Add python implementation of the algorithm

fimdlp/mdlp.py

@@ -1,5 +1,6 @@
 import numpy as np
 from .cppfimdlp import CFImdlp
+from .pyfimdlp import PyFImdlp
 from sklearn.base import BaseEstimator, TransformerMixin
 from sklearn.utils.multiclass import unique_labels
 from sklearn.utils.validation import check_X_y, check_array, check_is_fitted
@@ -85,7 +86,7 @@ class FImdlp(TransformerMixin, BaseEstimator):
         self.cut_points_ = [None] * self.n_features_
         # Can do it in parallel
         for feature in self.features_:
-            self.discretizer_[feature] = CFImdlp(proposal=self.proposal)
+            self.discretizer_[feature] = PyFImdlp(proposal=self.proposal)
             self.discretizer_[feature].fit(X[:, feature], y)
             self.cut_points_[feature] = self.discretizer_[
                 feature
@@ -149,5 +150,5 @@ class FImdlp(TransformerMixin, BaseEstimator):
     def get_cut_points(self):
         result = []
         for feature in range(self.n_features_):
-            result.append(self.cut_points_[feature][:-1])
+            result.append(self.cut_points_[feature])
         return result

fimdlp/pyfimdlp.py

@@ -0,0 +1,142 @@
+import numpy as np
+from math import log
+from types import SimpleNamespace
+
+
+class PyFImdlp:
+    def __init__(self, proposal=True):
+        self.proposal = proposal
+        self.n_features_ = None
+        self.X_ = None
+        self.y_ = None
+        self.features_ = None
+        self.cut_points_ = []
+        self.entropy_cache = {}
+        self.information_gain_cache = {}
+
+    def fit(self, X, y):
+        self.n_features_ = len(X)
+        self.indices_ = np.argsort(X)
+        self.use_indices = True
+        self.X_ = X[self.indices_] if not self.use_indices else X
+        self.y_ = y[self.indices_] if not self.use_indices else y
+        self.compute_cut_points(0, len(y))
+        return self
+
+    def get_cut_points(self):
+        return sorted(list(set([cut.value for cut in self.cut_points_])))
+
+    def compute_cut_points(self, start, end):
+        cut = self.get_candidate(start, end)
+        if cut.value is None:
+            return
+        if self.mdlp(cut, start, end):
+            print("¡Ding!", cut.value, cut.index)
+            self.cut_points_.append(cut)
+        self.compute_cut_points(start, cut.index)
+        self.compute_cut_points(cut.index, end)
+
+    def mdlp(self, cut, start, end):
+        N = end - start
+        k = self.num_classes(start, end)
+        k1 = self.num_classes(start, cut.index)
+        k2 = self.num_classes(cut.index, end)
+        ent = self.entropy(start, end)
+        ent1 = self.entropy(start, cut.index)
+        ent2 = self.entropy(cut.index, end)
+        ig = self.information_gain(start, cut.index, end)
+        delta = log(pow(3, k) - 2, 2) - (
+            float(k) * ent - float(k1) * ent1 - float(k2) * ent2
+        )
+        term = 1 / N * (log(N - 1, 2) + delta)
+        return ig > term
+
+    def num_classes(self, start, end):
+        n_classes = set()
+        for i in range(start, end):
+            n_classes.add(
+                self.y_[self.indices_[i]] if self.use_indices else self.y_[i]
+            )
+        return len(n_classes)
+
+    def get_candidate(self, start, end):
+        """Return the best cutpoint candidate for the given range.
+
+        Parameters
+        ----------
+        start : int
+            Start of the range.
+        end : int
+            End of the range.
+
+        Returns
+        -------
+        candidate : SimpleNamespace with attributes index and value
+            value == None if no candidate is found.
+        """
+        candidate = SimpleNamespace()
+        candidate.value = None
+        minEntropy = float("inf")
+        for idx in range(start + 1, end):
+            condition = (
+                self.y_[self.indices_[idx]] == self.y_[self.indices_[idx - 1]]
+                if self.use_indices
+                else self.y_[idx] == self.y_[idx - 1]
+            )
+            if condition:
+                continue
+            entropy_left = self.entropy(start, idx)
+            entropy_right = self.entropy(idx, end)
+            entropy_cut = entropy_left + entropy_right
+            if entropy_cut < minEntropy:
+                minEntropy = entropy_cut
+                candidate.index = idx
+                if self.use_indices:
+                    candidate.value = (
+                        self.X_[self.indices_[idx]]
+                        + self.X_[self.indices_[idx - 1]]
+                    ) / 2
+                else:
+                    candidate.value = (self.X_[idx] + self.X_[idx - 1]) / 2
+        return candidate
+
+    def entropy(self, start, end) -> float:
+        n_labels = end - start
+        if n_labels <= 1:
+            return 0
+        if (start, end) in self.entropy_cache:
+            return self.entropy_cache[(start, end)]
+        if self.use_indices:
+            counts = np.bincount(self.y_[self.indices_[start:end]])
+        else:
+            counts = np.bincount(self.y_[start:end])
+        proportions = counts / n_labels
+        n_classes = np.count_nonzero(proportions)
+        if n_classes <= 1:
+            return 0
+        entropy = 0.0
+        # Compute standard entropy.
+        for prop in proportions:
+            if prop != 0.0:
+                entropy -= prop * log(prop, 2)
+        self.entropy_cache[(start, end)] = entropy
+        return entropy
+
+    def information_gain(self, start, cut, end):
+        if (start, cut, end) in self.information_gain_cache:
+            return self.information_gain_cache[(start, cut, end)]
+        labels = end - start
+        if labels == 0:
+            return 0.0
+        entropy = self.entropy(start, end)
+        card_left = cut - start
+        entropy_left = self.entropy(start, cut)
+        card_right = end - cut
+        entropy_right = self.entropy(cut, end)
+        result = (
+            entropy
+            - (card_left / labels) * entropy_left
+            - (card_right / labels) * entropy_right
+        )
+        self.information_gain_cache[(start, cut, end)] = result
+        return result

