Store student submissions in files instead of .pickle

3 files changed, 24 insertions, 23 deletions

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))
diff --git a/abml/learn_dist.py b/abml/learn_dist.py
index 58e4968..44e19c3 100644
--- a/abml/learn_dist.py
+++ b/abml/learn_dist.py
@@ -1,15 +1,16 @@
+import os.path
 import pickle
 import argparse
 from Orange.data import Table
 import abml.rules_prolog as rp
 
 parser = argparse.ArgumentParser(description='Learn and test rules for prolog programs.')
-parser.add_argument('Name', type=str, help='Predicate name.')
+parser.add_argument('path', help='path to data directory')
 args = parser.parse_args()
-name = args.Name
+path = args.path
 
-data = Table('data/{}/programs-train'.format(name))
+data = Table(os.path.join(path, 'programs-train'))
 
-rule_learner = rp.create_learner(name, evds=False)
+rule_learner = rp.create_learner(path, evds=False)
 rule_learner.calculate_evds(data)
-pickle.dump(rule_learner.evds, open("data/{}/evds.pickle".format(name), "wb"))
+pickle.dump(rule_learner.evds, open(os.path.join(path, 'evds.pickle'), "wb"))
diff --git a/abml/rules_prolog.py b/abml/rules_prolog.py
index 9edd674..f67ba93 100644
--- a/abml/rules_prolog.py
+++ b/abml/rules_prolog.py
@@ -1,6 +1,8 @@
-import numpy as np
-import pickle
 import itertools
+import os.path
+import pickle
+
+import numpy as np
 from Orange.classification.rules import _RuleClassifier, GuardianValidator
 import orangecontrib.abml.abrules as rules
 from Orange.classification.rules import Rule
@@ -92,14 +94,14 @@ class NegativeFirstClassifier(_RuleClassifier):
         return probabilities
 
 class Rules4Prolog:
-    def __init__(self, name, threshold):
+    def __init__(self, path, threshold):
         self.threshold = threshold
         self.learner = rules.ABRuleLearner(width=50, parent_alpha=0.05)
         self.learner.rule_finder.general_validator = TrueCondValidator(self.learner.rule_finder.general_validator.max_rule_length,
                                                                        self.learner.rule_finder.general_validator.min_covered_examples)
         self.learner.rule_validator = PureAccuracyValidator(0, self.threshold)
         self.learner.classifier = NegativeFirstClassifier
-        self.learner.evds = pickle.load(open("data/{}/evds.pickle".format(name), "rb"))
+        self.learner.evds = pickle.load(open(os.path.join(path, 'evds.pickle'), 'rb'))
 
     def __call__(self, data):
         # first learn rules for negative class (quality should be higher than
@@ -161,13 +163,13 @@ class Rules4Prolog:
 
 
 
-def create_learner(name, evds=True):
+def create_learner(path, evds=True):
     rule_learner = rules.ABRuleLearner(width=50, parent_alpha=0.05)
     rule_learner.rule_finder.general_validator = TrueCondValidator(rule_learner.rule_finder.general_validator.max_rule_length,
                                                                    rule_learner.rule_finder.general_validator.min_covered_examples)
     rule_learner.rule_validator = PureAccuracyValidator(0, 0.8)
     rule_learner.classifier = NegativeFirstClassifier
     if evds:
-        rule_learner.evds = pickle.load(open("data/{}/evds.pickle".format(name), "rb"))
+        rule_learner.evds = pickle.load(open(os.path.join(path, 'evds.pickle'), 'rb'))
     return rule_learner