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Please provide a description of the function:def increment_lessons(self, measure_vals, reward_buff_sizes=None):
ret = {}
if reward_buff_sizes:
for brain_name, buff_size in reward_buff_sizes.items():
if self._lesson_ready_to_increment(brain_name, buff_size):
measure_val = measure_vals[brain_name]
ret[brain_name] = (self.brains_to_curriculums[brain_name]
.increment_lesson(measure_val))
else:
for brain_name, measure_val in measure_vals.items():
ret[brain_name] = (self.brains_to_curriculums[brain_name]
.increment_lesson(measure_val))
return ret | [
"Attempts to increments all the lessons of all the curriculums in this\n MetaCurriculum. Note that calling this method does not guarantee the\n lesson of a curriculum will increment. The lesson of a curriculum will\n only increment if the specified measure threshold defined in the\n curriculum has been reached and the minimum number of episodes in the\n lesson have been completed.\n\n Args:\n measure_vals (dict): A dict of brain name to measure value.\n reward_buff_sizes (dict): A dict of brain names to the size of their\n corresponding reward buffers.\n\n Returns:\n A dict from brain name to whether that brain's lesson number was\n incremented.\n "
] |
Please provide a description of the function:def set_all_curriculums_to_lesson_num(self, lesson_num):
for _, curriculum in self.brains_to_curriculums.items():
curriculum.lesson_num = lesson_num | [
"Sets all the curriculums in this meta curriculum to a specified\n lesson number.\n\n Args:\n lesson_num (int): The lesson number which all the curriculums will\n be set to.\n "
] |
Please provide a description of the function:def get_config(self):
config = {}
for _, curriculum in self.brains_to_curriculums.items():
curr_config = curriculum.get_config()
config.update(curr_config)
return config | [
"Get the combined configuration of all curriculums in this\n MetaCurriculum.\n\n Returns:\n A dict from parameter to value.\n "
] |
Please provide a description of the function:def reset(self, config=None, train_mode=True, custom_reset_parameters=None) -> AllBrainInfo:
if config is None:
config = self._resetParameters
elif config:
logger.info("Academy reset with parameters: {0}"
.format(', '.join([str(x) + ' -> ' + str(config[x]) for x in config])))
for k in config:
if (k in self._resetParameters) and (isinstance(config[k], (int, float))):
self._resetParameters[k] = config[k]
elif not isinstance(config[k], (int, float)):
raise UnityEnvironmentException(
"The value for parameter '{0}'' must be an Integer or a Float.".format(k))
else:
raise UnityEnvironmentException(
"The parameter '{0}' is not a valid parameter.".format(k))
if self._loaded:
outputs = self.communicator.exchange(
self._generate_reset_input(train_mode, config, custom_reset_parameters)
)
if outputs is None:
raise KeyboardInterrupt
rl_output = outputs.rl_output
s = self._get_state(rl_output)
self._global_done = s[1]
for _b in self._external_brain_names:
self._n_agents[_b] = len(s[0][_b].agents)
return s[0]
else:
raise UnityEnvironmentException("No Unity environment is loaded.") | [
"\n Sends a signal to reset the unity environment.\n :return: AllBrainInfo : A data structure corresponding to the initial reset state of the environment.\n "
] |
Please provide a description of the function:def step(self, vector_action=None, memory=None, text_action=None, value=None, custom_action=None) -> AllBrainInfo:
vector_action = {} if vector_action is None else vector_action
memory = {} if memory is None else memory
text_action = {} if text_action is None else text_action
value = {} if value is None else value
custom_action = {} if custom_action is None else custom_action
# Check that environment is loaded, and episode is currently running.
if self._loaded and not self._global_done and self._global_done is not None:
if isinstance(vector_action, self.SINGLE_BRAIN_ACTION_TYPES):
if self._num_external_brains == 1:
vector_action = {self._external_brain_names[0]: vector_action}
elif self._num_external_brains > 1:
raise UnityActionException(
"You have {0} brains, you need to feed a dictionary of brain names a keys, "
"and vector_actions as values".format(self._num_brains))
else:
raise UnityActionException(
"There are no external brains in the environment, "
"step cannot take a vector_action input")
if isinstance(memory, self.SINGLE_BRAIN_ACTION_TYPES):
if self._num_external_brains == 1:
memory = {self._external_brain_names[0]: memory}
elif self._num_external_brains > 1:
raise UnityActionException(
"You have {0} brains, you need to feed a dictionary of brain names as keys "
"and memories as values".format(self._num_brains))
else:
raise UnityActionException(
"There are no external brains in the environment, "
"step cannot take a memory input")
if isinstance(text_action, self.SINGLE_BRAIN_TEXT_TYPES):
if self._num_external_brains == 1:
text_action = {self._external_brain_names[0]: text_action}
elif self._num_external_brains > 1:
raise UnityActionException(
"You have {0} brains, you need to feed a dictionary of brain names as keys "
"and text_actions as values".format(self._num_brains))
else:
raise UnityActionException(
"There are no external brains in the environment, "
"step cannot take a value input")
if isinstance(value, self.SINGLE_BRAIN_ACTION_TYPES):
if self._num_external_brains == 1:
value = {self._external_brain_names[0]: value}
elif self._num_external_brains > 1:
raise UnityActionException(
"You have {0} brains, you need to feed a dictionary of brain names as keys "
"and state/action value estimates as values".format(self._num_brains))
else:
raise UnityActionException(
"There are no external brains in the environment, "
"step cannot take a value input")
if isinstance(custom_action, CustomAction):
if self._num_external_brains == 1:
custom_action = {self._external_brain_names[0]: custom_action}
elif self._num_external_brains > 1:
raise UnityActionException(
"You have {0} brains, you need to feed a dictionary of brain names as keys "
"and CustomAction instances as values".format(self._num_brains))
else:
raise UnityActionException(
"There are no external brains in the environment, "
"step cannot take a custom_action input")
for brain_name in list(vector_action.keys()) + list(memory.keys()) + list(
text_action.keys()):
if brain_name not in self._external_brain_names:
raise UnityActionException(
"The name {0} does not correspond to an external brain "
"in the environment".format(brain_name))
for brain_name in self._external_brain_names:
n_agent = self._n_agents[brain_name]
if brain_name not in vector_action:
if self._brains[brain_name].vector_action_space_type == "discrete":
vector_action[brain_name] = [0.0] * n_agent * len(
self._brains[brain_name].vector_action_space_size)
else:
vector_action[brain_name] = [0.0] * n_agent * \
self._brains[
brain_name].vector_action_space_size[0]
else:
vector_action[brain_name] = self._flatten(vector_action[brain_name])
if brain_name not in memory:
memory[brain_name] = []
else:
if memory[brain_name] is None:
memory[brain_name] = []
else:
memory[brain_name] = self._flatten(memory[brain_name])
if brain_name not in text_action:
text_action[brain_name] = [""] * n_agent
else:
if text_action[brain_name] is None:
text_action[brain_name] = [""] * n_agent
if isinstance(text_action[brain_name], str):
text_action[brain_name] = [text_action[brain_name]] * n_agent
if brain_name not in custom_action:
custom_action[brain_name] = [None] * n_agent
else:
if custom_action[brain_name] is None:
custom_action[brain_name] = [None] * n_agent
if isinstance(custom_action[brain_name], CustomAction):
custom_action[brain_name] = [custom_action[brain_name]] * n_agent
number_text_actions = len(text_action[brain_name])
if not ((number_text_actions == n_agent) or number_text_actions == 0):
raise UnityActionException(
"There was a mismatch between the provided text_action and "
"the environment's expectation: "
"The brain {0} expected {1} text_action but was given {2}".format(
brain_name, n_agent, number_text_actions))
discrete_check = self._brains[brain_name].vector_action_space_type == "discrete"
expected_discrete_size = n_agent * len(
self._brains[brain_name].vector_action_space_size)
continuous_check = self._brains[brain_name].vector_action_space_type == "continuous"
expected_continuous_size = self._brains[brain_name].vector_action_space_size[
0] * n_agent
if not ((discrete_check and len(
vector_action[brain_name]) == expected_discrete_size) or
(continuous_check and len(
vector_action[brain_name]) == expected_continuous_size)):
raise UnityActionException(
"There was a mismatch between the provided action and "
"the environment's expectation: "
"The brain {0} expected {1} {2} action(s), but was provided: {3}"
.format(brain_name, str(expected_discrete_size)
if discrete_check
else str(expected_continuous_size),
self._brains[brain_name].vector_action_space_type,
str(vector_action[brain_name])))
outputs = self.communicator.exchange(
self._generate_step_input(vector_action, memory, text_action, value, custom_action))
if outputs is None:
raise KeyboardInterrupt
rl_output = outputs.rl_output
state = self._get_state(rl_output)
self._global_done = state[1]
for _b in self._external_brain_names:
self._n_agents[_b] = len(state[0][_b].agents)
return state[0]
elif not self._loaded:
raise UnityEnvironmentException("No Unity environment is loaded.")
