i am using python 3.8.5 with jupyter notebook
spacy = 3.0.5
neuralcoref = 4.0
below is the code i running for testing
import datetime
import re
import time
import pandas as pd
from formative_assessment.dataset_extractor import ConvertDataType
from formative_assessment.feature_extractor import FeatureExtractor
class AEGrading:
"""
Automatically evaluates, grades and provides feedback to students' answers of the datasets.
Provides feedback as dict including total data of the student answer.
"""
def __init__(self, qid, stu_answer, dataset, dataset_path, score=5):
self.qid = qid
self.stu_answer = stu_answer
self.dataset = dataset
self.length_ratio = len(stu_answer) / len(dataset[qid]["desired_answer"])
self.score = score
self.fe = FeatureExtractor(qid, stu_answer, dataset, dataset_path)
self.wrong_terms = {}
self.feedback = {"id": self.qid, "question": self.dataset[self.qid]["question"],
"desired_answer": self.dataset[self.qid]["desired_answer"], "student_answer": stu_answer,
"length_ratio": self.length_ratio, "is_answered": "-", "is_wrong_answer": "not wrong answer",
"interchanged": "-", "missed_topics": "-", "missed_terms": "-", "irrelevant_terms": "-",
"score_avg": 0, "our_score": 0}
def is_answered(self, default="not answered"):
"""
Checks if the student answered or not given the default evaluator's string. Assigns score to 'zero' if not
answered.
:param default: str
String to be checked if student not answered
:return: bool
True if student answered, else False
"""
re_string = " *" + default + " *"
if re.match(re_string, self.stu_answer.lower()):
self.feedback["is_answered"] = "not answered"
self.score = 0
return False
else:
self.feedback["is_answered"] = "answered"
return True
def iot_score(self):
"""
Checks if there are any interchange of topics or missed topics and deduce the score accordingly. Deduce
nothing from the score if there are no interchange of topics or missed topics
:return: None
"""
iot = self.fe.get_interchanged_topics()
interchanged = iot["interchanged"]
missed_topics = iot["missed_topics"]
total_relations = iot["total_relations"]
topics_num = iot["total_topics"]
self.feedback["interchanged"] = interchanged
self.feedback["missed_topics"] = missed_topics
if interchanged:
iot_deduce = len(interchanged) / total_relations
self.score = self.score - (iot_deduce * self.score)
if missed_topics:
missed_deduce = len(missed_topics) / topics_num
self.score = self.score - (missed_deduce * self.score)
def missed_terms_score(self):
"""
Checks if there are any missed terms in the student answer and deduce score accordingly
:return: None
"""
missed_terms = self.fe.get_missed_terms()
self.feedback["missed_terms"] = missed_terms.keys()
total = round(sum(missed_terms.values()), 3)
self.score = self.score - (total * self.score) # self.score/2
def irrelevant_terms_score(self):
"""
Checks if there are any irrelevant terms in the student answer. We do not deduce score for this feature, as
we consider any irrelevant term as noise.
:return: None
"""
self.feedback["irrelevant_terms"] = self.fe.get_irrelevant_terms()
if __name__ == '__main__':
PATH = "dataset/mohler/cleaned/"
max_score = 5
# Convert the data into dictionary with ids, their corresponding questions, desired answers and student answers
convert_data = ConvertDataType(PATH)
dataset_dict = convert_data.to_dict()
id_list = list(dataset_dict.keys())
data = []
# random.seed(20)
for s_no in id_list[:7]:
# s_no = random.choice(id_list)
question = dataset_dict[s_no]["question"]
desired_answer = dataset_dict[s_no]["desired_answer"]
student_answers = dataset_dict[s_no]["student_answers"]
scores = dataset_dict[s_no]["scores"]
# score_me = dataset_dict[s_no]["score_me"]
# score_other = dataset_dict[s_no]["score_other"]
for index, _ in enumerate(student_answers):
# index = random.randint(0, 12)
start = time.time()
student_answer = str(student_answers[index])
print(s_no, student_answer)
aeg = AEGrading(s_no, student_answer, dataset_dict, PATH, max_score)
if aeg.is_answered():
aeg.iot_score()
aeg.missed_terms_score()
aeg.irrelevant_terms_score()
if aeg.score == 0:
aeg.feedback["is_wrong_answer"] = "wrong_answer"
# aeg.feedback["score_me"] = score_me[index] # Only for mohler data
# aeg.feedback["score_other"] = score_other[index]
aeg.feedback["score_avg"] = scores[index]
aeg.feedback["our_score"] = round((aeg.score * 4)) / 4 # Score in multiples of 0.25
data.append(aeg.feedback)
print(aeg.feedback)
print("It took ", time.time() - start, " secs")
print("----------------------------------------------------------")
if len(data) % 50 == 0:
df = pd.DataFrame(data)
SAVE_PATH = "outputs/automatic_evaluation/II_NN/" + str(datetime.datetime.now()) + ".csv"
df.to_csv(SAVE_PATH, sep=",")
df = pd.DataFrame(data)
SAVE_PATH = "outputs/automatic_evaluation/II_NN/" + str(datetime.datetime.now()) + ".csv"
df.to_csv(SAVE_PATH, sep=",")
after i running above code i get the error as below
ValueError Traceback (most recent call last) in 9 import pandas as pd 10 ---> 11 from formative_assessment.dataset_extractor import ConvertDataType 12 from formative_assessment.feature_extractor import FeatureExtractor 13
~\Desktop\FYP\Automatic-Formative-Assessment-main\formative_assessment\dataset_extractor.py in 8 import pandas as pd 9 ---> 10 from formative_assessment.utilities.utils import Utilities 11
~\Desktop\FYP\Automatic-Formative-Assessment-main\formative_assessment\utilities\utils.py in 11 from typing import List 12 ---> 13 import neuralcoref 14 import numpy as np 15 import pytextrank
~\anaconda3\lib\site-packages\neuralcoref_init_.py in 12 warnings.filterwarnings("ignore", message="spacy.strings.StringStore size changed,") 13 ---> 14 from .neuralcoref import NeuralCoref 15 from .file_utils import NEURALCOREF_MODEL_URL, NEURALCOREF_MODEL_PATH, NEURALCOREF_CACHE, cached_path 16
strings.pxd in init neuralcoref.neuralcoref()
ValueError: spacy.strings.StringStore size changed, may indicate binary incompatibility. Expected 80 from C header, got 64 from PyObject
**i had try to uinstall the follwing method
pip uninstall neuralcoref
pip install neuralcoref --no-binary neuralcoref
but the problem still the same, hope someone can help me, very appreciate..**
