Quick fix (namespace, nanmax, vspace).

Author: matevzkren

Orange/classification/rules.py

@@ -5,6 +5,7 @@
 
 import numpy as np
 from scipy.stats import chisqprob
+import bottlechest as bn
 import Orange
 from Orange.classification import Learner, Model
 
@@ -48,7 +49,8 @@ def argmaxrnd(a, random_seed=None):
     random = (np.random if random_seed is None
               else np.random.RandomState(random_seed))
 
-    def f(x): return random.choice((x == np.nanmax(x)).nonzero()[0])
+    def f(x): return random.choice((x == bn.nanmax(x)).nonzero()[0])
+
     return f(a) if a.ndim == 1 else np.apply_along_axis(f, axis=1, arr=a)
 
 
@@ -727,21 +729,24 @@ def __init__(self, preprocessors=None, base_rules=None):
 
     @abstractmethod
     def fit(self, X, Y, W=None):
+        row_defined = ~np.isnan(X).any(axis=1)
+        X = X[row_defined]
+        Y = Y[row_defined]
         rule_list = self.find_rules(X, Y, None, self.base_rules, self.domain)
         return RuleClassifier(domain=self.domain, rule_list=rule_list)
 
     def find_rules(self, X, Y, target_class, base_rules, domain):
         rule_list = []
         while not self.data_stopping(X, Y, target_class):
             new_rule = self.rule_finder(X, Y, target_class, base_rules, domain)
-            if self.rule_stopping(X, Y, new_rule):
+            if self.rule_stopping(new_rule):
                 break
             X, Y = self.cover_and_remove(X, Y, new_rule)
             rule_list.append(new_rule)
         return rule_list
 
     @staticmethod
-    def rule_stopping(X, Y, new_rule):
+    def rule_stopping(new_rule, alpha=1.0):
         return False
 
     @staticmethod
@@ -793,6 +798,7 @@ class CN2Learner(RuleLearner):
     .. [1] "The CN2 Induction Algorithm", Peter Clark and Tim Niblett,
            Machine Learning Journal, 3 (4), pp261-283, (1989)
     """
+    name = 'CN2 inducer'
 
     def __init__(self, preprocessors=None, base_rules=None):
         super().__init__(preprocessors, base_rules)
@@ -849,6 +855,8 @@ class CN2UnorderedLearner(RuleLearner):
            Clark and Robin Boswell, Machine Learning - Proceedings of
            the 5th European Conference (EWSL-91), pp151-163, 1991
     """
+    name = 'CN2 Unordered inducer'
+
     def __init__(self, preprocessors=None, base_rules=None):
         super().__init__(preprocessors, base_rules)
         self.rule_finder.search_algorithm.beam_width = 10
@@ -924,13 +932,14 @@ def main():
     classifier = learner(data)
     for rule in classifier.rule_list:
         print(rule.curr_class_dist.tolist(), rule)
+    print(len(classifier.rule_list))
 
     print()
 
-    learner = CN2UnorderedLearner()
-    classifier = learner(data)
-    for rule in classifier.rule_list:
-        print(rule, rule.curr_class_dist.tolist())
+    # learner = CN2UnorderedLearner()
+    # classifier = learner(data)
+    # for rule in classifier.rule_list:
+    #     print(rule, rule.curr_class_dist.tolist())
 
 if __name__ == "__main__":
     main()