- question type text now has a transformed option that is applied to

the answers before the correction.

- question type text now has a transformed option that is applied to
the answers before the correction.
Miguel Barão
1 parent 4cbc74ad
Showing 3 changed files with 30 additions and 5 deletions Show diff stats
aprendizations/learnapp.py
aprendizations/questions.py
aprendizations/student.py
@@ -370,7 +370,6 @@ class LearnApp(object):
  
             t['path'] = path.join(g.graph['prefix'], tref)    # prefix/topic
  
-
     # ========================================================================
     # methods that do not change state (pure functions)
     # ========================================================================
@@ -379,6 +378,7 @@ class LearnApp(object):
     # Buils dictionary of question factories
     # ------------------------------------------------------------------------
     def make_factory(self) -> Dict[str, QFactory]:
+
         logger.info('Building questions factory:')
         factory: Dict[str, QFactory] = {}
         g = self.deps
@@ -234,7 +234,8 @@ class QuestionText(Question):
  
         self.set_defaults(QDict({
             'text': '',
-            'correct': [],
+            'correct': [],  # no correct answers, always wrong
+            'transform': [],  # transformations applied to the answer, in order
             }))
  
         # make sure its always a list of possible correct answers
@@ -244,12 +245,36 @@ class QuestionText(Question):
         # make sure all elements of the list are strings
         self['correct'] = [str(a) for a in self['correct']]
  
+        # make sure that the answers are invariant with respect to the filters
+        if any(c != self.transform(c) for c in self['correct']):
+            logger.warning(f'in "{self["ref"]}", correct answers are not '
+                           'invariant wrt transformations')
+
+    # ------------------------------------------------------------------------
+    # apply optional filters to the answer
+    def transform(self, ans):
+        for f in self['transform']:
+            if f == 'remove_space':
+                ans = ans.replace(' ', '')
+            elif f == 'trim':
+                ans = ans.strip()
+            elif f == 'normalize_space':
+                ans = re.sub(r'\s+', ' ', ans.strip())
+            elif f == 'lower':
+                ans = ans.lower()
+            elif f == 'upper':
+                ans = ans.upper()
+            else:
+                logger.warning(f'in "{self["ref"]}", unknown transform "{f}"')
+        return ans
+
     # ------------------------------------------------------------------------
     def correct(self) -> None:
         super().correct()
  
         if self['answer'] is not None:
-            self['grade'] = 1.0 if self['answer'] in self['correct'] else 0.0
+            answer = self.transform(self['answer'])  # apply transformations
+            self['grade'] = 1.0 if answer in self['correct'] else 0.0
  
  
 # ============================================================================
@@ -3,7 +3,7 @@
 from datetime import datetime
 import logging
 import random
-from typing import Dict, List, Optional, Tuple
+from typing import List, Optional, Tuple
  
 # third party libraries
 import networkx as nx
@@ -176,7 +176,7 @@ class StudentState(object):
             dt = now - s['date']
             try:
                 forgetting_factor = self.deps.nodes[tref]['forgetting_factor']
-                s['level'] *= forgetting_factor ** dt.days   # forgetting factor
+                s['level'] *= forgetting_factor ** dt.days  # forgetting factor
             except KeyError:
                 logger.warning(f'Topic {tref} is not on the graph!')
...	...	@@ -370,7 +370,6 @@ class LearnApp(object):
370	370
371	371	t['path'] = path.join(g.graph['prefix'], tref) # prefix/topic
372	372
373		-
374	373	# ========================================================================
375	374	# methods that do not change state (pure functions)
376	375	# ========================================================================
...	...	@@ -379,6 +378,7 @@ class LearnApp(object):
379	378	# Buils dictionary of question factories
380	379	# ------------------------------------------------------------------------
381	380	def make_factory(self) -> Dict[str, QFactory]:
	381	+
382	382	logger.info('Building questions factory:')
383	383	factory: Dict[str, QFactory] = {}
384	384	g = self.deps
...	...
...	...	@@ -234,7 +234,8 @@ class QuestionText(Question):
234	234
235	235	self.set_defaults(QDict({
236	236	'text': '',
237		- 'correct': [],
	237	+ 'correct': [], # no correct answers, always wrong
	238	+ 'transform': [], # transformations applied to the answer, in order
238	239	}))
239	240
240	241	# make sure its always a list of possible correct answers
...	...	@@ -244,12 +245,36 @@ class QuestionText(Question):
244	245	# make sure all elements of the list are strings
245	246	self['correct'] = [str(a) for a in self['correct']]
246	247
	248	+ # make sure that the answers are invariant with respect to the filters
	249	+ if any(c != self.transform(c) for c in self['correct']):
	250	+ logger.warning(f'in "{self["ref"]}", correct answers are not '
	251	+ 'invariant wrt transformations')
	252	+
	253	+ # ------------------------------------------------------------------------
	254	+ # apply optional filters to the answer
	255	+ def transform(self, ans):
	256	+ for f in self['transform']:
	257	+ if f == 'remove_space':
	258	+ ans = ans.replace(' ', '')
	259	+ elif f == 'trim':
	260	+ ans = ans.strip()
	261	+ elif f == 'normalize_space':
	262	+ ans = re.sub(r'\s+', ' ', ans.strip())
	263	+ elif f == 'lower':
	264	+ ans = ans.lower()
	265	+ elif f == 'upper':
	266	+ ans = ans.upper()
	267	+ else:
	268	+ logger.warning(f'in "{self["ref"]}", unknown transform "{f}"')
	269	+ return ans
	270	+
247	271	# ------------------------------------------------------------------------
248	272	def correct(self) -> None:
249	273	super().correct()
250	274
251	275	if self['answer'] is not None:
252		- self['grade'] = 1.0 if self['answer'] in self['correct'] else 0.0
	276	+ answer = self.transform(self['answer']) # apply transformations
	277	+ self['grade'] = 1.0 if answer in self['correct'] else 0.0
253	278
254	279
255	280	# ============================================================================
...	...
...	...	@@ -3,7 +3,7 @@
3	3	from datetime import datetime
4	4	import logging
5	5	import random
6		-from typing import Dict, List, Optional, Tuple
	6	+from typing import List, Optional, Tuple
7	7
8	8	# third party libraries
9	9	import networkx as nx
...	...	@@ -176,7 +176,7 @@ class StudentState(object):
176	176	dt = now - s['date']
177	177	try:
178	178	forgetting_factor = self.deps.nodes[tref]['forgetting_factor']
179		- s['level'] = forgetting_factor * dt.days # forgetting factor
	179	+ s['level'] = forgetting_factor * dt.days # forgetting factor
180	180	except KeyError:
181	181	logger.warning(f'Topic {tref} is not on the graph!')
182	182
...	...