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| 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 funci贸n | |
| 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 funci贸n | |
| 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 | |