From cc41068e6b4566633ebc7a841290ce167671d2bf Mon Sep 17 00:00:00 2001
From: Timotej Lazar <timotej.lazar@fri.uni-lj.si>
Date: Tue, 18 Apr 2017 17:25:18 +0200
Subject: Store student submissions in files instead of .pickle

---
 abml/evaluate.py | 22 ++++++++++------------
 1 file changed, 10 insertions(+), 12 deletions(-)

(limited to 'abml/evaluate.py')

diff --git a/abml/evaluate.py b/abml/evaluate.py
index 2e318fb..cb959ed 100644
--- a/abml/evaluate.py
+++ b/abml/evaluate.py
@@ -1,4 +1,4 @@
-import pickle
+import os.path
 import argparse
 import Orange
 from Orange.evaluation import TestOnTestData, CA, AUC, LogLoss
@@ -8,30 +8,28 @@ import orangecontrib.evcrules.logistic as logistic
 import orangecontrib.abml.abrules as rules
 import orangecontrib.abml.argumentation as arg
 
-parser = argparse.ArgumentParser(description='Learn and test rules for prolog programs.')
-parser.add_argument('Name', type=str, help='Predicate name.')
+parser = argparse.ArgumentParser(description='Learn and evaluate rules for Prolog programs.')
+parser.add_argument('path', help='path to data directory')
 args = parser.parse_args()
-name = args.Name
+path = args.path
 
 # load data
-data = Orange.data.Table('data/{}/programs-train'.format(name))
+data = Orange.data.Table(os.path.join(path, 'programs-train'))
 
 # create learner
-rule_learner = rp.Rules4Prolog(name, 0.9)
-
-
+rule_learner = rp.Rules4Prolog(path, 0.9)
 
 # learn a classifier
 classifier = rule_learner(data)
 
 # save model
-fmodel = open("data/{}/model.txt".format(name), "wt")
+fmodel = open(os.path.join(path, 'model.txt'), "wt")
 for r in classifier.rule_list:
     print(r, r.curr_class_dist, r.quality)
     fmodel.write("{} dist={} quality={}\n".format(str(r), str(r.curr_class_dist), r.quality))
 
 # accuracy of model
-testdata = Orange.data.Table('data/{}/programs-test'.format(name))
+testdata = Orange.data.Table(os.path.join(path, 'programs-test'))
 predictions = classifier(testdata)
 acc = 0
 for i, p in enumerate(predictions):
@@ -64,12 +62,12 @@ scores += "CA\tAUC\tLogLoss\tMethod\n"
 for ni, n in enumerate(names):
     scores += "{}\t{}\t{}\t{}\n".format(ca[ni], auc[ni], ll[ni], n)
 print(scores)
-fscores = open("data/{}/scores.txt".format(name), "wt")
+fscores = open(os.path.join(path, 'scores.txt'), 'wt')
 fscores.write(scores)
 
 all_rules = classifier.rule_list
 all_rules.sort(key = lambda r: r.quality, reverse=True)
-rfile = open("data/{}/rules.txt".format(name), "wt")
+rfile = open(os.path.join(path, 'rules.txt'), 'wt')
 for r in all_rules:
     print(r, r.curr_class_dist, r.quality)
     rfile.write("{} {} {}\n".format(r, r.curr_class_dist, r.quality))
-- 
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