Merge pull request #4 from floriangardin/master

Any variable name can be used in "feature_names"

Author: datajms

skrules/__init__.py

@@ -1,4 +1,4 @@
 from .skope_rules import SkopeRules
-from .rule import Rule
+from .rule import Rule, replace_feature_name
 
 __all__ = ['SkopeRules', 'Rule']

skrules/rule.py

@@ -1,3 +1,13 @@
+import re
+
+def replace_feature_name(rule, replace_dict):
+    def replace(match):
+        return replace_dict[match.group(0)]
+
+    rule = re.sub('|'.join(r'\b%s\b' % re.escape(s) for s in replace_dict),
+           replace, rule)
+    return rule
+
 class Rule:
     """ An object modelizing a logical rule and add factorization methods.
     It is used to simplify rules and deduplicate them.
@@ -56,3 +66,4 @@ def __repr__(self):
                 [feature, symbol, str(self.agg_dict[(feature, symbol)])])
                 for feature, symbol in sorted(self.agg_dict.keys())
                 ])
+

skrules/skope_rules.py

@@ -12,10 +12,10 @@
 from sklearn.externals import six
 from sklearn.tree import _tree
 
-from .rule import Rule
+from .rule import Rule, replace_feature_name
 
 INTEGER_TYPES = (numbers.Integral, np.integer)
-
+BASE_FEATURE_NAME = "__C__"
 
 class SkopeRules(BaseEstimator):
     """ An easy-interpretable classifier optimizing simple logical rules.
@@ -249,11 +249,17 @@ def fit(self, X, y, sample_weight=None):
         self.estimators_samples_ = []
         self.estimators_features_ = []
 
-        # default columns names of the form ['c0', 'c1', ...]:
-        feature_names_ = (self.feature_names if self.feature_names is not None
-                          else ['c' + x for x in
-                                np.arange(X.shape[1]).astype(str)])
+        # default columns names :
+        feature_names_ = [BASE_FEATURE_NAME + x for x in
+                            np.arange(X.shape[1]).astype(str)]
+        if self.feature_names is not None:
+            self.feature_dict_ = {BASE_FEATURE_NAME + str(i): feat
+                                  for i, feat in enumerate(self.feature_names)}
+        else:
+            self.feature_dict_ = {BASE_FEATURE_NAME + str(i): feat
+                                  for i, feat in enumerate(feature_names_)}
         self.feature_names_ = feature_names_
+
         clfs = []
         regs = []
 
@@ -356,6 +362,10 @@ def fit(self, X, y, sample_weight=None):
             for rule in
             [Rule(r, args=args) for r, args in rules_]]
 
+
+
+
+
         # keep only rules verifying precision_min and recall_min:
         for rule, score in rules_:
             if score[0] >= self.precision_min and score[1] >= self.recall_min:
@@ -377,7 +387,14 @@ def fit(self, X, y, sample_weight=None):
         # Deduplicate the rule using semantic tree
         if self.max_depth_duplication is not None:
             self.rules_ = self.deduplicate(self.rules_)
+
         self.rules_ = sorted(self.rules_, key=lambda x: - self.f1_score(x))
+        self.rules_without_feature_names_ = self.rules_
+
+        # Replace generic feature names by real feature names
+        self.rules_ = [(replace_feature_name(rule, self.feature_dict_), perf)
+                  for rule, perf in self.rules_]
+
         return self
 
     def predict(self, X):
@@ -432,7 +449,7 @@ def decision_function(self, X):
                              % (X.shape[1], self.n_features_))
 
         df = pandas.DataFrame(X, columns=self.feature_names_)
-        selected_rules = self.rules_
+        selected_rules = self.rules_without_feature_names_
 
         scores = np.zeros(X.shape[0])
         for (r, w) in selected_rules:

skrules/tests/test_rule.py

@@ -1,6 +1,6 @@
 from sklearn.utils.testing import assert_equal, assert_not_equal
 
-from skrules import Rule
+from skrules import Rule, replace_feature_name
 
 
 def test_rule():
@@ -53,3 +53,13 @@ def test_equals_rule():
 
     rule3 = "a < 3.0 and a == a"
     assert_equal(rule3, str(Rule(rule3)))
+
+
+def test_replace_feature_name():
+    rule = "__C__0 <= 3 and __C__1 > 4"
+    real_rule = "$b <= 3 and c(4) > 4"
+    replace_dict = {
+                    "__C__0": "$b",
+                    "__C__1": "c(4)"
+                    }
+    assert_equal(replace_feature_name(rule, replace_dict=replace_dict), real_rule)