elif self._global_done:
raise UnityActionException(
"The episode is completed. Reset the environment with 'reset()'")
elif self.global_done is None:
raise UnityActionException(
"You cannot conduct step without first calling reset. "
"Reset the environment with 'reset()'") | [
"\n Provides the environment with an action, moves the environment dynamics forward accordingly,\n and returns observation, state, and reward information to the agent.\n :param value: Value estimates provided by agents.\n :param vector_action: Agent's vector action. Can be a scalar or vector of int/floats.\n :param memory: Vector corresponding to memory used for recurrent policies.\n :param text_action: Text action to send to environment for.\n :param custom_action: Optional instance of a CustomAction protobuf message.\n :return: AllBrainInfo : A Data structure corresponding to the new state of the environment.\n "
] |
Please provide a description of the function:def _flatten(cls, arr) -> List[float]:
if isinstance(arr, cls.SCALAR_ACTION_TYPES):
arr = [float(arr)]
if isinstance(arr, np.ndarray):
arr = arr.tolist()
if len(arr) == 0:
return arr
if isinstance(arr[0], np.ndarray):
arr = [item for sublist in arr for item in sublist.tolist()]
if isinstance(arr[0], list):
arr = [item for sublist in arr for item in sublist]
arr = [float(x) for x in arr]
return arr | [
"\n Converts arrays to list.\n :param arr: numpy vector.\n :return: flattened list.\n "
] |
Please provide a description of the function:def _get_state(self, output: UnityRLOutput) -> (AllBrainInfo, bool):
_data = {}
global_done = output.global_done
for brain_name in output.agentInfos:
agent_info_list = output.agentInfos[brain_name].value
_data[brain_name] = BrainInfo.from_agent_proto(agent_info_list,
self.brains[brain_name])
return _data, global_done | [
"\n Collects experience information from all external brains in environment at current step.\n :return: a dictionary of BrainInfo objects.\n "
] |
Please provide a description of the function:def end_experience_collection_timer(self):
if self.time_start_experience_collection:
curr_delta = time() - self.time_start_experience_collection
if self.delta_last_experience_collection is None:
self.delta_last_experience_collection = curr_delta
else:
self.delta_last_experience_collection += curr_delta
self.time_start_experience_collection = None | [
"\n Inform Metrics class that experience collection is done.\n "
] |
Please provide a description of the function:def add_delta_step(self, delta: float):
if self.delta_last_experience_collection:
self.delta_last_experience_collection += delta
else:
self.delta_last_experience_collection = delta | [
"\n Inform Metrics class about time to step in environment.\n "
] |
Please provide a description of the function:def start_policy_update_timer(self, number_experiences: int, mean_return: float):
self.last_buffer_length = number_experiences
self.last_mean_return = mean_return
self.time_policy_update_start = time() | [
"\n Inform Metrics class that policy update has started.\n :int number_experiences: Number of experiences in Buffer at this point.\n :float mean_return: Return averaged across all cumulative returns since last policy update\n "
] |
Please provide a description of the function:def end_policy_update(self):
if self.time_policy_update_start:
self.delta_policy_update = time() - self.time_policy_update_start
else:
self.delta_policy_update = 0
delta_train_start = time() - self.time_training_start
LOGGER.debug(" Policy Update Training Metrics for {}: "
"\n\t\tTime to update Policy: {:0.3f} s \n"
"\t\tTime elapsed since training: {:0.3f} s \n"
"\t\tTime for experience collection: {:0.3f} s \n"
"\t\tBuffer Length: {} \n"
"\t\tReturns : {:0.3f}\n"
.format(self.brain_name, self.delta_policy_update,
delta_train_start, self.delta_last_experience_collection,
self.last_buffer_length, self.last_mean_return))
self._add_row(delta_train_start) | [
"\n Inform Metrics class that policy update has started.\n "
] |
Please provide a description of the function:def write_training_metrics(self):
with open(self.path, 'w') as file:
writer = csv.writer(file)
writer.writerow(FIELD_NAMES)
for row in self.rows:
writer.writerow(row) | [
"\n Write Training Metrics to CSV\n "
] |
Please provide a description of the function:def create_reward_encoder():
last_reward = tf.Variable(0, name="last_reward", trainable=False, dtype=tf.float32)
new_reward = tf.placeholder(shape=[], dtype=tf.float32, name='new_reward')
update_reward = tf.assign(last_reward, new_reward)
return last_reward, new_reward, update_reward | [
"Creates TF ops to track and increment recent average cumulative reward."
] |
Please provide a description of the function:def create_curiosity_encoders(self):
encoded_state_list = []
encoded_next_state_list = []
if self.vis_obs_size > 0:
self.next_visual_in = []
visual_encoders = []
next_visual_encoders = []
for i in range(self.vis_obs_size):
# Create input ops for next (t+1) visual observations.
next_visual_input = self.create_visual_input(self.brain.camera_resolutions[i],
name="next_visual_observation_" + str(i))
self.next_visual_in.append(next_visual_input)
# Create the encoder ops for current and next visual input. Not that these encoders are siamese.
encoded_visual = self.create_visual_observation_encoder(self.visual_in[i], self.curiosity_enc_size,
self.swish, 1, "stream_{}_visual_obs_encoder"
.format(i), False)
encoded_next_visual = self.create_visual_observation_encoder(self.next_visual_in[i],
self.curiosity_enc_size,
self.swish, 1,
"stream_{}_visual_obs_encoder".format(i),
True)
visual_encoders.append(encoded_visual)
next_visual_encoders.append(encoded_next_visual)
hidden_visual = tf.concat(visual_encoders, axis=1)
hidden_next_visual = tf.concat(next_visual_encoders, axis=1)
encoded_state_list.append(hidden_visual)
encoded_next_state_list.append(hidden_next_visual)
if self.vec_obs_size > 0:
# Create the encoder ops for current and next vector input. Not that these encoders are siamese.
# Create input op for next (t+1) vector observation.
self.next_vector_in = tf.placeholder(shape=[None, self.vec_obs_size], dtype=tf.float32,
name='next_vector_observation')
encoded_vector_obs = self.create_vector_observation_encoder(self.vector_in,
self.curiosity_enc_size,
self.swish, 2, "vector_obs_encoder",
False)
encoded_next_vector_obs = self.create_vector_observation_encoder(self.next_vector_in,
self.curiosity_enc_size,
self.swish, 2,
"vector_obs_encoder",
True)
encoded_state_list.append(encoded_vector_obs)
encoded_next_state_list.append(encoded_next_vector_obs)
encoded_state = tf.concat(encoded_state_list, axis=1)
encoded_next_state = tf.concat(encoded_next_state_list, axis=1)
return encoded_state, encoded_next_state | [
"\n Creates state encoders for current and future observations.\n Used for implementation of Curiosity-driven Exploration by Self-supervised Prediction\n See https://arxiv.org/abs/1705.05363 for more details.\n :return: current and future state encoder tensors.\n "
] |
Please provide a description of the function:def create_inverse_model(self, encoded_state, encoded_next_state):
combined_input = tf.concat([encoded_state, encoded_next_state], axis=1)
hidden = tf.layers.dense(combined_input, 256, activation=self.swish)
if self.brain.vector_action_space_type == "continuous":
pred_action = tf.layers.dense(hidden, self.act_size[0], activation=None)
squared_difference = tf.reduce_sum(tf.squared_difference(pred_action, self.selected_actions), axis=1)
self.inverse_loss = tf.reduce_mean(tf.dynamic_partition(squared_difference, self.mask, 2)[1])
else:
pred_action = tf.concat(
[tf.layers.dense(hidden, self.act_size[i], activation=tf.nn.softmax)
for i in range(len(self.act_size))], axis=1)
cross_entropy = tf.reduce_sum(-tf.log(pred_action + 1e-10) * self.selected_actions, axis=1)
self.inverse_loss = tf.reduce_mean(tf.dynamic_partition(cross_entropy, self.mask, 2)[1]) | [
"\n Creates inverse model TensorFlow ops for Curiosity module.\n Predicts action taken given current and future encoded states.\n :param encoded_state: Tensor corresponding to encoded current state.\n :param encoded_next_state: Tensor corresponding to encoded next state.\n "
] |
Please provide a description of the function:def create_forward_model(self, encoded_state, encoded_next_state):
combined_input = tf.concat([encoded_state, self.selected_actions], axis=1)
hidden = tf.layers.dense(combined_input, 256, activation=self.swish)
# We compare against the concatenation of all observation streams, hence `self.vis_obs_size + int(self.vec_obs_size > 0)`.
pred_next_state = tf.layers.dense(hidden, self.curiosity_enc_size * (self.vis_obs_size + int(self.vec_obs_size > 0)),
activation=None)
squared_difference = 0.5 * tf.reduce_sum(tf.squared_difference(pred_next_state, encoded_next_state), axis=1)
self.intrinsic_reward = tf.clip_by_value(self.curiosity_strength * squared_difference, 0, 1)
self.forward_loss = tf.reduce_mean(tf.dynamic_partition(squared_difference, self.mask, 2)[1]) | [
"\n Creates forward model TensorFlow ops for Curiosity module.\n Predicts encoded future state based on encoded current state and given action.\n :param encoded_state: Tensor corresponding to encoded current state.\n :param encoded_next_state: Tensor corresponding to encoded next state.\n "
] |
Please provide a description of the function:def create_ppo_optimizer(self, probs, old_probs, value, entropy, beta, epsilon, lr, max_step):
self.returns_holder = tf.placeholder(shape=[None], dtype=tf.float32, name='discounted_rewards')
self.advantage = tf.placeholder(shape=[None, 1], dtype=tf.float32, name='advantages')
self.learning_rate = tf.train.polynomial_decay(lr, self.global_step, max_step, 1e-10, power=1.0)
self.old_value = tf.placeholder(shape=[None], dtype=tf.float32, name='old_value_estimates')
decay_epsilon = tf.train.polynomial_decay(epsilon, self.global_step, max_step, 0.1, power=1.0)
decay_beta = tf.train.polynomial_decay(beta, self.global_step, max_step, 1e-5, power=1.0)
optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate)
clipped_value_estimate = self.old_value + tf.clip_by_value(tf.reduce_sum(value, axis=1) - self.old_value,
- decay_epsilon, decay_epsilon)
v_opt_a = tf.squared_difference(self.returns_holder, tf.reduce_sum(value, axis=1))
v_opt_b = tf.squared_difference(self.returns_holder, clipped_value_estimate)
self.value_loss = tf.reduce_mean(tf.dynamic_partition(tf.maximum(v_opt_a, v_opt_b), self.mask, 2)[1])