fimdlp/tests/FImdlp_test.py

@@ -31,13 +31,9 @@ class FImdlpTest(unittest.TestCase):
             [0.75, 1.399999976158142, 1.5],
         ]
         self.assertListEqual(expected, clf.get_cut_points())
-        self.assertListEqual(
-            ["feature_0", "feature_1", "feature_2", "feature_3"], clf.features_
-        )
-        self.assertEqual("class", clf.class_name_)
-        clf.fit(X, y, features=["a", "b", "c", "d"], class_name="class_name")
-        self.assertListEqual(["a", "b", "c", "d"], clf.features_)
-        self.assertEqual("class_name", clf.class_name_)
+        self.assertListEqual([0, 1, 2, 3], clf.features_)
+        clf.fit(X, y, features=[0, 2, 3])
+        self.assertListEqual([0, 2, 3], clf.features_)
 
     def test_fit_Errors(self):
         clf = FImdlp()

sample.py

@@ -61,69 +61,69 @@ data = load_iris()
 X = data.data
 y = data.target
 features = data.feature_names
-# test = FImdlp()
-# test.fit(X, y, features=features)
-# test.transform(X)
-# test.get_cut_points()
-for proposal in [True, False]:
-    X = data.data
-    y = data.target
-    print("*** Proposal: ", proposal)
-    test = CFImdlp(debug=True, proposal=proposal)
-    test.fit(X[:, 0], y)
-    result = test.get_cut_points()
-    for item in result:
-        print(
-            f"Class={item['classNumber']} - ({item['start']:3d}, "
-            f"{item['end']:3d}) -> ({item['fromValue']:3.1f}, "
-            f"{item['toValue']:3.1f}]"
-        )
-    print(test.get_discretized_values())
-    print("+" * 40)
-    X = np.array(
-        [
-            [5.1, 3.5, 1.4, 0.2],
-            [5.2, 3.0, 1.4, 0.2],
-            [5.3, 3.2, 1.3, 0.2],
-            [5.4, 3.1, 1.5, 0.2],
-        ]
-    )
-    y = np.array([0, 0, 0, 1])
-    print(test.fit(X[:, 0], y).transform(X[:, 0]))
-    result = test.get_cut_points()
-    for item in result:
-        print(
-            f"Class={item['classNumber']} - ({item['start']:3d}, {item['end']:3d})"
-            f" -> ({item['fromValue']:3.1f}, {item['toValue']:3.1f}]"
-        )
-    print("*" * 40)
-# print(Xs, ys)
-# print("**********************")
-# test = [(0, 3), (4, 4), (5, 5), (6, 8), (9, 9)]
-# print(ys)
-# for start, end in test:
-#     print("Testing ", start, end, ys[:end], ys[end:])
-#     print("Information gain: ", information_gain(ys, ys[:end], ys[end:]))
-# print(test.transform(X))
-# print(X)
-# print(indices)
-# print(np.array(X)[indices])
+test = FImdlp()
+test.fit(X, y)
+test.transform(X)
+print(test.get_cut_points())
+# for proposal in [True, False]:
+#     X = data.data
