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import numpy as np
from codeScripts.rubrics import *
class PlentasMethodology():
"""
The methodology developed in Plentas consists in iteratively compute the similarity between two sentences: the baseline response and one variable group of sentences from the student's response. The goal of it is to identify in the text the subresponses to each subquestion so the answer-question similarity is better obtained and thus the overall calculated similarity fits better to the expected one.
Inputs:
-settings: The settings from the config json and the api.
"""
def __init__(self, settings):
self.settings = settings
self.maxSimilarity = -99999
self.SemanticLevel = Semantica2(self.settings)
def getSimilarity(self, sentences, similarityMethod):
"""
This function calculates the similarity between two responses using the Plentas methodology
Inputs:
sentences: pre-processed sentences of the student's response
similarityMethod: choose between spacy or bert
Outputs:
similarity: an array of the generated similarity for each subquestion
"""
#Initializing the similarity array so if the student's response is blank default content is output
similarity = np.zeros(len(self.settings.indice_minipreguntas))
#obtaining the similarity for each subquestion
for minirespuesta, minipregunta in zip(self.settings.minirespuestas, self.settings.indice_minipreguntas):
self.SemanticLevel.output.initInforms(self.settings.studentID, minipregunta, similarityMethod)
#if the response is not blank ...
if len(sentences) >= 1 and sentences[0] != '':
self.maxSimilarity = -99999
#varying the group of sentences
for agrupation in list(range(self.settings.minAgrupation,self.settings.maxAgrupation)):
#varying the size of the group of sentences
for s in range(len(sentences)):
try:
#extracting the sentences
r_alumno, r_label = self.__Line2LineAnalysis__(sentences, s, agrupation)
#computing its similarity
#similar = self.__computeSimilarity__(r_alumno, minirespuesta, similarityMethod)
similar = self.SemanticLevel.computeSimilarity(r_alumno, minirespuesta, similarityMethod)
self.SemanticLevel.output.updateInformsBucle(self.settings.studentID, minipregunta, r_alumno, r_label, agrupation, similar, similarityMethod, 1 if similar > self.maxSimilarity else 0)
#storing the highest
if similar > self.maxSimilarity:
self.maxSimilarity = similar
except:
break
#stacking the similarity of each subquestion
similarity[int(minipregunta[12:])] = self.maxSimilarity
return similarity
def __Line2LineAnalysis__(self, sentences, size, start):
"""
This function extracts the required group of sentences from a response.
Inputs:
-sentences: the array of sentences from the student's response
-size: the max number of sentences to extract.
-start: the array position from where to start extracting
Outputs:
respuesta_alumno: the extracted sentences
r_name: the label of those sentences (their position in the response and, thus, in the input array)
"""
new_respuesta = ""
breaking_variable = sentences[size+start-1]
for line in sentences[size:size+start]:
new_respuesta= new_respuesta + line + '. '
respuesta_alumno = new_respuesta.lower()
if start == 1:
r_name = "Line " + str(size+1)
else:
r_name = "Lines " + str(size+1) + " - " + str(size+start)
return respuesta_alumno, r_name
def EvaluationMethod(self, studentID, response, similarity_array, similarity_type = "spacy"):
notaSemantica = 0
esSuperior = 0
esIntermedio = 0
for umbralL, umbralH in zip(self.SemanticLevel.output.min_umbral, self.SemanticLevel.output.max_umbral):
for minipregunta, similarity in zip(self.settings.indice_minipreguntas, similarity_array):
#print(minipregunta, similarity)
if similarity >= umbralL:
if similarity <= umbralH:
if not esSuperior:
esIntermedio = 1
else:
esIntermedio = 0
esSuperior = 1
if esSuperior:
notaSemantica +=1
elif esIntermedio:
notaSemantica += 0.5
esSuperior = 0
esIntermedio = 0
notaSemantica = notaSemantica/len(self.settings.indice_minipreguntas)
self.SemanticLevel.output.updateInforms(studentID, umbralL, umbralH, notaSemantica, similarity_type, response)
#Descomment if analyzing more than one threshold option
#if umbralL == 0.3 and umbralH == 0.7:
#notaGuardar = notaSemantica
#notaSemantica = 0
#return notaGuardar
return notaSemantica
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