File size: 7,022 Bytes
8e10d25
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ce84b09
8e10d25
 
 
4991b78
 
8e10d25
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ce84b09
 
 
8e10d25
 
ce84b09
 
8e10d25
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4991b78
 
8e10d25
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ce84b09
8e10d25
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
import pandas as pd

from codeScripts.methodologyPlentas import *
from codeScripts.rubrics import Ortografia2, Sintaxis2, GenerateFeedback
from codeScripts.settings import GetSettings
from codeScripts.utils import getIDrange, splitResponse


class Plentas():

    def __init__(self, config, studentsData):

        self.settings = GetSettings(config, studentsData)
        #semantica
        self.semantic_methodology = PlentasMethodology(self.settings)
        #ortografia
        self.ortografia = Ortografia2(self.settings)
        #sintaxis
        self.sintaxis = Sintaxis2(self.settings)

    def __jsonToExcel__(self, jsonFile):
        outputExcel = dict()
        #print(jsonFile)
        for student in jsonFile:
            for numb_id in student.keys():
                for column in student[numb_id].keys():
                    #if column == "SimilitudBert" or column == "SimilitudSpacy":
                    if column == "SumaTotalBert" or column == "SumaTotalSpacy":
                        pass
                    else:
                        if column not in outputExcel.keys():
                            outputExcel[column] = [] 
                        outputExcel[column].append(student[numb_id][column])
            
                    
        
        df = pd.DataFrame(data=outputExcel)
        #df = (df.T)
        df.to_excel('archivos/OutputFiles2/backendExcel.xlsx')
        return [jsonFile, outputExcel]               
        
  




    def setApiSettings(self, api_settings):
        #lectura de parametros de la api
        self.settings.setApiSettings(api_settings)

    def processApiData(self):


        if self.settings.PesoOrtografia == 0.0:
            self.settings.Ortografia = 0
        if self.settings.PesoSintaxis == 0.0:
            self.settings.Sintaxis = 0
        if self.settings.PesoSemantics == 0.0:
            self.settings.Semantica = 0

        AnalysisOfResponses = []
        IDs = getIDrange(self.settings.rango_ID, self.settings.answersDF)

        print("Total IDS: " + str(len(IDs)))

        for id in IDs:
            studentID = self.settings.answersDF['hashed_id'][id]

            print("StudentID: " + studentID)
            self.settings.studentID = studentID

            nota_rubrica_spacy = 0
            nota_rubrica_bert = 0

            respuesta_alumno_raw = self.settings.answersDF['respuesta'][id].lower()
            
 

            if self.settings.Sintaxis:
                #ponderacion dentro de la función
                nota_rubrica_sintaxis = self.sintaxis.Evaluation(respuesta_alumno_raw)
                nota_rubrica_spacy = nota_rubrica_spacy + nota_rubrica_sintaxis
                nota_rubrica_bert = nota_rubrica_bert + nota_rubrica_sintaxis
            else:
                nota_rubrica_sintaxis = 0
                
            
            if self.settings.Ortografia:
                #ponderacion dentro de la función
                nota_rubrica_ortografia = self.ortografia.Evaluation(respuesta_alumno_raw)

                nota_rubrica_spacy = nota_rubrica_spacy + nota_rubrica_ortografia
                nota_rubrica_bert = nota_rubrica_bert + nota_rubrica_ortografia
            else:
                nota_rubrica_ortografia = 0                
            
        
            if self.settings.Semantica:
                sentencesArr = splitResponse(respuesta_alumno_raw)

                spacy_eval = self.semantic_methodology.getSimilarity(sentencesArr, "spacy")
                bert_eval = self.semantic_methodology.getSimilarity(sentencesArr, "bert")

                for sim1, sim2, nminip in zip(spacy_eval, bert_eval, range(len(spacy_eval))):
                    if sim1 < 0.5:
                        self.settings.minipreguntasMalSpacy = self.settings.minipreguntasMalSpacy + "Minipregunta " + str(nminip + 1)

                    if sim2 < 0.5:
                        if self.settings.minipreguntasMalBert != "" and nminip>0:
                            self.settings.minipreguntasMalBert = self.settings.minipreguntasMalBert + ", "
                            
                        self.settings.minipreguntasMalBert = self.settings.minipreguntasMalBert + "Minipregunta " + str(nminip + 1)



                spacy_eval_umbral = self.semantic_methodology.EvaluationMethod(studentID, "" if len(sentencesArr) == 1 and sentencesArr[0] == '' else sentencesArr, spacy_eval, "spacy")

                bert_eval_umbral = self.semantic_methodology.EvaluationMethod(studentID, "" if len(sentencesArr) == 1 and sentencesArr[0] == '' else sentencesArr, bert_eval, "bert")

                nota_rubrica_spacy = nota_rubrica_spacy + self.settings.PesoSemantics * spacy_eval_umbral
                nota_rubrica_bert = nota_rubrica_bert + self.settings.PesoSemantics * bert_eval_umbral
            else:
                spacy_eval_umbral = 0
                bert_eval_umbral = 0

            feedback = GenerateFeedback(self.settings, respuesta_alumno_raw,nota_rubrica_ortografia, nota_rubrica_sintaxis, spacy_eval_umbral * self.settings.PesoSemantics, bert_eval_umbral * self.settings.PesoSemantics)

            self.settings.minipreguntasMalSpacy = ""
            self.settings.minipreguntasMalBert = ""

                
            AnalysisOfResponses.append({ id : {
                                                "ID": studentID,
                                                "SumaTotalSpacy": round(nota_rubrica_spacy,2),
                                                "SumaTotalBert": round(nota_rubrica_bert,2),
                                                "NotaSemanticaSpacy": round(spacy_eval_umbral * self.settings.PesoSemantics,2),
                                                "NotaSemanticaBert": round(bert_eval_umbral * self.settings.PesoSemantics,2),
                                                "NotaSintaxis": round(nota_rubrica_sintaxis,2),
                                                "NotaOrtografia": round(nota_rubrica_ortografia,2),
                                                "NotaTotalSpacy": (round(nota_rubrica_ortografia,2) + round(nota_rubrica_sintaxis,2) + round(spacy_eval_umbral * self.settings.PesoSemantics,2))*10,
                                                "NotaTotalBert": (round(nota_rubrica_ortografia,2) + round(nota_rubrica_sintaxis,2) + round(bert_eval_umbral * self.settings.PesoSemantics,2))*10,
                                                "Feedback": feedback }
                                    } )      
        
        AnalysisOfResponses = self.__jsonToExcel__(AnalysisOfResponses)
        self.semantic_methodology.SemanticLevel.output.saveSimilarityResults(self.settings, "spacy")
        self.semantic_methodology.SemanticLevel.output.saveSimilarityResults(self.settings, "bert")
        if self.settings.Sintaxis:
            self.sintaxis.saveResults()
        if self.settings.Ortografia:
            self.ortografia.SaveMistakes()

        #print(AnalysisOfResponses)
        return AnalysisOfResponses