# Here we calculate PPO policy loss. In continuous control this is done independently for each action gaussian
# and then averaged together. This provides significantly better performance than treating the probability
# as an average of probabilities, or as a joint probability.
r_theta = tf.exp(probs - old_probs)
p_opt_a = r_theta * self.advantage
p_opt_b = tf.clip_by_value(r_theta, 1.0 - decay_epsilon, 1.0 + decay_epsilon) * self.advantage
self.policy_loss = -tf.reduce_mean(tf.dynamic_partition(tf.minimum(p_opt_a, p_opt_b), self.mask, 2)[1])
self.loss = self.policy_loss + 0.5 * self.value_loss - decay_beta * tf.reduce_mean(
tf.dynamic_partition(entropy, self.mask, 2)[1])
if self.use_curiosity:
self.loss += 10 * (0.2 * self.forward_loss + 0.8 * self.inverse_loss)
self.update_batch = optimizer.minimize(self.loss) | [
"\n Creates training-specific Tensorflow ops for PPO models.\n :param probs: Current policy probabilities\n :param old_probs: Past policy probabilities\n :param value: Current value estimate\n :param beta: Entropy regularization strength\n :param entropy: Current policy entropy\n :param epsilon: Value for policy-divergence threshold\n :param lr: Learning rate\n :param max_step: Total number of training steps.\n "
] |
Please provide a description of the function:def evaluate(self, brain_info):
feed_dict = {self.model.batch_size: len(brain_info.vector_observations),
self.model.sequence_length: 1}
epsilon = None
if self.use_recurrent:
if not self.use_continuous_act:
feed_dict[self.model.prev_action] = brain_info.previous_vector_actions.reshape(
[-1, len(self.model.act_size)])
if brain_info.memories.shape[1] == 0:
brain_info.memories = self.make_empty_memory(len(brain_info.agents))
feed_dict[self.model.memory_in] = brain_info.memories
if self.use_continuous_act:
epsilon = np.random.normal(
size=(len(brain_info.vector_observations), self.model.act_size[0]))
feed_dict[self.model.epsilon] = epsilon
feed_dict = self._fill_eval_dict(feed_dict, brain_info)
run_out = self._execute_model(feed_dict, self.inference_dict)
if self.use_continuous_act:
run_out['random_normal_epsilon'] = epsilon
return run_out | [
"\n Evaluates policy for the agent experiences provided.\n :param brain_info: BrainInfo object containing inputs.\n :return: Outputs from network as defined by self.inference_dict.\n "
] |
Please provide a description of the function:def update(self, mini_batch, num_sequences):
feed_dict = {self.model.batch_size: num_sequences,
self.model.sequence_length: self.sequence_length,
self.model.mask_input: mini_batch['masks'].flatten(),
self.model.returns_holder: mini_batch['discounted_returns'].flatten(),
self.model.old_value: mini_batch['value_estimates'].flatten(),
self.model.advantage: mini_batch['advantages'].reshape([-1, 1]),
self.model.all_old_log_probs: mini_batch['action_probs'].reshape(
[-1, sum(self.model.act_size)])}
if self.use_continuous_act:
feed_dict[self.model.output_pre] = mini_batch['actions_pre'].reshape(
[-1, self.model.act_size[0]])
feed_dict[self.model.epsilon] = mini_batch['random_normal_epsilon'].reshape(
[-1, self.model.act_size[0]])
else:
feed_dict[self.model.action_holder] = mini_batch['actions'].reshape(
[-1, len(self.model.act_size)])
if self.use_recurrent:
feed_dict[self.model.prev_action] = mini_batch['prev_action'].reshape(
[-1, len(self.model.act_size)])
feed_dict[self.model.action_masks] = mini_batch['action_mask'].reshape(
[-1, sum(self.brain.vector_action_space_size)])
if self.use_vec_obs:
feed_dict[self.model.vector_in] = mini_batch['vector_obs'].reshape(
[-1, self.vec_obs_size])
if self.use_curiosity:
feed_dict[self.model.next_vector_in] = mini_batch['next_vector_in'].reshape(
[-1, self.vec_obs_size])
if self.model.vis_obs_size > 0:
for i, _ in enumerate(self.model.visual_in):
_obs = mini_batch['visual_obs%d' % i]
if self.sequence_length > 1 and self.use_recurrent:
(_batch, _seq, _w, _h, _c) = _obs.shape
feed_dict[self.model.visual_in[i]] = _obs.reshape([-1, _w, _h, _c])
else:
feed_dict[self.model.visual_in[i]] = _obs
if self.use_curiosity:
for i, _ in enumerate(self.model.visual_in):
_obs = mini_batch['next_visual_obs%d' % i]
if self.sequence_length > 1 and self.use_recurrent:
(_batch, _seq, _w, _h, _c) = _obs.shape
feed_dict[self.model.next_visual_in[i]] = _obs.reshape([-1, _w, _h, _c])
else:
feed_dict[self.model.next_visual_in[i]] = _obs
if self.use_recurrent:
mem_in = mini_batch['memory'][:, 0, :]
feed_dict[self.model.memory_in] = mem_in
self.has_updated = True
run_out = self._execute_model(feed_dict, self.update_dict)
return run_out | [
"\n Updates model using buffer.\n :param num_sequences: Number of trajectories in batch.\n :param mini_batch: Experience batch.\n :return: Output from update process.\n "
] |
Please provide a description of the function:def get_intrinsic_rewards(self, curr_info, next_info):
if self.use_curiosity:
if len(curr_info.agents) == 0:
return []
feed_dict = {self.model.batch_size: len(next_info.vector_observations),
self.model.sequence_length: 1}
if self.use_continuous_act:
feed_dict[self.model.selected_actions] = next_info.previous_vector_actions
else:
feed_dict[self.model.action_holder] = next_info.previous_vector_actions
for i in range(self.model.vis_obs_size):
feed_dict[self.model.visual_in[i]] = curr_info.visual_observations[i]
feed_dict[self.model.next_visual_in[i]] = next_info.visual_observations[i]
if self.use_vec_obs:
feed_dict[self.model.vector_in] = curr_info.vector_observations
feed_dict[self.model.next_vector_in] = next_info.vector_observations
if self.use_recurrent:
if curr_info.memories.shape[1] == 0:
curr_info.memories = self.make_empty_memory(len(curr_info.agents))
feed_dict[self.model.memory_in] = curr_info.memories
intrinsic_rewards = self.sess.run(self.model.intrinsic_reward,
feed_dict=feed_dict) * float(self.has_updated)
return intrinsic_rewards
else:
return None | [
"\n Generates intrinsic reward used for Curiosity-based training.\n :BrainInfo curr_info: Current BrainInfo.\n :BrainInfo next_info: Next BrainInfo.\n :return: Intrinsic rewards for all agents.\n "
] |
Please provide a description of the function:def get_value_estimate(self, brain_info, idx):
feed_dict = {self.model.batch_size: 1, self.model.sequence_length: 1}
for i in range(len(brain_info.visual_observations)):
feed_dict[self.model.visual_in[i]] = [brain_info.visual_observations[i][idx]]
if self.use_vec_obs:
feed_dict[self.model.vector_in] = [brain_info.vector_observations[idx]]
if self.use_recurrent:
if brain_info.memories.shape[1] == 0:
brain_info.memories = self.make_empty_memory(len(brain_info.agents))
feed_dict[self.model.memory_in] = [brain_info.memories[idx]]
if not self.use_continuous_act and self.use_recurrent:
feed_dict[self.model.prev_action] = brain_info.previous_vector_actions[idx].reshape(
[-1, len(self.model.act_size)])
value_estimate = self.sess.run(self.model.value, feed_dict)
return value_estimate | [
"\n Generates value estimates for bootstrapping.\n :param brain_info: BrainInfo to be used for bootstrapping.\n :param idx: Index in BrainInfo of agent.\n :return: Value estimate.\n "
] |
Please provide a description of the function:def update_reward(self, new_reward):
self.sess.run(self.model.update_reward,
feed_dict={self.model.new_reward: new_reward}) | [
"\n Updates reward value for policy.\n :param new_reward: New reward to save.\n "
] |
Please provide a description of the function:def add_experiences(self, curr_info: AllBrainInfo, next_info: AllBrainInfo,
take_action_outputs):
# Used to collect information about student performance.
info_student = curr_info[self.brain_name]
next_info_student = next_info[self.brain_name]
for agent_id in info_student.agents:
self.evaluation_buffer[agent_id].last_brain_info = info_student
for agent_id in next_info_student.agents:
stored_info_student = self.evaluation_buffer[agent_id].last_brain_info
if stored_info_student is None:
continue
else:
next_idx = next_info_student.agents.index(agent_id)
if agent_id not in self.cumulative_rewards:
self.cumulative_rewards[agent_id] = 0
self.cumulative_rewards[agent_id] += next_info_student.rewards[next_idx]
if not next_info_student.local_done[next_idx]:
if agent_id not in self.episode_steps:
self.episode_steps[agent_id] = 0
self.episode_steps[agent_id] += 1 | [
"\n Adds experiences to each agent's experience history.\n :param curr_info: Current AllBrainInfo (Dictionary of all current brains and corresponding BrainInfo).\n :param next_info: Next AllBrainInfo (Dictionary of all current brains and corresponding BrainInfo).\n :param take_action_outputs: The outputs of the take action method.\n "
] |
Please provide a description of the function:def process_experiences(self, current_info: AllBrainInfo, next_info: AllBrainInfo):
info_student = next_info[self.brain_name]
for l in range(len(info_student.agents)):
if info_student.local_done[l]:
agent_id = info_student.agents[l]
self.stats['Environment/Cumulative Reward'].append(
self.cumulative_rewards.get(agent_id, 0))
self.stats['Environment/Episode Length'].append(
self.episode_steps.get(agent_id, 0))
self.cumulative_rewards[agent_id] = 0
self.episode_steps[agent_id] = 0 | [
"\n Checks agent histories for processing condition, and processes them as necessary.\n Processing involves calculating value and advantage targets for model updating step.\n :param current_info: Current AllBrainInfo\n :param next_info: Next AllBrainInfo\n "
] |
Please provide a description of the function:def end_episode(self):
self.evaluation_buffer.reset_local_buffers()
for agent_id in self.cumulative_rewards:
self.cumulative_rewards[agent_id] = 0
for agent_id in self.episode_steps:
self.episode_steps[agent_id] = 0 | [
"\n A signal that the Episode has ended. The buffer must be reset.\n Get only called when the academy resets.\n "
] |
Please provide a description of the function:def update_policy(self):
self.demonstration_buffer.update_buffer.shuffle()
batch_losses = []
num_batches = min(len(self.demonstration_buffer.update_buffer['actions']) //
self.n_sequences, self.batches_per_epoch)
for i in range(num_batches):
update_buffer = self.demonstration_buffer.update_buffer
start = i * self.n_sequences
end = (i + 1) * self.n_sequences
mini_batch = update_buffer.make_mini_batch(start, end)
run_out = self.policy.update(mini_batch, self.n_sequences)
loss = run_out['policy_loss']
batch_losses.append(loss)
if len(batch_losses) > 0:
self.stats['Losses/Cloning Loss'].append(np.mean(batch_losses))
else:
self.stats['Losses/Cloning Loss'].append(0) | [
"\n Updates the policy.\n "
] |
Please provide a description of the function:def create_global_steps():
global_step = tf.Variable(0, name="global_step", trainable=False, dtype=tf.int32)
increment_step = tf.assign(global_step, tf.add(global_step, 1))
return global_step, increment_step | [
"Creates TF ops to track and increment global training step."
