Update RuleList

Author: zStupan

niaarm/rule_list.py

@@ -5,33 +5,26 @@
 
 
 class RuleList(UserList):
-    """A wrapper around a list of rules.
-
-    Attributes:
-        mean_fitness (float): Mean fitness.
-        mean_support (float): Mean support.
-        mean_confidence (float): Mean confidence.
-        mean_lift (float): Mean lift.
-        mean_coverage (float): Mean coverage.
-        mean_rhs_support (float): Mean consequent support.
-        mean_conviction (float): Mean conviction.
-        mean_inclusion (float): Mean inclusion.
-        mean_amplitude (float): Mean amplitude.
-        mean_interestingness (float): Mean interestingness.
-        mean_comprehensibility (float): Mean comprehensibility.
-        mean_netconf (float): Mean netconf.
-        mean_yulesq (float): Mean Yule's Q.
-        mean_antecedent_length (float): Mean antecedent length.
-        mean_consequent_length (float): Mean consequent length.
-
-    """
+    """A list of rules."""
+
+    def get(self, metric):
+        """Get values of `metric` for each rule as a numpy array.
+
+        Args:
+            metric (str): Metric.
+
+        Returns:
+            numpy.ndarray: Array of `metric` for all rules.
+
+        """
+        return np.array([getattr(rule, metric) for rule in self.data])
 
     def sort(self, by='fitness', reverse=True):
         """Sort rules by metric.
 
         Args:
             by (str): Metric to sort rules by. Default: ``'fitness'``.
-            reverse (bool): Sort in descending order. Default: ``True``
+            reverse (bool): Sort in descending order. Default: ``True``.
 
         """
         self.data.sort(key=lambda rule: getattr(rule, by), reverse=reverse)
@@ -46,7 +39,7 @@ def mean(self, metric):
             float: Mean value of metric in rule list.
 
         """
-        return np.mean([getattr(rule, metric) for rule in self.data])
+        return sum(getattr(rule, metric) for rule in self.data) / len(self.data)
 
     def min(self, metric):
         """Get min value of metric.
@@ -97,87 +90,25 @@ def to_csv(self, filename):
             # write header
             writer.writerow(("antecedent", "consequent", "fitness") + Rule.metrics)
 
-            for rule in self:
+            for rule in self.data:
                 writer.writerow(
                     [rule.antecedent, rule.consequent, rule.fitness] + [getattr(rule, metric) for metric in Rule.metrics])
         print(f"Rules exported to {filename}")
 
-    @property
-    def mean_fitness(self):
-        return np.mean([rule.fitness for rule in self.data])
-
-    @property
-    def mean_support(self):
-        return np.mean([rule.support for rule in self.data])
-
-    @property
-    def mean_confidence(self):
-        return np.mean([rule.confidence for rule in self.data])
-
-    @property
-    def mean_lift(self):
-        return np.mean([rule.lift for rule in self.data])
-
-    @property
-    def mean_coverage(self):
-        return np.mean([rule.coverage for rule in self.data])
-
-    @property
-    def mean_rhs_support(self):
-        return np.mean([rule.rhs_support for rule in self.data])
-
-    @property
-    def mean_conviction(self):
-        return np.mean([rule.conviction for rule in self.data])
-
-    @property
-    def mean_inclusion(self):
-        return np.mean([rule.inclusion for rule in self.data])
-
-    @property
-    def mean_amplitude(self):
-        return np.mean([rule.amplitude for rule in self.data])
-
-    @property
-    def mean_interestingness(self):
-        return np.mean([rule.interestingness for rule in self.data])
-
-    @property
-    def mean_comprehensibility(self):
-        return np.mean([rule.comprehensibility for rule in self.data])
-
-    @property
-    def mean_netconf(self):
-        return np.mean([rule.netconf for rule in self.data])
-
-    @property
-    def mean_yulesq(self):
-        return np.mean([rule.yulesq for rule in self.data])
-
-    @property
-    def mean_antecedent_length(self):
-        return np.mean([len(rule.antecedent) for rule in self.data])
-
-    @property
-    def mean_consequent_length(self):
-        return np.mean([len(rule.consequent) for rule in self.data])
-
     def __str__(self):
         string = f'STATS:\n' \
                  f'Total rules: {len(self)}\n' \
-                 f'Average fitness: {self.mean_fitness}\n' \
-                 f'Average support: {self.mean_support}\n' \
-                 f'Average confidence: {self.mean_confidence}\n' \
-                 f'Average lift: {self.mean_lift}\n' \
-                 f'Average coverage: {self.mean_coverage}\n' \
-                 f'Average consequent support: {self.mean_rhs_support}\n' \
-                 f'Average conviction: {self.mean_conviction}\n' \
-                 f'Average amplitude: {self.mean_amplitude}\n' \
-                 f'Average inclusion: {self.mean_inclusion}\n' \
-                 f'Average interestingness: {self.mean_interestingness}\n' \
-                 f'Average comprehensibility: {self.mean_comprehensibility}\n' \
-                 f'Average netconf: {self.mean_netconf}\n' \
-                 f'Average Yule\'s Q: {self.mean_yulesq}\n' \
-                 f'Average length of antecedent: {self.mean_antecedent_length}\n' \
-                 f'Average length of consequent: {self.mean_consequent_length}'
+                 f'Average fitness: {self.mean("fitness")}\n' \
+                 f'Average support: {self.mean("support")}\n' \
+                 f'Average confidence: {self.mean("confidence")}\n' \
+                 f'Average lift: {self.mean("lift")}\n' \
+                 f'Average coverage: {self.mean("coverage")}\n' \
+                 f'Average consequent support: {self.mean("rhs_support")}\n' \
+                 f'Average conviction: {self.mean("conviction")}\n' \
+                 f'Average amplitude: {self.mean("amplitude")}\n' \
+                 f'Average inclusion: {self.mean("inclusion")}\n' \
+                 f'Average interestingness: {self.mean("interestingness")}\n' \
+                 f'Average comprehensibility: {self.mean("comprehensibility")}\n' \
+                 f'Average netconf: {self.mean("netconf")}\n' \
+                 f'Average Yule\'s Q: {self.mean("yulesq")}\n'
         return string