Fixes #393. label ordering in binary logistic regression,(#432)

 it now assings positive/negative labels based on Ord ordering of the label values rather than encouter order in the training dat

Author: espenloov

algorithms/linfa-logistic/src/lib.rs

@@ -301,32 +301,33 @@ where
 {
     let y = y.as_single_targets();
 
-    // counts the instances of two distinct class labels
     let mut binary_classes = [None, None];
-    // find binary classes of our target dataset
     for class in y {
         binary_classes = match binary_classes {
-            // count the first class label
             [None, None] => [Some((class, 1)), None],
-            // if the class has already been counted, increment the count
             [Some((c, count)), c2] if c == class => [Some((class, count + 1)), c2],
             [c1, Some((c, count))] if c == class => [c1, Some((class, count + 1))],
-            // count the second class label
             [Some(c1), None] => [Some(c1), Some((class, 1))],
-
-            // should not be possible
             [None, Some(_)] => unreachable!("impossible binary class array"),
-            // found 3rd distinct class
             [Some(_), Some(_)] => return Err(Error::TooManyClasses),
         };
     }
 
-    let (pos_class, neg_class) = match binary_classes {
+    let (class_a, class_b) = match binary_classes {
         [Some(a), Some(b)] => (a, b),
         _ => return Err(Error::TooFewClasses),
     };
 
-    let mut target_array = y
+    // Sort by label value (Ord), not by encounter order or count.
+    // The smaller label is always negative (-1),
+    // the larger label is always positive (+1).
+    let (neg_class, pos_class) = if class_a.0 < class_b.0 {
+        (class_a, class_b)
+    } else {
+        (class_b, class_a)
+    };
+
+    let target_array = y
         .into_iter()
         .map(|x| {
             if x == pos_class.0 {
@@ -337,24 +338,14 @@ where
         })
         .collect::<Array1<_>>();
 
-    let (pos_cl, neg_cl) = if pos_class.1 < neg_class.1 {
-        // If we found the larger class first, flip the sign in the target
-        // vector, so that -1.0 is always the label for the smaller class
-        // and 1.0 the label for the larger class
-        target_array *= -F::one();
-        (neg_class.0.clone(), pos_class.0.clone())
-    } else {
-        (pos_class.0.clone(), neg_class.0.clone())
-    };
-
     Ok((
         BinaryClassLabels {
             pos: ClassLabel {
-                class: pos_cl,
+                class: pos_class.0.clone(),
                 label: F::POSITIVE_LABEL,
             },
             neg: ClassLabel {
-                class: neg_cl,
+                class: neg_class.0.clone(),
                 label: F::NEGATIVE_LABEL,
             },
         },
@@ -989,7 +980,7 @@ mod test {
         let dataset = Dataset::new(x, y);
         let res = log_reg.fit(&dataset).unwrap();
         assert_abs_diff_eq!(res.intercept(), 0.0);
-        assert!(res.params().abs_diff_eq(&array![-0.681], 1e-3));
+        assert!(res.params().abs_diff_eq(&array![0.681], 1e-3));
         assert_eq!(
             &res.predict(dataset.records()),
             dataset.targets().as_single_targets()
@@ -1172,7 +1163,7 @@ mod test {
         let dataset = Dataset::new(x, y);
         let res = log_reg.fit(&dataset).unwrap();
         assert_abs_diff_eq!(res.intercept(), 0.0_f32);
-        assert!(res.params().abs_diff_eq(&array![-0.682_f32], 1e-3));
+        assert!(res.params().abs_diff_eq(&array![0.682_f32], 1e-3));
         assert_eq!(
             &res.predict(dataset.records()),
             dataset.targets().as_single_targets()
@@ -1375,4 +1366,28 @@ mod test {
             }
         ));
     }
+
+    #[test]
+    fn label_order_independent() {
+        let x1 = array![[-1.0], [1.0], [-0.5], [0.5]];
+        let y1 = array!["cat", "dog", "cat", "dog"];
+
+        let x2 = array![[1.0], [-1.0], [0.5], [-0.5]];
+        let y2 = array!["dog", "cat", "dog", "cat"];
+
+        let model1 = LogisticRegression::default()
+            .fit(&Dataset::new(x1, y1))
+            .unwrap();
+        let model2 = LogisticRegression::default()
+            .fit(&Dataset::new(x2, y2))
+            .unwrap();
+
+        assert_eq!(model1.labels().pos.class, "dog");
+        assert_eq!(model1.labels().neg.class, "cat");
+        assert_eq!(model2.labels().pos.class, "dog");
+        assert_eq!(model2.labels().neg.class, "cat");
+
+        assert_abs_diff_eq!(model1.intercept(), model2.intercept());
+        assert!(model1.params().abs_diff_eq(model2.params(), 1e-6));
+    }
 }