coach v0.8.0

memories/differentiable_neural_dictionary.py

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+#
+# Copyright (c) 2017 Intel Corporation 
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import numpy as np
+from annoy import AnnoyIndex
+
+
+class AnnoyDictionary:
+    def __init__(self, dict_size, key_width, new_value_shift_coefficient=0.1, batch_size=100, key_error_threshold=0.01):
+        self.max_size = dict_size
+        self.curr_size = 0
+        self.new_value_shift_coefficient = new_value_shift_coefficient
+
+        self.index = AnnoyIndex(key_width, metric='euclidean')
+        self.index.set_seed(1)
+
+        self.embeddings = np.zeros((dict_size, key_width))
+        self.values = np.zeros(dict_size)
+
+        self.lru_timestamps = np.zeros(dict_size)
+        self.current_timestamp = 0.0
+
+        # keys that are in this distance will be considered as the same key
+        self.key_error_threshold = key_error_threshold
+
+        self.initial_update_size = batch_size
+        self.min_update_size = self.initial_update_size
+        self.key_dimension = key_width
+        self.value_dimension = 1
+        self._reset_buffer()
+
+        self.built_capacity = 0
+
+    def add(self, keys, values):
+        # Adds new embeddings and values to the dictionary
+        indices = []
+        indices_to_remove = []
+        for i in range(keys.shape[0]):
+            index = self._lookup_key_index(keys[i])
+            if index:
+                # update existing value
+                self.values[index] += self.new_value_shift_coefficient * (values[i] - self.values[index])
+                self.lru_timestamps[index] = self.current_timestamp
+                indices_to_remove.append(i)
+            else:
+                # add new
+                if self.curr_size >= self.max_size:
+                    # find the LRU entry
+                    index = np.argmin(self.lru_timestamps)
+                else:
+                    index = self.curr_size
+                    self.curr_size += 1
+                self.lru_timestamps[index] = self.current_timestamp
+                indices.append(index)
+
+        for i in reversed(indices_to_remove):
+            keys = np.delete(keys, i, 0)
+            values = np.delete(values, i, 0)
+
+        self.buffered_keys = np.vstack((self.buffered_keys, keys))
+        self.buffered_values = np.vstack((self.buffered_values, values))
+        self.buffered_indices = self.buffered_indices + indices
+
+        if len(self.buffered_indices) >= self.min_update_size:
+            self.min_update_size = max(self.initial_update_size, int(self.curr_size * 0.02))
+            self._rebuild_index()
+
+        self.current_timestamp += 1
+
+    # Returns the stored embeddings and values of the closest embeddings
+    def query(self, keys, k):
+        _, indices = self._get_k_nearest_neighbors_indices(keys, k)
+
+        embeddings = []
+        values = []
+        for ind in indices:
+            self.lru_timestamps[ind] = self.current_timestamp
+            embeddings.append(self.embeddings[ind])
+            values.append(self.values[ind])
+
+        self.current_timestamp += 1
+
+        return embeddings, values
+
+    def has_enough_entries(self, k):
+        return self.curr_size > k and (self.built_capacity > k)
+
+    def _get_k_nearest_neighbors_indices(self, keys, k):
+        distances = []
+        indices = []
+        for key in keys:
+            index, distance = self.index.get_nns_by_vector(key, k, include_distances=True)
+            distances.append(distance)
+            indices.append(index)
+        return distances, indices
+
+    def _rebuild_index(self):
+        self.index.unbuild()
+        self.embeddings[self.buffered_indices] = self.buffered_keys
+        self.values[self.buffered_indices] = np.squeeze(self.buffered_values)
+        for idx, key in zip(self.buffered_indices, self.buffered_keys):
+            self.index.add_item(idx, key)
+
+        self._reset_buffer()
+
+        self.index.build(50)
+        self.built_capacity = self.curr_size
+
+    def _reset_buffer(self):
+        self.buffered_keys = np.zeros((0, self.key_dimension))
+        self.buffered_values = np.zeros((0, self.value_dimension))
+        self.buffered_indices = []
+
+    def _lookup_key_index(self, key):
+        distance, index = self._get_k_nearest_neighbors_indices([key], 1)
+        if distance != [[]] and distance[0][0] <= self.key_error_threshold:
+            return index
+        return None
+
+
+class QDND:
+    def __init__(self, dict_size, key_width, num_actions, new_value_shift_coefficient=0.1, key_error_threshold=0.01):
+        self.num_actions = num_actions
+        self.dicts = []
+
+        # create a dict for each action
+        for a in range(num_actions):
+            new_dict = AnnoyDictionary(dict_size, key_width, new_value_shift_coefficient, key_error_threshold=key_error_threshold)
+            self.dicts.append(new_dict)
+
+    def add(self, embeddings, actions, values):
+        # add a new set of embeddings and values to each of the underlining dictionaries
+        embeddings = np.array(embeddings)
+        actions = np.array(actions)
+        values = np.array(values)
+        for a in range(self.num_actions):
+            idx = np.where(actions == a)
+            curr_action_embeddings = embeddings[idx]
+            curr_action_values = np.expand_dims(values[idx], -1)
+
+            self.dicts[a].add(curr_action_embeddings, curr_action_values)
+        return True
+
+    def query(self, embeddings, actions, k):
+        # query for nearest neighbors to the given embeddings
+        dnd_embeddings = []
+        dnd_values = []
+        for i, action in enumerate(actions):
+            embedding, value = self.dicts[action].query([embeddings[i]], k)
+            dnd_embeddings.append(embedding[0])
+            dnd_values.append(value[0])
+
+        return dnd_embeddings, dnd_values
+
+    def has_enough_entries(self, k):
+        # check if each of the action dictionaries has at least k entries
+        for a in range(self.num_actions):
+            if not self.dicts[a].has_enough_entries(k):
+                return False
+        return True