From 651e2be4480b19ac486cb8a4dd2fb08b448ebc67 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Martin=20Mo=C5=BEina?= <martin.mozina@fri.uni-lj.si>
Date: Tue, 17 Jan 2017 20:12:29 +0100
Subject: Added scripts for learning rules.

---
 abml/evaluate.py | 75 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 75 insertions(+)
 create mode 100644 abml/evaluate.py

(limited to 'abml/evaluate.py')

diff --git a/abml/evaluate.py b/abml/evaluate.py
new file mode 100644
index 0000000..2e318fb
--- /dev/null
+++ b/abml/evaluate.py
@@ -0,0 +1,75 @@
+import pickle
+import argparse
+import Orange
+from Orange.evaluation import TestOnTestData, CA, AUC, LogLoss
+import abml.rules_prolog as rp
+
+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.')
+args = parser.parse_args()
+name = args.Name
+
+# load data
+data = Orange.data.Table('data/{}/programs-train'.format(name))
+
+# create learner
+rule_learner = rp.Rules4Prolog(name, 0.9)
+
+
+
+# learn a classifier
+classifier = rule_learner(data)
+
+# save model
+fmodel = open("data/{}/model.txt".format(name), "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))
+predictions = classifier(testdata)
+acc = 0
+for i, p in enumerate(predictions):
+    acc += p == testdata.Y[i]
+acc /= len(testdata)
+print("Accuracy on test data: ", acc)
+predictions = classifier(data)
+acc = 0
+for i, p in enumerate(predictions):
+    acc += p == data.Y[i]
+acc /= len(data)
+print("Accuracy on train data: ", acc)
+
+# test model + other methodsstrong_piece_attack	defends_around_king
+bayes = Orange.classification.NaiveBayesLearner()
+logistic = Orange.classification.LogisticRegressionLearner()
+tree = Orange.classification.TreeLearner()
+random_forest = Orange.classification.RandomForestLearner()
+svm = Orange.classification.SVMLearner()
+cn2 = Orange.classification.rules.CN2UnorderedLearner()
+learners = [rule_learner, logistic, bayes, cn2, tree, random_forest, svm]
+res = TestOnTestData(data, testdata, learners)
+ca = CA(res)
+auc = AUC(res)
+ll = LogLoss(res)
+
+names = ['logrules', 'logistic', 'naive-bayes', 'cn2', 'tree', 'random-forest', 'svm']
+scores = ""
+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.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")
+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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