initial commit of project

evaluation/helpers/knn_classifier.py

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+# adapted from:
+# Pellegrini., C., E. Özsoy., M. Wintergerst., and G. Groh. (2021).
+# “Exploiting Food Embeddings for Ingredient Substitution.”
+# In: Proceedings of the 14th International Joint Conference on Biomedical
+# Engineering Systems and Technologies - Volume 5: HEALTHINF, INSTICC.
+# SciTePress, pp. 67–77. isbn: 978-989-758-490-9. doi: 10.5220/0010202000670077.
+
+from pathlib import Path
+
+import joblib
+from sklearn.neighbors import NearestNeighbors
+
+
+class KNNClassifier:
+    def __init__(self, all_ingredient_embeddings, max_embedding_count,
+                 save_path=Path('data/eval/knn_classifier.joblib')):
+
+        if save_path.exists():
+            print('Loading Existing Classifier')
+            self.knn_classifier: NearestNeighbors = joblib.load(save_path)
+        else:
+            print('Training New Classifier')
+            # To make sure we don't just get ourselves: add max_embedding_count
+            self.knn_classifier: NearestNeighbors = NearestNeighbors(n_neighbors=max_embedding_count + 200, n_jobs=12,
+                                                                     algorithm='brute')  # kd_tree, ball_tree or brute
+            self.knn_classifier.fit(all_ingredient_embeddings)
+
+            print('Saving Classifier')
+            joblib.dump(self.knn_classifier, save_path)
+
+        print(f'\nKNN with: {self.knn_classifier._fit_method} and leaf size: {self.knn_classifier.leaf_size}\n')
+
+    def k_nearest_neighbors(self, ingredient_embeddings):
+        distances, indices = self.knn_classifier.kneighbors(ingredient_embeddings, return_distance=True)
+
+        return distances, indices