] |
Please provide a description of the function:def create_visual_input(camera_parameters, name):
o_size_h = camera_parameters['height']
o_size_w = camera_parameters['width']
bw = camera_parameters['blackAndWhite']
if bw:
c_channels = 1
else:
c_channels = 3
visual_in = tf.placeholder(shape=[None, o_size_h, o_size_w, c_channels], dtype=tf.float32,
name=name)
return visual_in | [
"\n Creates image input op.\n :param camera_parameters: Parameters for visual observation from BrainInfo.\n :param name: Desired name of input op.\n :return: input op.\n "
] |
Please provide a description of the function:def create_vector_input(self, name='vector_observation'):
self.vector_in = tf.placeholder(shape=[None, self.vec_obs_size], dtype=tf.float32,
name=name)
if self.normalize:
self.running_mean = tf.get_variable("running_mean", [self.vec_obs_size],
trainable=False, dtype=tf.float32,
initializer=tf.zeros_initializer())
self.running_variance = tf.get_variable("running_variance", [self.vec_obs_size],
trainable=False,
dtype=tf.float32,
initializer=tf.ones_initializer())
self.update_mean, self.update_variance = self.create_normalizer_update(self.vector_in)
self.normalized_state = tf.clip_by_value((self.vector_in - self.running_mean) / tf.sqrt(
self.running_variance / (tf.cast(self.global_step, tf.float32) + 1)), -5, 5,
name="normalized_state")
return self.normalized_state
else:
return self.vector_in | [
"\n Creates ops for vector observation input.\n :param name: Name of the placeholder op.\n :param vec_obs_size: Size of stacked vector observation.\n :return:\n "
] |
Please provide a description of the function:def create_vector_observation_encoder(observation_input, h_size, activation, num_layers, scope,
reuse):
with tf.variable_scope(scope):
hidden = observation_input
for i in range(num_layers):
hidden = tf.layers.dense(hidden, h_size, activation=activation, reuse=reuse,
name="hidden_{}".format(i),
kernel_initializer=c_layers.variance_scaling_initializer(
1.0))
return hidden | [
"\n Builds a set of hidden state encoders.\n :param reuse: Whether to re-use the weights within the same scope.\n :param scope: Graph scope for the encoder ops.\n :param observation_input: Input vector.\n :param h_size: Hidden layer size.\n :param activation: What type of activation function to use for layers.\n :param num_layers: number of hidden layers to create.\n :return: List of hidden layer tensors.\n "
] |
Please provide a description of the function:def create_visual_observation_encoder(self, image_input, h_size, activation, num_layers, scope,
reuse):
with tf.variable_scope(scope):
conv1 = tf.layers.conv2d(image_input, 16, kernel_size=[8, 8], strides=[4, 4],
activation=tf.nn.elu, reuse=reuse, name="conv_1")
conv2 = tf.layers.conv2d(conv1, 32, kernel_size=[4, 4], strides=[2, 2],
activation=tf.nn.elu, reuse=reuse, name="conv_2")
hidden = c_layers.flatten(conv2)
with tf.variable_scope(scope + '/' + 'flat_encoding'):
hidden_flat = self.create_vector_observation_encoder(hidden, h_size, activation,
num_layers, scope, reuse)
return hidden_flat | [
"\n Builds a set of visual (CNN) encoders.\n :param reuse: Whether to re-use the weights within the same scope.\n :param scope: The scope of the graph within which to create the ops.\n :param image_input: The placeholder for the image input to use.\n :param h_size: Hidden layer size.\n :param activation: What type of activation function to use for layers.\n :param num_layers: number of hidden layers to create.\n :return: List of hidden layer tensors.\n "
] |
Please provide a description of the function:def create_discrete_action_masking_layer(all_logits, action_masks, action_size):
action_idx = [0] + list(np.cumsum(action_size))
branches_logits = [all_logits[:, action_idx[i]:action_idx[i + 1]] for i in range(len(action_size))]
branch_masks = [action_masks[:, action_idx[i]:action_idx[i + 1]] for i in range(len(action_size))]
raw_probs = [tf.multiply(tf.nn.softmax(branches_logits[k]) + 1.0e-10, branch_masks[k])
for k in range(len(action_size))]
normalized_probs = [
tf.divide(raw_probs[k], tf.reduce_sum(raw_probs[k], axis=1, keepdims=True))
for k in range(len(action_size))]
output = tf.concat([tf.multinomial(tf.log(normalized_probs[k]), 1) for k in range(len(action_size))], axis=1)
return output, tf.concat([tf.log(normalized_probs[k] + 1.0e-10) for k in range(len(action_size))], axis=1) | [
"\n Creates a masking layer for the discrete actions\n :param all_logits: The concatenated unnormalized action probabilities for all branches\n :param action_masks: The mask for the logits. Must be of dimension [None x total_number_of_action]\n :param action_size: A list containing the number of possible actions for each branch\n :return: The action output dimension [batch_size, num_branches] and the concatenated normalized logits\n "
] |
Please provide a description of the function:def create_observation_streams(self, num_streams, h_size, num_layers):
brain = self.brain
activation_fn = self.swish
self.visual_in = []
for i in range(brain.number_visual_observations):
visual_input = self.create_visual_input(brain.camera_resolutions[i],
name="visual_observation_" + str(i))
self.visual_in.append(visual_input)
vector_observation_input = self.create_vector_input()
final_hiddens = []
for i in range(num_streams):
visual_encoders = []
hidden_state, hidden_visual = None, None
if self.vis_obs_size > 0:
for j in range(brain.number_visual_observations):
encoded_visual = self.create_visual_observation_encoder(self.visual_in[j],
h_size,
activation_fn,
num_layers,
"main_graph_{}_encoder{}"
.format(i, j), False)
visual_encoders.append(encoded_visual)
hidden_visual = tf.concat(visual_encoders, axis=1)
if brain.vector_observation_space_size > 0:
hidden_state = self.create_vector_observation_encoder(vector_observation_input,
h_size, activation_fn,
num_layers,
"main_graph_{}".format(i),
False)
if hidden_state is not None and hidden_visual is not None:
final_hidden = tf.concat([hidden_visual, hidden_state], axis=1)
elif hidden_state is None and hidden_visual is not None:
final_hidden = hidden_visual
elif hidden_state is not None and hidden_visual is None:
final_hidden = hidden_state
else:
raise Exception("No valid network configuration possible. "
"There are no states or observations in this brain")
final_hiddens.append(final_hidden)
return final_hiddens | [
"\n Creates encoding stream for observations.\n :param num_streams: Number of streams to create.\n :param h_size: Size of hidden linear layers in stream.\n :param num_layers: Number of hidden linear layers in stream.\n :return: List of encoded streams.\n "
] |
Please provide a description of the function:def create_recurrent_encoder(input_state, memory_in, sequence_length, name='lstm'):
s_size = input_state.get_shape().as_list()[1]
m_size = memory_in.get_shape().as_list()[1]
lstm_input_state = tf.reshape(input_state, shape=[-1, sequence_length, s_size])
memory_in = tf.reshape(memory_in[:, :], [-1, m_size])
_half_point = int(m_size / 2)
with tf.variable_scope(name):
rnn_cell = tf.contrib.rnn.BasicLSTMCell(_half_point)
lstm_vector_in = tf.contrib.rnn.LSTMStateTuple(memory_in[:, :_half_point],
memory_in[:, _half_point:])
recurrent_output, lstm_state_out = tf.nn.dynamic_rnn(rnn_cell, lstm_input_state,
initial_state=lstm_vector_in)
recurrent_output = tf.reshape(recurrent_output, shape=[-1, _half_point])
return recurrent_output, tf.concat([lstm_state_out.c, lstm_state_out.h], axis=1) | [
"\n Builds a recurrent encoder for either state or observations (LSTM).\n :param sequence_length: Length of sequence to unroll.\n :param input_state: The input tensor to the LSTM cell.\n :param memory_in: The input memory to the LSTM cell.\n :param name: The scope of the LSTM cell.\n "
] |
Please provide a description of the function:def create_cc_actor_critic(self, h_size, num_layers):
hidden_streams = self.create_observation_streams(2, h_size, num_layers)
if self.use_recurrent:
self.memory_in = tf.placeholder(shape=[None, self.m_size], dtype=tf.float32,
name='recurrent_in')
_half_point = int(self.m_size / 2)
hidden_policy, memory_policy_out = self.create_recurrent_encoder(
hidden_streams[0], self.memory_in[:, :_half_point], self.sequence_length,
name='lstm_policy')
hidden_value, memory_value_out = self.create_recurrent_encoder(
hidden_streams[1], self.memory_in[:, _half_point:], self.sequence_length,
name='lstm_value')
self.memory_out = tf.concat([memory_policy_out, memory_value_out], axis=1,
name='recurrent_out')
else:
hidden_policy = hidden_streams[0]
hidden_value = hidden_streams[1]
mu = tf.layers.dense(hidden_policy, self.act_size[0], activation=None,
kernel_initializer=c_layers.variance_scaling_initializer(factor=0.01))
log_sigma_sq = tf.get_variable("log_sigma_squared", [self.act_size[0]], dtype=tf.float32,
initializer=tf.zeros_initializer())
sigma_sq = tf.exp(log_sigma_sq)
self.epsilon = tf.placeholder(shape=[None, self.act_size[0]], dtype=tf.float32, name='epsilon')
