs_work = unlocked and self.state[topic]['level'] < 0.8
# factory = self.deps.node[topic]['factory']
# if factory and (not unlocked or needs_work):
# break
# use given topic if possible
# else:
# unlocked = topic in self.state
# factory = self.deps.node[topic]['factory']
# if not factory or not unlocked:
# logger.debug(f'Can\'t start topic "{topic}".')
# return
# ------------------------------------------------------------------------
# Start a new topic. If not provided, gets a recommendation.
# questions: list of generated questions to do in the topic
# finished_questions: [] will contain correctly answered questions
# current_question: the current question to be presented
# ------------------------------------------------------------------------
def init_topic(self, topic=''):
# self.unlock_topics() # FIXME needed?
if not topic:
topic = self.recommended_topic()
self.current_topic = topic
logger.info(f'Topic set to "{topic}"')
# generate question instances for current topic
factory = self.deps.node[topic]['factory']
questionlist = self.deps.node[topic]['questions']
self.questions = [factory[qref].generate() for qref in questionlist]
self.finished_questions = []
self.current_question = self.questions.pop(0) # FIXME crash if empty
self.current_question['start_time'] = datetime.now()
# ------------------------------------------------------------------------
# returns the current question with correction, time and comments updated
# ------------------------------------------------------------------------
def check_answer(self, answer):
logger.debug('StudentKnowledge.check_answer()')
q = self.current_question
q['finish_time'] = datetime.now()
q['answer'] = answer
grade = q.correct()
logger.debug(f'Grade = {grade:.2} ({q["ref"]})')
# if answer is correct, get next question
if grade > 0.999:
self.finished_questions.append(q)
try:
self.current_question = self.questions.pop(0) # FIXME empty?
except IndexError:
self.current_question = None
self.state[self.current_topic] = {
'level': 1.0,
'date': datetime.now()
}
else:
self.current_question['start_time'] = datetime.now()
# if answer is wrong, keep same question and add a similar one at the end
else:
factory = self.deps.node[self.current_topic]['factory']
self.questions.append(factory[q['ref']].generate())
# returns answered and corrected question
return q
# ========================================================================
# pure functions of the state (no side effects)
# ========================================================================
# ------------------------------------------------------------------------
def get_current_question(self):
return self.current_question
# ------------------------------------------------------------------------
def get_current_topic(self):
return self.current_topic
# ------------------------------------------------------------------------
# Return list of tuples (topic, level).
# Levels are in the interval [0, 1] or None if the topic is locked.
# Topics unlocked but not yet done have level 0.0.
# Example: [('topic_A', 0.9), ('topic_B', None), ...]
# ------------------------------------------------------------------------
def get_knowledge_state(self):
return [{
'ref': ref,
'name': self.deps.nodes[ref]['name'],
'level': self.state[ref]['level'] if ref in self.state else None
} for ref in self.topic_sequence ]
# ts = []
# for ref in self.topic_sequence:
# ts.append({
# 'ref': ref,
# 'name': self.deps.nodes[ref]['name'],
# 'level': self.state[ref]['level'] if ref in self.state else None
# })
# return ts
# ------------------------------------------------------------------------
def get_topic_progress(self):
return len(self.finished_questions) / (1 + len(self.finished_questions) + len(self.questions))