Commit f5df42e6 by Vik Paruchuri

Add logging to grade

parent b19ce60c
...@@ -9,6 +9,7 @@ import sys ...@@ -9,6 +9,7 @@ import sys
import pickle import pickle
import os import os
import numpy import numpy
import logging
base_path = os.path.dirname(__file__) base_path = os.path.dirname(__file__)
sys.path.append(base_path) sys.path.append(base_path)
...@@ -19,7 +20,10 @@ from essay_set import EssaySet ...@@ -19,7 +20,10 @@ from essay_set import EssaySet
import feature_extractor import feature_extractor
import sklearn.ensemble import sklearn.ensemble
log = logging.getLogger(__name__)
def grade(grader_path,submission,sandbox=None): def grade(grader_path,submission,sandbox=None):
log.debug("Grader path: {0}\n Submission: {1}".format(grader_path,submission))
results = {'errors': [],'tests': [],'correct': False,'score': 0, 'feedback' : []} results = {'errors': [],'tests': [],'correct': False,'score': 0, 'feedback' : []}
#Try to find and load the model file #Try to find and load the model file
...@@ -42,7 +46,7 @@ def grade(grader_path,submission,sandbox=None): ...@@ -42,7 +46,7 @@ def grade(grader_path,submission,sandbox=None):
grader_feats=grader_data['extractor'].gen_feats(grader_set) grader_feats=grader_data['extractor'].gen_feats(grader_set)
results['feedback']=grader_data['extractor'].gen_feedback(grader_set) results['feedback']=grader_data['extractor'].gen_feedback(grader_set)
results['score']=int(grader_data['model'].predict(grader_feats)[0]) results['score']=int(grader_data['model'].predict(grader_feats)[0])
except: except :
results['errors'].append("Could not extract features and score essay.") results['errors'].append("Could not extract features and score essay.")
#Determine maximum score and correctness of response #Determine maximum score and correctness of response
......
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