From a9754f46bc7f422eb1d3bf9d2d62b2f68afede61 Mon Sep 17 00:00:00 2001
From: Anton Akhmerov <anton.akhmerov@gmail.com>
Date: Mon, 26 Mar 2018 02:18:28 +0200
Subject: [PATCH] implement exam summary statistics image
---
zesje/api.py | 6 ++
zesje/helpers/db_helper.py | 73 ++++++++++++++++++++-
zesje/resources/summary_plot.py | 110 ++++++++++++++++++++++++++++++++
3 files changed, 187 insertions(+), 2 deletions(-)
create mode 100644 zesje/resources/summary_plot.py
diff --git a/zesje/api.py b/zesje/api.py
index 84b630313..a9e8ffc93 100644
--- a/zesje/api.py
+++ b/zesje/api.py
@@ -8,6 +8,7 @@ from .resources.students import Students
from .resources.submissions import Submissions
from .resources import signature
from .resources import images
+from .resources import summary_plot
from .resources.problems import Problems
from .resources.feedback import Feedback
@@ -47,3 +48,8 @@ api_bp.add_url_rule(
'solution',
images.get,
)
+api_bp.add_url_rule(
+ '/images/summary/<int:exam_id>',
+ 'exam_summary',
+ summary_plot.get,
+)
diff --git a/zesje/helpers/db_helper.py b/zesje/helpers/db_helper.py
index 30b970ac4..d22e59506 100644
--- a/zesje/helpers/db_helper.py
+++ b/zesje/helpers/db_helper.py
@@ -1,4 +1,8 @@
-from ..models import Problem
+import pandas
+from pony import orm
+from collections import namedtuple, OrderedDict, ChainMap
+
+from ..models import Exam, Problem, Student, Solution
from . import yaml_helper
def update_exam(exam, existing_yaml, new_yaml):
@@ -16,8 +20,73 @@ def update_exam(exam, existing_yaml, new_yaml):
new_problem_names = list(name for name in new_widgets.index
if name != 'studentnr')
-
problems = list(Problem.select(lambda p: p.exam == exam)
.order_by(lambda p: p.id))
for problem, name in zip(problems, new_problem_names):
problem.name = name
+
+
+def solution_data(exam_id, student_id):
+ """Return Python datastructures corresponding to the student submission."""
+ with orm.db_session:
+ exam = Exam[exam_id]
+ student = Student[student_id]
+ if any(i is None for i in (exam, student)):
+ raise RuntimeError('Student did not make a '
+ 'submission for this exam')
+
+ results = []
+ for problem in exam.problems.order_by(Problem.id):
+ if not orm.count(problem.solutions.feedback):
+ # Nobody received any grade for this problem
+ continue
+ problem_data = {
+ 'name': problem.name,
+ 'max_score': orm.max(problem.feedback_options.score, default=0)
+ }
+ solutions = Solution.select(lambda s: s.problem == problem
+ and s.submission.student == student)
+ problem_data['feedback'] = [
+ {'short': fo.text,
+ 'score': fo.score,
+ 'description': fo.description}
+ for solution in solutions for fo in solution.feedback
+ ]
+ problem_data['score'] = sum(i['score'] or 0
+ for i in problem_data['feedback'])
+ problem_data['remarks'] = '\n\n'.join(sol.remarks
+ for sol in solutions
+ if sol.remarks)
+ results.append(problem_data)
+
+ student = student.to_dict()
+
+ student['total'] = sum(i['score'] for i in results)
+ return student, results
+
+
+def full_exam_data(exam_id):
+ """Compute all grades of an exam as a pandas DataFrame."""
