limit pandas version to 2.1.4 (issues with query function)

Author: csinva

imodels/rule_set/rule_fit.py

@@ -113,7 +113,8 @@ def fit(self, X, y=None, feature_names=None):
         self.feature_names = np.array(list(self.feature_dict_.values()))
 
         extracted_rules = self._extract_rules(X, y)
-        self.rules_without_feature_names_, self.coef, self.intercept = self._score_rules(X, y, extracted_rules)
+        self.rules_without_feature_names_, self.coef, self.intercept = self._score_rules(
+            X, y, extracted_rules)
         self.rules_ = [
             replace_feature_name(rule, self.feature_dict_) for rule in self.rules_without_feature_names_
         ]
@@ -160,7 +161,8 @@ def predict_proba(self, X):
             X = X.toarray()
         X = check_array(X)
         continuous_output = self._predict_continuous_output(X)
-        logits = np.vstack((1 - continuous_output, continuous_output)).transpose()
+        logits = np.vstack(
+            (1 - continuous_output, continuous_output)).transpose()
         return softmax(logits, axis=1)
 
     def transform(self, X=None, rules=None):
@@ -178,9 +180,15 @@ def transform(self, X=None, rules=None):
             Transformed data set
         """
         df = pd.DataFrame(X, columns=self.feature_placeholders)
+        print('df', df.dtypes, df.head())
         X_transformed = np.zeros((X.shape[0], len(rules)))
         for i, r in enumerate(rules):
             features_r_uses = [term.split(' ')[0] for term in r.split(' and ')]
+            # print('r', r)
+            # print('feats', df[features_r_uses])
+            # print('ans', df[features_r_uses].query(r))
+            # print(
+            #     'tra', X_transformed[df[features_r_uses].query(r).index.values, i])
             X_transformed[df[features_r_uses].query(r).index.values, i] = 1
         return X_transformed
 
@@ -216,21 +224,26 @@ def _get_rules(self, exclude_zero_coef=False, subregion=None):
                 subregion = np.array(subregion)
                 importance = sum(abs(coef) * abs([x[i] for x in self.winsorizer.trim(subregion)] - self.mean[i])) / len(
                     subregion)
-            output_rules += [(self.feature_names[i], 'linear', coef, 1, importance)]
+            output_rules += [(self.feature_names[i],
+                              'linear', coef, 1, importance)]
 
         # Add rules
         for i in range(0, len(self.rules_)):
             rule = rule_ensemble[i]
             coef = self.coef[i + n_features]
 
             if subregion is None:
-                importance = abs(coef) * (rule.support * (1 - rule.support)) ** (1 / 2)
+                importance = abs(coef) * (rule.support *
+                                          (1 - rule.support)) ** (1 / 2)
             else:
                 rkx = self.transform(subregion, [rule])[:, -1]
-                importance = sum(abs(coef) * abs(rkx - rule.support)) / len(subregion)
+                importance = sum(
+                    abs(coef) * abs(rkx - rule.support)) / len(subregion)
 
-            output_rules += [(self.rules_[i].rule, 'rule', coef, rule.support, importance)]
-        rules = pd.DataFrame(output_rules, columns=["rule", "type", "coef", "support", "importance"])
+            output_rules += [(self.rules_[i].rule, 'rule',
+                              coef, rule.support, importance)]
+        rules = pd.DataFrame(output_rules, columns=[
+                             "rule", "type", "coef", "support", "importance"])
         if exclude_zero_coef:
             rules = rules.ix[rules.coef != 0]
         return rules
@@ -292,7 +305,8 @@ def _score_rules(self, X, y, rules) -> Tuple[List[Rule], List[float], float]:
         # no rules fit and self.include_linear == False
         if X_concat.shape[1] == 0:
             return [], [], 0
-        prediction_task = 'regression' if isinstance(self, RegressorMixin) else 'classification'
+        prediction_task = 'regression' if isinstance(
+            self, RegressorMixin) else 'classification'
         return score_linear(X_concat, y, rules,
                             prediction_task=prediction_task,
                             max_rules=self.max_rules,

setup.py

@@ -8,12 +8,12 @@
 
 required_pypi = [
     'matplotlib',
-    'mlxtend>=0.18.0',  # some lower version are missing fpgrowth
+    'mlxtend>=0.18.0',  # some lower versions are missing fpgrowth
     'numpy',
-    'pandas',
+    'pandas<=2.1.4',  # pandas 2.2 introduced some issues with the query function
     'requests',  # used in c4.5
     'scipy',
-    'scikit-learn>=1.2.0',  # recently updates this
+    'scikit-learn>=1.2.0',  # recently updated this
     'tqdm',  # used in BART
 ]
 

tests/classification_continuous_inputs_test.py

@@ -8,18 +8,20 @@ class TestClassClassificationContinuousInputs:
     '''Tests simple classification for different models. Note: still doesn't test all the models!
     '''
 
-    def setup(self):
+    def setup_method(self):
         np.random.seed(13)
         random.seed(13)
         self.n = 40
         self.p = 2
         self.X_classification_binary = np.random.randn(self.n, self.p)
-        
+
         # y = x0 > 0
-        self.y_classification_binary = (self.X_classification_binary[:, 0] > 0).astype(int)
+        self.y_classification_binary = (
+            self.X_classification_binary[:, 0] > 0).astype(int)
 
         # flip labels for last few
-        self.y_classification_binary[-2:] = 1 - self.y_classification_binary[-2:]
+        self.y_classification_binary[-2:] = 1 - \
+            self.y_classification_binary[-2:]
 
     def test_classification_binary(self):
         '''Test imodels on basic binary classification task
@@ -58,7 +60,8 @@ def test_classification_binary(self):
                 preds_proba = m.predict_proba(X)
                 assert len(preds_proba.shape) == 2, 'preds_proba has 2 columns'
                 assert preds_proba.shape[1] == 2, 'preds_proba has 2 columns'
-                assert np.max(preds_proba) < 1.1, 'preds_proba has no values over 1'
+                assert np.max(
+                    preds_proba) < 1.1, 'preds_proba has no values over 1'
                 assert (np.argmax(preds_proba, axis=1) == preds).all(), ("predict_proba and "
                                                                          "predict agree")
 
@@ -70,5 +73,5 @@ def test_classification_binary(self):
 
 if __name__ == '__main__':
     t = TestClassClassificationContinuousInputs()
-    t.setup()
+    t.setup_method()
     t.test_classification_binary()