# Clip and scale output to ensure actions are always within [-1, 1] range.
self.output_pre = mu + tf.sqrt(sigma_sq) * self.epsilon
output_post = tf.clip_by_value(self.output_pre, -3, 3) / 3
self.output = tf.identity(output_post, name='action')
self.selected_actions = tf.stop_gradient(output_post)
# Compute probability of model output.
all_probs = - 0.5 * tf.square(tf.stop_gradient(self.output_pre) - mu) / sigma_sq \
- 0.5 * tf.log(2.0 * np.pi) - 0.5 * log_sigma_sq
self.all_log_probs = tf.identity(all_probs, name='action_probs')
self.entropy = 0.5 * tf.reduce_mean(tf.log(2 * np.pi * np.e) + log_sigma_sq)
value = tf.layers.dense(hidden_value, 1, activation=None)
self.value = tf.identity(value, name="value_estimate")
self.all_old_log_probs = tf.placeholder(shape=[None, self.act_size[0]], dtype=tf.float32,
name='old_probabilities')
# We keep these tensors the same name, but use new nodes to keep code parallelism with discrete control.
self.log_probs = tf.reduce_sum((tf.identity(self.all_log_probs)), axis=1, keepdims=True)
self.old_log_probs = tf.reduce_sum((tf.identity(self.all_old_log_probs)), axis=1,
keepdims=True) | [
"\n Creates Continuous control actor-critic model.\n :param h_size: Size of hidden linear layers.\n :param num_layers: Number of hidden linear layers.\n "
] |
Please provide a description of the function:def create_dc_actor_critic(self, h_size, num_layers):
hidden_streams = self.create_observation_streams(1, h_size, num_layers)
hidden = hidden_streams[0]
if self.use_recurrent:
self.prev_action = tf.placeholder(shape=[None, len(self.act_size)], dtype=tf.int32,
name='prev_action')
prev_action_oh = tf.concat([
tf.one_hot(self.prev_action[:, i], self.act_size[i]) for i in
range(len(self.act_size))], axis=1)
hidden = tf.concat([hidden, prev_action_oh], axis=1)
self.memory_in = tf.placeholder(shape=[None, self.m_size], dtype=tf.float32,
name='recurrent_in')
hidden, memory_out = self.create_recurrent_encoder(hidden, self.memory_in,
self.sequence_length)
self.memory_out = tf.identity(memory_out, name='recurrent_out')
policy_branches = []
for size in self.act_size:
policy_branches.append(tf.layers.dense(hidden, size, activation=None, use_bias=False,
kernel_initializer=c_layers.variance_scaling_initializer(factor=0.01)))
self.all_log_probs = tf.concat([branch for branch in policy_branches], axis=1, name="action_probs")
self.action_masks = tf.placeholder(shape=[None, sum(self.act_size)], dtype=tf.float32, name="action_masks")
output, normalized_logits = self.create_discrete_action_masking_layer(
self.all_log_probs, self.action_masks, self.act_size)
self.output = tf.identity(output)
self.normalized_logits = tf.identity(normalized_logits, name='action')
value = tf.layers.dense(hidden, 1, activation=None)
self.value = tf.identity(value, name="value_estimate")
self.action_holder = tf.placeholder(
shape=[None, len(policy_branches)], dtype=tf.int32, name="action_holder")
self.action_oh = tf.concat([
tf.one_hot(self.action_holder[:, i], self.act_size[i]) for i in range(len(self.act_size))], axis=1)
self.selected_actions = tf.stop_gradient(self.action_oh)
self.all_old_log_probs = tf.placeholder(
shape=[None, sum(self.act_size)], dtype=tf.float32, name='old_probabilities')
_, old_normalized_logits = self.create_discrete_action_masking_layer(
self.all_old_log_probs, self.action_masks, self.act_size)
action_idx = [0] + list(np.cumsum(self.act_size))
self.entropy = tf.reduce_sum((tf.stack([
tf.nn.softmax_cross_entropy_with_logits_v2(
labels=tf.nn.softmax(self.all_log_probs[:, action_idx[i]:action_idx[i + 1]]),
logits=self.all_log_probs[:, action_idx[i]:action_idx[i + 1]])
for i in range(len(self.act_size))], axis=1)), axis=1)
self.log_probs = tf.reduce_sum((tf.stack([
-tf.nn.softmax_cross_entropy_with_logits_v2(
labels=self.action_oh[:, action_idx[i]:action_idx[i + 1]],
logits=normalized_logits[:, action_idx[i]:action_idx[i + 1]]
)
for i in range(len(self.act_size))], axis=1)), axis=1, keepdims=True)
self.old_log_probs = tf.reduce_sum((tf.stack([
-tf.nn.softmax_cross_entropy_with_logits_v2(
labels=self.action_oh[:, action_idx[i]:action_idx[i + 1]],
logits=old_normalized_logits[:, action_idx[i]:action_idx[i + 1]]
)
for i in range(len(self.act_size))], axis=1)), axis=1, keepdims=True) | [
"\n Creates Discrete control actor-critic model.\n :param h_size: Size of hidden linear layers.\n :param num_layers: Number of hidden linear layers.\n "
] |
Please provide a description of the function:def add_experiences(self, curr_info: AllBrainInfo, next_info: AllBrainInfo,
take_action_outputs):
# Used to collect teacher experience into training buffer
info_teacher = curr_info[self.brain_to_imitate]
next_info_teacher = next_info[self.brain_to_imitate]
for agent_id in info_teacher.agents:
self.demonstration_buffer[agent_id].last_brain_info = info_teacher
for agent_id in next_info_teacher.agents:
stored_info_teacher = self.demonstration_buffer[agent_id].last_brain_info
if stored_info_teacher is None:
continue
else:
idx = stored_info_teacher.agents.index(agent_id)
next_idx = next_info_teacher.agents.index(agent_id)
if stored_info_teacher.text_observations[idx] != "":
info_teacher_record, info_teacher_reset = \
stored_info_teacher.text_observations[idx].lower().split(",")
next_info_teacher_record, next_info_teacher_reset = \
next_info_teacher.text_observations[idx]. \
lower().split(",")
if next_info_teacher_reset == "true":
self.demonstration_buffer.reset_update_buffer()
else:
info_teacher_record, next_info_teacher_record = "true", "true"
if info_teacher_record == "true" and next_info_teacher_record == "true":
if not stored_info_teacher.local_done[idx]:
for i in range(self.policy.vis_obs_size):
self.demonstration_buffer[agent_id]['visual_obs%d' % i] \
.append(stored_info_teacher.visual_observations[i][idx])
if self.policy.use_vec_obs:
self.demonstration_buffer[agent_id]['vector_obs'] \
.append(stored_info_teacher.vector_observations[idx])
if self.policy.use_recurrent:
if stored_info_teacher.memories.shape[1] == 0:
stored_info_teacher.memories = np.zeros(
(len(stored_info_teacher.agents),
self.policy.m_size))
self.demonstration_buffer[agent_id]['memory'].append(
stored_info_teacher.memories[idx])
self.demonstration_buffer[agent_id]['actions'].append(
next_info_teacher.previous_vector_actions[next_idx])
super(OnlineBCTrainer, self).add_experiences(curr_info, next_info, take_action_outputs) | [
"\n Adds experiences to each agent's experience history.\n :param curr_info: Current AllBrainInfo (Dictionary of all current brains and corresponding BrainInfo).\n :param next_info: Next AllBrainInfo (Dictionary of all current brains and corresponding BrainInfo).\n :param take_action_outputs: The outputs of the take action method.\n "
] |
Please provide a description of the function:def process_experiences(self, current_info: AllBrainInfo, next_info: AllBrainInfo):
info_teacher = next_info[self.brain_to_imitate]
for l in range(len(info_teacher.agents)):
teacher_action_list = len(self.demonstration_buffer[info_teacher.agents[l]]['actions'])
horizon_reached = teacher_action_list > self.trainer_parameters['time_horizon']
teacher_filled = len(self.demonstration_buffer[info_teacher.agents[l]]['actions']) > 0
if (info_teacher.local_done[l] or horizon_reached) and teacher_filled:
agent_id = info_teacher.agents[l]
self.demonstration_buffer.append_update_buffer(
agent_id, batch_size=None, training_length=self.policy.sequence_length)
self.demonstration_buffer[agent_id].reset_agent()
super(OnlineBCTrainer, self).process_experiences(current_info, next_info) | [
"\n Checks agent histories for processing condition, and processes them as necessary.\n Processing involves calculating value and advantage targets for model updating step.\n :param current_info: Current AllBrainInfo\n :param next_info: Next AllBrainInfo\n "
] |
Please provide a description of the function:def flatten(items,enter=lambda x:isinstance(x, list)):
# http://stackoverflow.com/a/40857703
# https://github.com/ctmakro/canton/blob/master/canton/misc.py
for x in items:
if enter(x):
yield from flatten(x)
else:
yield x | [
"Yield items from any nested iterable; see REF."