+#     y = data.target
+#     print("*** Proposal: ", proposal)
+#     test = CFImdlp(debug=True, proposal=proposal)
+#     test.fit(X[:, 0], y)
+#     result = test.get_cut_points()
+#     for item in result:
+#         print(
+#             f"Class={item['classNumber']} - ({item['start']:3d}, "
+#             f"{item['end']:3d}) -> ({item['fromValue']:3.1f}, "
+#             f"{item['toValue']:3.1f}]"
+#         )
+#     print(test.get_discretized_values())
+#     print("+" * 40)
+#     X = np.array(
+#         [
+#             [5.1, 3.5, 1.4, 0.2],
+#             [5.2, 3.0, 1.4, 0.2],
+#             [5.3, 3.2, 1.3, 0.2],
+#             [5.4, 3.1, 1.5, 0.2],
+#         ]
+#     )
+#     y = np.array([0, 0, 0, 1])
+#     print(test.fit(X[:, 0], y).transform(X[:, 0]))
+#     result = test.get_cut_points()
+#     for item in result:
+#         print(
+#             f"Class={item['classNumber']} - ({item['start']:3d}, {item['end']:3d})"
+#             f" -> ({item['fromValue']:3.1f}, {item['toValue']:3.1f}]"
+#         )
+#     print("*" * 40)
+# # print(Xs, ys)
+# # print("**********************")
+# # test = [(0, 3), (4, 4), (5, 5), (6, 8), (9, 9)]
+# # print(ys)
+# # for start, end in test:
+# #     print("Testing ", start, end, ys[:end], ys[end:])
+# #     print("Information gain: ", information_gain(ys, ys[:end], ys[end:]))
+# # print(test.transform(X))
+# # print(X)
+# # print(indices)
+# # print(np.array(X)[indices])
 
 
-# # k = test.cut_points(X[:, 0], y)
-# # print(k)
-# # k = test.cut_points_ant(X[:, 0], y)
-# # print(k)
-# # test.debug_points(X[:, 0], y)
-# X = [5.7, 5.3, 5.2, 5.1, 5.0, 5.6, 5.1, 6.0, 5.1, 5.9]
-# y = [1, 1, 1, 1, 1, 2, 2, 2, 2, 2]
-# indices = [4, 3, 6, 8, 2, 1, 5, 0, 9, 7]
-# clf = CFImdlp(debug=True, proposal=False)
-# clf.fit(X, y)
-# print(clf.get_cut_points())
-# y = [1, 1, 1, 1, 1, 2, 2, 2, 2, 2]
-# # To check
-# indices2 = np.argsort(X)
-# Xs = np.array(X)[indices2]
-# ys = np.array(y)[indices2]
-kdd_JapaneseVowels
+# # # k = test.cut_points(X[:, 0], y)
+# # # print(k)
+# # # k = test.cut_points_ant(X[:, 0], y)
+# # # print(k)
+# # # test.debug_points(X[:, 0], y)
+# # X = [5.7, 5.3, 5.2, 5.1, 5.0, 5.6, 5.1, 6.0, 5.1, 5.9]
+# # y = [1, 1, 1, 1, 1, 2, 2, 2, 2, 2]
+# # indices = [4, 3, 6, 8, 2, 1, 5, 0, 9, 7]
+# # clf = CFImdlp(debug=True, proposal=False)
+# # clf.fit(X, y)
+# # print(clf.get_cut_points())
+# # y = [1, 1, 1, 1, 1, 2, 2, 2, 2, 2]
+# # # To check
+# # indices2 = np.argsort(X)
+# # Xs = np.array(X)[indices2]
+# # ys = np.array(y)[indices2]
+# kdd_JapaneseVowels