Add max_features to MFS to help STree integration

mfs/Selection.py

@@ -109,10 +109,16 @@ class MFS:
 
     Correlated Feature Selection as in "Correlation-based Feature Selection for
     Machine Learning" by Mark A. Hall
+
+    Parameters
+    ----------
+    max_features: int
+        The maximum number of features to return
     """
 
-    def __init__(self):
+    def __init__(self, max_features):
         self._initialize()
+        self._max_features = max_features
 
     def _initialize(self):
         """Initialize the attributes so support multiple calls using same
@@ -180,8 +186,8 @@ class MFS:
         """
         # lgtm has already recognized that this is a false positive
         rcf = self._su_labels[
-            features
-        ].sum()  # lgtm [py/hash-unhashable-value]
+            features  # lgtm [py/hash-unhashable-value]
+        ].sum()
         rff = 0.0
         k = len(features)
         for pair in list(combinations(features, 2)):
@@ -229,7 +235,10 @@ class MFS:
             candidates.append(feature_order[id_selected])
             self._scores.append(merit)
             del feature_order[id_selected]
-            if len(feature_order) == 0:
+            if (
+                len(feature_order) == 0
+                or len(candidates) == self._max_features
+            ):
                 # Force leaving the loop
                 continue_condition = False
             if len(self._scores) >= 5:
@@ -253,7 +262,7 @@ class MFS:
         self._result = candidates
         return self
 
-    def fcbs(self, X, y, threshold):
+    def fcbf(self, X, y, threshold):
         """Fast Correlation-Based Filter
 
         Parameters
@@ -273,10 +282,10 @@ class MFS:
         Raises
         ------
         ValueError
-            if the threshold is less than a selected value of 1e-4
+            if the threshold is less than a selected value of 1e-7
         """
-        if threshold < 1e-4:
-            raise ValueError("Threshold cannot be less than 1e-4")
+        if threshold < 1e-7:
+            raise ValueError("Threshold cannot be less than 1e-7")
         self._initialize()
         self.X_ = X
         self.y_ = y
@@ -301,6 +310,8 @@ class MFS:
                     s_list[index_q] = 0.0
             self._result.append(index_p)
             self._scores.append(s_list[index_p])
+            if len(self._result) == self._max_features:
+                break
         return self
 
     def get_results(self):