] |
Please provide a description of the function:def replace_strings_in_list(array_of_strigs, replace_with_strings):
"A value in replace_with_strings can be either single string or list of strings"
potentially_nested_list = [replace_with_strings.get(s) or s for s in array_of_strigs]
return list(flatten(potentially_nested_list)) | [] |
Please provide a description of the function:def remove_duplicates_from_list(array):
"Preserves the order of elements in the list"
output = []
unique = set()
for a in array:
if a not in unique:
unique.add(a)
output.append(a)
return output | [] |
Please provide a description of the function:def pool_to_HW(shape, data_frmt):
if len(shape) != 4:
return shape # Not NHWC|NCHW, return as is
if data_frmt == 'NCHW':
return [shape[2], shape[3]]
return [shape[1], shape[2]] | [
" Convert from NHWC|NCHW => HW\n "
] |
Please provide a description of the function:def convert(source_file, target_file, trim_unused_by_output="", verbose=False, compress_f16=False):
if (type(verbose)==bool):
args = Struct()
args.verbose = verbose
args.print_layers = verbose
args.print_source_json = verbose
args.print_barracuda_json = verbose
args.print_layer_links = verbose
args.print_patterns = verbose
args.print_tensors = verbose
else:
args = verbose
# Load Tensorflow model
print("Converting %s to %s" % (source_file, target_file))
f = open(source_file, 'rb')
i_model = tf.GraphDef()
i_model.ParseFromString(f.read())
if args.verbose:
print('OP_TYPES:', {layer.op for layer in i_model.node})
if args.print_source_json or args.verbose:
for layer in i_model.node:
if not layer.op == 'Const':
print('MODEL:', MessageToJson(layer) + ",")
# Convert
o_model = barracuda.Model()
o_model.layers, o_input_shapes, o_model.tensors, o_model.memories = \
process_model(i_model, args)
# Cleanup unconnected Identities (they might linger after processing complex node patterns like LSTM)
def cleanup_layers(layers):
all_layers = {l.name for l in layers}
all_inputs = {i for l in layers for i in l.inputs}
def is_unconnected_identity(layer):
if layer.class_name == 'Activation' and layer.activation == 0: # Identity
assert(len(layer.inputs) == 1)
if layer.inputs[0] not in all_layers and layer.name not in all_inputs:
return True;
return False;
return [l for l in layers if not is_unconnected_identity(l)]
o_model.layers = cleanup_layers(o_model.layers)
all_inputs = {i for l in o_model.layers for i in l.inputs}
embedded_tensors = {t.name for l in o_model.layers for t in l.tensors}
# Find global tensors
def dims_to_barracuda_shape(dims):
shape = list(dims)
while len(shape) < 4:
shape = [1] + shape
return shape
o_model.globals = [t for t in o_model.tensors if t not in all_inputs and t not in embedded_tensors]
#for x in global_tensors:
# shape = dims_to_barracuda_shape(get_tensor_dims(o_model.tensors[x]))
# o_globals += [Struct(
# name = x,
# shape = shape,
# data = np.reshape(get_tensor_data(o_model.tensors[x]), shape).astype(np.float32))]
# Trim
if trim_unused_by_output:
o_model.layers = barracuda.trim(o_model.layers, trim_unused_by_output, args.verbose)
# Create load layers for constants
const_tensors = [i for i in all_inputs if i in o_model.tensors]
const_tensors += o_model.globals
for x in const_tensors:
shape = dims_to_barracuda_shape(get_tensor_dims(o_model.tensors[x]))
o_l = Struct(
type = 255, # Load
class_name = "Const",
name = x,
pads = [0,0,0,0],
strides = [],
pool_size = [],
axis = -1,
alpha = 1,
beta = 0,
activation = 0,
inputs = [],
tensors = [Struct(
name = x,
shape = shape,
data = np.reshape(get_tensor_data(o_model.tensors[x]), shape).astype(np.float32))]
)
o_model.layers.insert(0, o_l)
# Find model inputs & outputs
all_layers = {l.name for l in o_model.layers}
# global inputs => are inputs that are NOT connected to any layer in the network
# global outputs => are outputs that are NOT feeding any layer in the network OR are coming from Identity layers
o_model.inputs = {i:o_input_shapes[i] for l in o_model.layers for i in l.inputs if i not in all_layers and i not in o_model.memories}
def is_output_layer(layer):
if layer.class_name == 'Const': # Constants never count as global output even when unconnected
return False;
if layer.name not in all_inputs: # this layer is not inputing to any other layer
return True
if layer.class_name == 'Activation' and layer.activation == 0: # Identity marks global output
return True
return False
o_model.outputs = [l.name for l in o_model.layers if is_output_layer(l)]
# Compress
if compress_f16:
o_model = barracuda.compress(o_model)
# Sort model so that layer inputs are always ready upfront
o_model.layers = barracuda.sort(o_model.layers, o_model.inputs, o_model.memories, args.verbose)
# Summary
barracuda.summary(o_model,
print_layer_links = args.print_layer_links or args.verbose,
print_barracuda_json = args.print_barracuda_json or args.verbose,
print_tensors = args.print_tensors or args.verbose)
# Write to file
barracuda.write(o_model, target_file)
print('DONE: wrote', target_file, 'file.') | [
"\n Converts a TensorFlow model into a Barracuda model.\n :param source_file: The TensorFlow Model\n :param target_file: The name of the file the converted model will be saved to\n :param trim_unused_by_output: The regexp to match output nodes to remain in the model. All other uconnected nodes will be removed.\n :param verbose: If True, will display debug messages\n :param compress_f16: If true, the float values will be converted to f16\n :return:\n "
] |
Please provide a description of the function:def demo_to_buffer(file_path, sequence_length):
brain_params, brain_infos, _ = load_demonstration(file_path)
demo_buffer = make_demo_buffer(brain_infos, brain_params, sequence_length)
return brain_params, demo_buffer | [
"\n Loads demonstration file and uses it to fill training buffer.\n :param file_path: Location of demonstration file (.demo).\n :param sequence_length: Length of trajectories to fill buffer.\n :return:\n "
] |
Please provide a description of the function:def load_demonstration(file_path):
# First 32 bytes of file dedicated to meta-data.
INITIAL_POS = 33
if not os.path.isfile(file_path):
raise FileNotFoundError("The demonstration file {} does not exist.".format(file_path))
file_extension = pathlib.Path(file_path).suffix
if file_extension != '.demo':
raise ValueError("The file is not a '.demo' file. Please provide a file with the "
"correct extension.")
brain_params = None
brain_infos = []
data = open(file_path, "rb").read()
next_pos, pos, obs_decoded = 0, 0, 0
total_expected = 0
while pos < len(data):
next_pos, pos = _DecodeVarint32(data, pos)
if obs_decoded == 0:
meta_data_proto = DemonstrationMetaProto()
meta_data_proto.ParseFromString(data[pos:pos + next_pos])
total_expected = meta_data_proto.number_steps
pos = INITIAL_POS
if obs_decoded == 1:
brain_param_proto = BrainParametersProto()
brain_param_proto.ParseFromString(data[pos:pos + next_pos])
brain_params = BrainParameters.from_proto(brain_param_proto)
pos += next_pos
if obs_decoded > 1:
agent_info = AgentInfoProto()
agent_info.ParseFromString(data[pos:pos + next_pos])
brain_info = BrainInfo.from_agent_proto([agent_info], brain_params)
brain_infos.append(brain_info)
if len(brain_infos) == total_expected:
break
pos += next_pos
obs_decoded += 1
return brain_params, brain_infos, total_expected | [
"\n Loads and parses a demonstration file.\n :param file_path: Location of demonstration file (.demo).\n :return: BrainParameter and list of BrainInfos containing demonstration data.\n "
] |
Please provide a description of the function:def _save_model(self, steps=0):
for brain_name in self.trainers.keys():
self.trainers[brain_name].save_model()
self.logger.info('Saved Model') | [
"\n Saves current model to checkpoint folder.\n :param steps: Current number of steps in training process.\n :param saver: Tensorflow saver for session.\n "
] |
Please provide a description of the function:def _write_training_metrics(self):
for brain_name in self.trainers.keys():
if brain_name in self.trainer_metrics:
self.trainers[brain_name].write_training_metrics() | [
"\n Write all CSV metrics\n :return:\n "
] |
Please provide a description of the function:def _export_graph(self):
for brain_name in self.trainers.keys():
self.trainers[brain_name].export_model() | [
"\n Exports latest saved models to .nn format for Unity embedding.\n "
] |
Please provide a description of the function:def initialize_trainers(self, trainer_config: Dict[str, Dict[str, str]]):
trainer_parameters_dict = {}
for brain_name in self.external_brains:
trainer_parameters = trainer_config['default'].copy()
trainer_parameters['summary_path'] = '{basedir}/{name}'.format(
basedir=self.summaries_dir,
name=str(self.run_id) + '_' + brain_name)
trainer_parameters['model_path'] = '{basedir}/{name}'.format(
basedir=self.model_path,
name=brain_name)
trainer_parameters['keep_checkpoints'] = self.keep_checkpoints
if brain_name in trainer_config:
_brain_key = brain_name
while not isinstance(trainer_config[_brain_key], dict):
_brain_key = trainer_config[_brain_key]
for k in trainer_config[_brain_key]:
trainer_parameters[k] = trainer_config[_brain_key][k]
trainer_parameters_dict[brain_name] = trainer_parameters.copy()
for brain_name in self.external_brains:
if trainer_parameters_dict[brain_name]['trainer'] == 'offline_bc':
self.trainers[brain_name] = OfflineBCTrainer(
