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1b095aeeca
Till now, most of the modules were importing all of the module objects (variables, classes, functions, other imports) into module namespace, which potentially could (and was) cause of unintentional use of class or methods, which was indirect imported. With this patch, all the star imports were substituted with top-level module, which provides desired class or function. Besides, all imports where sorted (where possible) in a way pep8[1] suggests - first are imports from standard library, than goes third party imports (like numpy, tensorflow etc) and finally coach modules. All of those sections are separated by one empty line. [1] https://www.python.org/dev/peps/pep-0008/#imports
208 lines
7.5 KiB
Python
208 lines
7.5 KiB
Python
#
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# Copyright (c) 2017 Intel Corporation
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import os
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import pickle
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import numpy as np
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from annoy import AnnoyIndex
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class AnnoyDictionary(object):
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def __init__(self, dict_size, key_width, new_value_shift_coefficient=0.1, batch_size=100, key_error_threshold=0.01):
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self.max_size = dict_size
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self.curr_size = 0
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self.new_value_shift_coefficient = new_value_shift_coefficient
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self.index = AnnoyIndex(key_width, metric='euclidean')
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self.index.set_seed(1)
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self.embeddings = np.zeros((dict_size, key_width))
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self.values = np.zeros(dict_size)
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self.lru_timestamps = np.zeros(dict_size)
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self.current_timestamp = 0.0
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# keys that are in this distance will be considered as the same key
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self.key_error_threshold = key_error_threshold
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self.initial_update_size = batch_size
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self.min_update_size = self.initial_update_size
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self.key_dimension = key_width
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self.value_dimension = 1
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self._reset_buffer()
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self.built_capacity = 0
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def add(self, keys, values):
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# Adds new embeddings and values to the dictionary
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indices = []
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indices_to_remove = []
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for i in range(keys.shape[0]):
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index = self._lookup_key_index(keys[i])
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if index:
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# update existing value
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self.values[index] += self.new_value_shift_coefficient * (values[i] - self.values[index])
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self.lru_timestamps[index] = self.current_timestamp
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indices_to_remove.append(i)
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else:
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# add new
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if self.curr_size >= self.max_size:
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# find the LRU entry
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index = np.argmin(self.lru_timestamps)
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else:
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index = self.curr_size
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self.curr_size += 1
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self.lru_timestamps[index] = self.current_timestamp
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indices.append(index)
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for i in reversed(indices_to_remove):
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keys = np.delete(keys, i, 0)
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values = np.delete(values, i, 0)
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self.buffered_keys = np.vstack((self.buffered_keys, keys))
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self.buffered_values = np.vstack((self.buffered_values, values))
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self.buffered_indices = self.buffered_indices + indices
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if len(self.buffered_indices) >= self.min_update_size:
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self.min_update_size = max(self.initial_update_size, int(self.curr_size * 0.02))
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self._rebuild_index()
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self.current_timestamp += 1
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# Returns the stored embeddings and values of the closest embeddings
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def query(self, keys, k):
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if not self.has_enough_entries(k):
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# this will only happen when the DND is not yet populated with enough entries, which is only during heatup
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# these values won't be used and therefore they are meaningless
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return [0.0], [0.0], [0]
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_, indices = self._get_k_nearest_neighbors_indices(keys, k)
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embeddings = []
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values = []
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for ind in indices:
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self.lru_timestamps[ind] = self.current_timestamp
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embeddings.append(self.embeddings[ind])
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values.append(self.values[ind])
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self.current_timestamp += 1
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return embeddings, values, indices
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def has_enough_entries(self, k):
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return self.curr_size > k and (self.built_capacity > k)
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def _get_k_nearest_neighbors_indices(self, keys, k):
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distances = []
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indices = []
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for key in keys:
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index, distance = self.index.get_nns_by_vector(key, k, include_distances=True)
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distances.append(distance)
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indices.append(index)
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return distances, indices
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def _rebuild_index(self):
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self.index.unbuild()
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self.embeddings[self.buffered_indices] = self.buffered_keys
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self.values[self.buffered_indices] = np.squeeze(self.buffered_values)
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for idx, key in zip(self.buffered_indices, self.buffered_keys):
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self.index.add_item(idx, key)
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self._reset_buffer()
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self.index.build(50)
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self.built_capacity = self.curr_size
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def _reset_buffer(self):
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self.buffered_keys = np.zeros((0, self.key_dimension))
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self.buffered_values = np.zeros((0, self.value_dimension))
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self.buffered_indices = []
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def _lookup_key_index(self, key):
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distance, index = self._get_k_nearest_neighbors_indices([key], 1)
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if distance != [[]] and distance[0][0] <= self.key_error_threshold:
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return index
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return None
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class QDND:
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def __init__(self, dict_size, key_width, num_actions, new_value_shift_coefficient=0.1, key_error_threshold=0.01,
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learning_rate=0.01):
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self.num_actions = num_actions
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self.dicts = []
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self.learning_rate = learning_rate
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# create a dict for each action
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for a in range(num_actions):
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new_dict = AnnoyDictionary(dict_size, key_width, new_value_shift_coefficient, key_error_threshold=key_error_threshold)
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self.dicts.append(new_dict)
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def add(self, embeddings, actions, values):
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# add a new set of embeddings and values to each of the underlining dictionaries
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embeddings = np.array(embeddings)
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actions = np.array(actions)
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values = np.array(values)
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for a in range(self.num_actions):
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idx = np.where(actions == a)
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curr_action_embeddings = embeddings[idx]
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curr_action_values = np.expand_dims(values[idx], -1)
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self.dicts[a].add(curr_action_embeddings, curr_action_values)
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return True
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def query(self, embeddings, action, k):
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# query for nearest neighbors to the given embeddings
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dnd_embeddings = []
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dnd_values = []
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dnd_indices = []
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for i in range(len(embeddings)):
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embedding, value, indices = self.dicts[action].query([embeddings[i]], k)
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dnd_embeddings.append(embedding[0])
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dnd_values.append(value[0])
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dnd_indices.append(indices[0])
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return dnd_embeddings, dnd_values, dnd_indices
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def has_enough_entries(self, k):
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# check if each of the action dictionaries has at least k entries
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for a in range(self.num_actions):
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if not self.dicts[a].has_enough_entries(k):
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return False
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return True
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def load_dnd(model_dir):
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max_id = 0
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for f in [s for s in os.listdir(model_dir) if s.endswith('.dnd')]:
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if int(f.split('.')[0]) > max_id:
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max_id = int(f.split('.')[0])
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model_path = str(max_id) + '.dnd'
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with open(os.path.join(model_dir, model_path), 'rb') as f:
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DND = pickle.load(f)
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for a in range(DND.num_actions):
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DND.dicts[a].index = AnnoyIndex(512, metric='euclidean')
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DND.dicts[a].index.set_seed(1)
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for idx, key in zip(range(DND.dicts[a].curr_size), DND.dicts[a].embeddings[:DND.dicts[a].curr_size]):
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DND.dicts[a].index.add_item(idx, key)
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DND.dicts[a].index.build(50)
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return DND
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