+ with orm.db_session:
+ students = sorted(Exam[exam_id].submissions.student.id)
+
+ data = [solution_data(exam_id, student_id)
+ for student_id in students]
+
+ students = pandas.DataFrame({i[0]['id']: i[0] for i in data}).T
+ del students['id']
+
+ results = {}
+ for result in data:
+ for problem in result[1]:
+ name = problem.pop('name')
+ problem[(name, 'remarks')] = problem.pop('remarks')
+ for fo in problem.pop('feedback'):
+ problem[(name, fo['short'])] = fo['score']
+ problem[(name, 'total')] = problem.pop('score')
+ problem.pop('max_score')
+ results[result[0]['id']] = dict(ChainMap({('total', 'total'):
+ result[0]['total']},
+ *result[1]))
+
+ return pandas.DataFrame(results).T
diff --git a/zesje/resources/summary_plot.py b/zesje/resources/summary_plot.py
new file mode 100644
index 000000000..b2c4f940d
--- /dev/null
+++ b/zesje/resources/summary_plot.py
@@ -0,0 +1,110 @@
+import os
+from io import BytesIO
+from flask import abort, Response, current_app as app
+from pony import orm
+
+import pandas
+import numpy as np
+
+from ..models import Exam, Submission
+from ..helpers.db_helper import full_exam_data
+
+import matplotlib
+matplotlib.use('agg')
+import seaborn
+from matplotlib import pyplot
+
+@orm.db_session
+def get(exam_id):
+ """Plot exam summary statistics.
+
+ Parameters
+ ----------
+ exam_id : int
+
+ Returns
+ -------
+ Image (JPEG mimetype)
+ """
+ try:
+ exam = Exam[exam_id]
+ except KeyError:
+ abort(404)
+
+ scores = {problem.name: max(list(problem.feedback_options.score) + [0])
+ for problem in exam.problems}
+ scores['total'] = sum(scores.values())
+
+ full_scores = full_exam_data(exam_id)
+ # Full exam data has multilevel columns (includes detailed feedback), we
+ # flatten them out first.
+ problem_scores = full_scores.iloc[
+ :, full_scores.columns.get_level_values(1) == 'total'
+ ]
+ problem_scores.columns = problem_scores.columns.get_level_values(0)
+ # Exclude empty columns from statistics
+ problem_scores = problem_scores.loc[:, ~(problem_scores == 0).all()]
+
+ seaborn.set()
+ seaborn.set_context("notebook", font_scale=1.5, rc={"lines.linewidth": 2.5})
+
+ cm = matplotlib.cm.magma
+ # define the bins and normalize
+ bounds = np.linspace(0, 1, 21)
+ norm = matplotlib.colors.BoundaryNorm(bounds, cm.N)
+
+
+ maxes = pandas.DataFrame(problem_scores.max())
+ maxes['max_rubric'] = maxes.index
+ maxes = maxes.replace({'max_rubric': scores}).max(axis=1)
+
+ corrs = {column: (problem_scores[column]
+ .astype(float)
+ .corr(problem_scores
+ .total
+ .subtract(problem_scores[column])
+ .astype(float)
+ ).round(2)
+ ) for column in problem_scores if column != 'total'}
+
+ alpha = ((len(problem_scores) - 1) / (len(problem_scores) - 2)
+ * (1 - problem_scores.var()[:-1].sum()
+ / problem_scores.total.var())
+ )
+
+ vals = [
+ problem_scores[i].value_counts(normalize=True).sort_index().cumsum()
+ for i in problem_scores
+ ]
+ data = np.array(
+ [
+ (-i, upper-lower, lower, num/maxes.ix[i])
+ for i, val in enumerate(vals)
+ for num, upper, lower in zip(
+ val.index, val.data, [0] + list(val.data[:-1])
+ )
+ ]
+ ).T
+ fig = pyplot.figure(figsize=(12, 9))
+ ax = fig.add_subplot(1, 1, 1)
+ ax.barh(
+ data[0], data[1], 0.5, data[2], color=cm(norm(data[3])), align='center'
+ )
+ ax.set_yticks(np.arange(0, -len(problem_scores.columns), -1));
+ ax.set_yticklabels(
+ [f'{i} ($Rir={corrs[i]:.2f}$)' for i in problem_scores.columns[:-1]]
+ + [f'total: ($\\alpha = {alpha:.2f}$)']
+ )
+ ax.set_xlabel('fraction of students')
+ ax.set_xlim(-0.025, 1.025)
+ sm = matplotlib.cm.ScalarMappable(cmap=cm, norm=norm)
+ sm._A = []
+ colorbar = fig.colorbar(sm)
+ colorbar.set_ticks(np.linspace(0, 1, 11))
+ colorbar.set_label('score percentage')
+
+ pyplot.tight_layout()
+ image = BytesIO()
+ pyplot.savefig(image)
+
+ return Response(image.getvalue(), 200, mimetype='image/jpeg')
--
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