self.external_brains[brain_name],
trainer_parameters_dict[brain_name], self.train_model,
self.load_model, self.seed, self.run_id)
elif trainer_parameters_dict[brain_name]['trainer'] == 'online_bc':
self.trainers[brain_name] = OnlineBCTrainer(
self.external_brains[brain_name],
trainer_parameters_dict[brain_name], self.train_model,
self.load_model, self.seed, self.run_id)
elif trainer_parameters_dict[brain_name]['trainer'] == 'ppo':
self.trainers[brain_name] = PPOTrainer(
self.external_brains[brain_name],
self.meta_curriculum
.brains_to_curriculums[brain_name]
.min_lesson_length if self.meta_curriculum else 0,
trainer_parameters_dict[brain_name],
self.train_model, self.load_model, self.seed,
self.run_id)
self.trainer_metrics[brain_name] = self.trainers[brain_name].trainer_metrics
else:
raise UnityEnvironmentException('The trainer config contains '
'an unknown trainer type for '
'brain {}'
.format(brain_name)) | [
"\n Initialization of the trainers\n :param trainer_config: The configurations of the trainers\n "
] |
Please provide a description of the function:def _reset_env(self, env: BaseUnityEnvironment):
if self.meta_curriculum is not None:
return env.reset(train_mode=self.fast_simulation, config=self.meta_curriculum.get_config())
else:
return env.reset(train_mode=self.fast_simulation) | [
"Resets the environment.\n\n Returns:\n A Data structure corresponding to the initial reset state of the\n environment.\n "
] |
Please provide a description of the function:def close(self):
if self._socket is not None and self._conn is not None:
message_input = UnityMessage()
message_input.header.status = 400
self._communicator_send(message_input.SerializeToString())
if self._socket is not None:
self._socket.close()
self._socket = None
if self._socket is not None:
self._conn.close()
self._conn = None | [
"\n Sends a shutdown signal to the unity environment, and closes the socket connection.\n "
] |
Please provide a description of the function:def fuse_batchnorm_weights(gamma, beta, mean, var, epsilon):
# https://github.com/Tencent/ncnn/blob/master/src/layer/batchnorm.cpp
scale = gamma / np.sqrt(var + epsilon)
bias = beta - gamma * mean / np.sqrt(var + epsilon)
return [scale, bias] | [
" float sqrt_var = sqrt(var_data[i]);\n a_data[i] = bias_data[i] - slope_data[i] * mean_data[i] / sqrt_var;\n b_data[i] = slope_data[i] / sqrt_var;\n ...\n ptr[i] = b * ptr[i] + a;\n "
] |
Please provide a description of the function:def rnn(name, input, state, kernel, bias, new_state, number_of_gates = 2):
''' - Ht = f(Xt*Wi + Ht_1*Ri + Wbi + Rbi)
'''
nn = Build(name)
nn.tanh(
nn.mad(kernel=kernel, bias=bias,
x=nn.concat(input, state)),
out=new_state);
return nn.layers; | [] |
Please provide a description of the function:def gru(name, input, state, kernel_r, kernel_u, kernel_c, bias_r, bias_u, bias_c, new_state, number_of_gates = 2):
''' - zt = f(Xt*Wz + Ht_1*Rz + Wbz + Rbz)
- rt = f(Xt*Wr + Ht_1*Rr + Wbr + Rbr)
- ht = g(Xt*Wh + (rt . Ht_1)*Rh + Rbh + Wbh)
- Ht = (1-zt).ht + zt.Ht_1
'''
nn = Build(name)
inputs = nn.concat(input, state)
u = nn.sigmoid(nn.mad(inputs, kernel_u, bias_u))
r = nn.sigmoid(nn.mad(inputs, kernel_r, bias_r))
r_state = nn.mul(r, state)
c = nn.tanh(nn.mad(kernel=kernel_c, bias=bias_c,
x=nn.concat(input, r_state)))
# new_h = u' * state + (1 - u') * c'
# = u' * state + c' - u' * c'
# u' * state + c'
nn.add(nn.mul(u, state), c)
# - u' * c'
nn.sub(nn._, nn.mul(u, c),
out=new_state)
return nn.layers; | [] |
Please provide a description of the function:def lstm(name, input, state_c, state_h, kernel_i, kernel_j, kernel_f, kernel_o, bias_i, bias_j, bias_f, bias_o, new_state_c, new_state_h):
''' Full:
- it = f(Xt*Wi + Ht_1*Ri + Pi . Ct_1 + Wbi + Rbi)
- ft = f(Xt*Wf + Ht_1*Rf + Pf . Ct_1 + Wbf + Rbf)
- ct = g(Xt*Wc + Ht_1*Rc + Wbc + Rbc)
- Ct = ft . Ct_1 + it . ct
- ot = f(Xt*Wo + Ht_1*Ro + Po . Ct + Wbo + Rbo)
- Ht = ot . h(Ct)
'''
''' No peephole:
- it = f(Xt*Wi + Ht_1*Ri + Wbi + Rbi)
- ft = f(Xt*Wf + Ht_1*Rf + Wbf + Rbf)
- ct = g(Xt*Wc + Ht_1*Rc + Wbc + Rbc)
- Ct = ft . Ct_ + it . ct
- ot = f(Xt*Wo + Ht_1*Ro + Wbo + Rbo)
- Ht = ot . h(Ct)
'''
nn = Build(name)
inputs = nn.concat(input, state_h)
i = nn.sigmoid(nn.mad(x=inputs, kernel=kernel_i, bias=bias_i))
j = nn.tanh(nn.mad(inputs, kernel_j, bias_j))
f = nn.sigmoid(nn.mad(inputs, kernel_f, bias_f))
o = nn.sigmoid(nn.mad(inputs, kernel_o, bias_o))
# new_c = state_c * f' + i' * j'
nn.add(
nn.mul(state_c, f), nn.mul(i, j),
out=new_state_c)
# new_h =
nn.mul(o, nn.tanh(new_state_c),
out=new_state_h)
return nn.layers | [] |
Please provide a description of the function:def evaluate(self, brain_info):
feed_dict = {self.model.dropout_rate: self.evaluate_rate,
self.model.sequence_length: 1}
feed_dict = self._fill_eval_dict(feed_dict, brain_info)
if self.use_recurrent:
if brain_info.memories.shape[1] == 0:
brain_info.memories = self.make_empty_memory(len(brain_info.agents))
feed_dict[self.model.memory_in] = brain_info.memories
run_out = self._execute_model(feed_dict, self.inference_dict)
return run_out | [
"\n Evaluates policy for the agent experiences provided.\n :param brain_info: BrainInfo input to network.\n :return: Results of evaluation.\n "
] |
Please provide a description of the function:def update(self, mini_batch, num_sequences):
feed_dict = {self.model.dropout_rate: self.update_rate,
self.model.batch_size: num_sequences,
self.model.sequence_length: self.sequence_length}
if self.use_continuous_act:
feed_dict[self.model.true_action] = mini_batch['actions']. \
reshape([-1, self.brain.vector_action_space_size[0]])
else:
feed_dict[self.model.true_action] = mini_batch['actions'].reshape(
[-1, len(self.brain.vector_action_space_size)])
feed_dict[self.model.action_masks] = np.ones(
(num_sequences, sum(self.brain.vector_action_space_size)))
if self.use_vec_obs:
apparent_obs_size = self.brain.vector_observation_space_size * \
self.brain.num_stacked_vector_observations
feed_dict[self.model.vector_in] = mini_batch['vector_obs'] \
.reshape([-1,apparent_obs_size])
for i, _ in enumerate(self.model.visual_in):
visual_obs = mini_batch['visual_obs%d' % i]
feed_dict[self.model.visual_in[i]] = visual_obs
if self.use_recurrent:
feed_dict[self.model.memory_in] = np.zeros([num_sequences, self.m_size])
run_out = self._execute_model(feed_dict, self.update_dict)
return run_out | [
"\n Performs update on model.\n :param mini_batch: Batch of experiences.\n :param num_sequences: Number of sequences to process.\n :return: Results of update.\n "
] |
Please provide a description of the function:def increment_lesson(self, measure_val):
if not self.data or not measure_val or math.isnan(measure_val):
return False
if self.data['signal_smoothing']:
measure_val = self.smoothing_value * 0.25 + 0.75 * measure_val
self.smoothing_value = measure_val
if self.lesson_num < self.max_lesson_num:
if measure_val > self.data['thresholds'][self.lesson_num]:
self.lesson_num += 1
config = {}
parameters = self.data['parameters']
for key in parameters:
config[key] = parameters[key][self.lesson_num]
logger.info('{0} lesson changed. Now in lesson {1}: {2}'
.format(self._brain_name,
self.lesson_num,
', '.join([str(x) + ' -> ' + str(config[x])
for x in config])))
return True
return False | [
"\n Increments the lesson number depending on the progress given.\n :param measure_val: Measure of progress (either reward or percentage\n steps completed).\n :return Whether the lesson was incremented.\n "
] |
Please provide a description of the function:def get_config(self, lesson=None):
if not self.data:
return {}
if lesson is None:
lesson = self.lesson_num
lesson = max(0, min(lesson, self.max_lesson_num))
config = {}
parameters = self.data['parameters']
for key in parameters:
config[key] = parameters[key][lesson]
return config | [
"\n Returns reset parameters which correspond to the lesson.\n :param lesson: The lesson you want to get the config of. If None, the\n current lesson is returned.\n :return: The configuration of the reset parameters.\n "
] |
Please provide a description of the function:def get_gae(rewards, value_estimates, value_next=0.0, gamma=0.99, lambd=0.95):
value_estimates = np.asarray(value_estimates.tolist() + [value_next])
delta_t = rewards + gamma * value_estimates[1:] - value_estimates[:-1]
advantage = discount_rewards(r=delta_t, gamma=gamma * lambd)
return advantage | [
"\n Computes generalized advantage estimate for use in updating policy.\n :param rewards: list of rewards for time-steps t to T.\n :param value_next: Value estimate for time-step T+1.\n :param value_estimates: list of value estimates for time-steps t to T.\n :param gamma: Discount factor.\n :param lambd: GAE weighing factor.\n :return: list of advantage estimates for time-steps t to T.\n "
] |
Please provide a description of the function:def increment_step_and_update_last_reward(self):
if len(self.stats['Environment/Cumulative Reward']) > 0:
mean_reward = np.mean(self.stats['Environment/Cumulative Reward'])
self.policy.update_reward(mean_reward)
self.policy.increment_step()
self.step = self.policy.get_current_step() | [
"\n Increment the step count of the trainer and Updates the last reward\n "
] |
Please provide a description of the function:def construct_curr_info(self, next_info: BrainInfo) -> BrainInfo:
visual_observations = [[]]
vector_observations = []
text_observations = []
memories = []
rewards = []
local_dones = []
max_reacheds = []
agents = []
prev_vector_actions = []
prev_text_actions = []
action_masks = []
for agent_id in next_info.agents:
agent_brain_info = self.training_buffer[agent_id].last_brain_info
if agent_brain_info is None:
agent_brain_info = next_info
agent_index = agent_brain_info.agents.index(agent_id)
for i in range(len(next_info.visual_observations)):
visual_observations[i].append(agent_brain_info.visual_observations[i][agent_index])
vector_observations.append(agent_brain_info.vector_observations[agent_index])
text_observations.append(agent_brain_info.text_observations[agent_index])
if self.policy.use_recurrent:
if len(agent_brain_info.memories) > 0:
memories.append(agent_brain_info.memories[agent_index])
else:
memories.append(self.policy.make_empty_memory(1))
rewards.append(agent_brain_info.rewards[agent_index])
local_dones.append(agent_brain_info.local_done[agent_index])
max_reacheds.append(agent_brain_info.max_reached[agent_index])
agents.append(agent_brain_info.agents[agent_index])
prev_vector_actions.append(agent_brain_info.previous_vector_actions[agent_index])
prev_text_actions.append(agent_brain_info.previous_text_actions[agent_index])
action_masks.append(agent_brain_info.action_masks[agent_index])
if self.policy.use_recurrent:
memories = np.vstack(memories)
curr_info = BrainInfo(visual_observations, vector_observations, text_observations,
memories, rewards, agents, local_dones, prev_vector_actions,
prev_text_actions, max_reacheds, action_masks)
return curr_info | [
"\n Constructs a BrainInfo which contains the most recent previous experiences for all agents info\n which correspond to the agents in a provided next_info.\n :BrainInfo next_info: A t+1 BrainInfo.\n :return: curr_info: Reconstructed BrainInfo to match agents of next_info.\n "
] |
Please provide a description of the function:def add_experiences(self, curr_all_info: AllBrainInfo, next_all_info: AllBrainInfo, take_action_outputs):
self.trainer_metrics.start_experience_collection_timer()
if take_action_outputs:
self.stats['Policy/Value Estimate'].append(take_action_outputs['value'].mean())
self.stats['Policy/Entropy'].append(take_action_outputs['entropy'].mean())
self.stats['Policy/Learning Rate'].append(take_action_outputs['learning_rate'])
curr_info = curr_all_info[self.brain_name]
next_info = next_all_info[self.brain_name]
for agent_id in curr_info.agents:
self.training_buffer[agent_id].last_brain_info = curr_info
self.training_buffer[agent_id].last_take_action_outputs = take_action_outputs
if curr_info.agents != next_info.agents:
curr_to_use = self.construct_curr_info(next_info)
else:
curr_to_use = curr_info
intrinsic_rewards = self.policy.get_intrinsic_rewards(curr_to_use, next_info)
for agent_id in next_info.agents:
stored_info = self.training_buffer[agent_id].last_brain_info
stored_take_action_outputs = self.training_buffer[agent_id].last_take_action_outputs
if stored_info is not None:
idx = stored_info.agents.index(agent_id)
next_idx = next_info.agents.index(agent_id)
if not stored_info.local_done[idx]:
for i, _ in enumerate(stored_info.visual_observations):
self.training_buffer[agent_id]['visual_obs%d' % i].append(
stored_info.visual_observations[i][idx])
self.training_buffer[agent_id]['next_visual_obs%d' % i].append(
next_info.visual_observations[i][next_idx])
if self.policy.use_vec_obs:
self.training_buffer[agent_id]['vector_obs'].append(stored_info.vector_observations[idx])
self.training_buffer[agent_id]['next_vector_in'].append(
next_info.vector_observations[next_idx])
if self.policy.use_recurrent:
if stored_info.memories.shape[1] == 0:
stored_info.memories = np.zeros((len(stored_info.agents), self.policy.m_size))
self.training_buffer[agent_id]['memory'].append(stored_info.memories[idx])
actions = stored_take_action_outputs['action']
if self.policy.use_continuous_act:
actions_pre = stored_take_action_outputs['pre_action']
self.training_buffer[agent_id]['actions_pre'].append(actions_pre[idx])
epsilons = stored_take_action_outputs['random_normal_epsilon']
self.training_buffer[agent_id]['random_normal_epsilon'].append(
epsilons[idx])
else:
self.training_buffer[agent_id]['action_mask'].append(
stored_info.action_masks[idx], padding_value=1)
a_dist = stored_take_action_outputs['log_probs']
value = stored_take_action_outputs['value']
self.training_buffer[agent_id]['actions'].append(actions[idx])
self.training_buffer[agent_id]['prev_action'].append(stored_info.previous_vector_actions[idx])
self.training_buffer[agent_id]['masks'].append(1.0)
if self.use_curiosity:
self.training_buffer[agent_id]['rewards'].append(next_info.rewards[next_idx] +
intrinsic_rewards[next_idx])
else:
self.training_buffer[agent_id]['rewards'].append(next_info.rewards[next_idx])
self.training_buffer[agent_id]['action_probs'].append(a_dist[idx])
self.training_buffer[agent_id]['value_estimates'].append(value[idx][0])
if agent_id not in self.cumulative_rewards:
self.cumulative_rewards[agent_id] = 0
self.cumulative_rewards[agent_id] += next_info.rewards[next_idx]
if self.use_curiosity:
if agent_id not in self.intrinsic_rewards:
self.intrinsic_rewards[agent_id] = 0
self.intrinsic_rewards[agent_id] += intrinsic_rewards[next_idx]
if not next_info.local_done[next_idx]:
if agent_id not in self.episode_steps:
self.episode_steps[agent_id] = 0
self.episode_steps[agent_id] += 1
self.trainer_metrics.end_experience_collection_timer() | [
"\n Adds experiences to each agent's experience history.\n :param curr_all_info: Dictionary of all current brains and corresponding BrainInfo.\n :param next_all_info: Dictionary of all current brains and corresponding BrainInfo.\n :param take_action_outputs: The outputs of the Policy's get_action method.\n "
] |
Please provide a description of the function:def process_experiences(self, current_info: AllBrainInfo, new_info: AllBrainInfo):
self.trainer_metrics.start_experience_collection_timer()
info = new_info[self.brain_name]
for l in range(len(info.agents)):
agent_actions = self.training_buffer[info.agents[l]]['actions']
if ((info.local_done[l] or len(agent_actions) > self.trainer_parameters['time_horizon'])
and len(agent_actions) > 0):
agent_id = info.agents[l]
if info.local_done[l] and not info.max_reached[l]:
value_next = 0.0
else:
if info.max_reached[l]:
bootstrapping_info = self.training_buffer[agent_id].last_brain_info
idx = bootstrapping_info.agents.index(agent_id)
else:
bootstrapping_info = info
idx = l
value_next = self.policy.get_value_estimate(bootstrapping_info, idx)
self.training_buffer[agent_id]['advantages'].set(
get_gae(
rewards=self.training_buffer[agent_id]['rewards'].get_batch(),
value_estimates=self.training_buffer[agent_id]['value_estimates'].get_batch(),
value_next=value_next,
gamma=self.trainer_parameters['gamma'],
lambd=self.trainer_parameters['lambd']))
self.training_buffer[agent_id]['discounted_returns'].set(
self.training_buffer[agent_id]['advantages'].get_batch()
+ self.training_buffer[agent_id]['value_estimates'].get_batch())
self.training_buffer.append_update_buffer(agent_id, batch_size=None,
training_length=self.policy.sequence_length)
self.training_buffer[agent_id].reset_agent()
if info.local_done[l]:
self.cumulative_returns_since_policy_update.append(self.
cumulative_rewards.get(agent_id, 0))
self.stats['Environment/Cumulative Reward'].append(
self.cumulative_rewards.get(agent_id, 0))
self.reward_buffer.appendleft(self.cumulative_rewards.get(agent_id, 0))
self.stats['Environment/Episode Length'].append(
self.episode_steps.get(agent_id, 0))
self.cumulative_rewards[agent_id] = 0
self.episode_steps[agent_id] = 0
if self.use_curiosity:
self.stats['Policy/Curiosity Reward'].append(
self.intrinsic_rewards.get(agent_id, 0))
self.intrinsic_rewards[agent_id] = 0
self.trainer_metrics.end_experience_collection_timer() | [
"\n Checks agent histories for processing condition, and processes them as necessary.\n Processing involves calculating value and advantage targets for model updating step.\n :param current_info: Dictionary of all current brains and corresponding BrainInfo.\n :param new_info: Dictionary of all next brains and corresponding BrainInfo.\n "
] |
Please provide a description of the function:def end_episode(self):
self.training_buffer.reset_local_buffers()
for agent_id in self.cumulative_rewards:
self.cumulative_rewards[agent_id] = 0
for agent_id in self.episode_steps:
self.episode_steps[agent_id] = 0
if self.use_curiosity:
for agent_id in self.intrinsic_rewards:
self.intrinsic_rewards[agent_id] = 0 | [
"\n A signal that the Episode has ended. The buffer must be reset.\n Get only called when the academy resets.\n "
] |
Please provide a description of the function:def is_ready_update(self):
size_of_buffer = len(self.training_buffer.update_buffer['actions'])
return size_of_buffer > max(int(self.trainer_parameters['buffer_size'] / self.policy.sequence_length), 1) | [
"\n Returns whether or not the trainer has enough elements to run update model\n :return: A boolean corresponding to whether or not update_model() can be run\n "
] |
Please provide a description of the function:def update_policy(self):
self.trainer_metrics.start_policy_update_timer(
number_experiences=len(self.training_buffer.update_buffer['actions']),
mean_return=float(np.mean(self.cumulative_returns_since_policy_update)))
n_sequences = max(int(self.trainer_parameters['batch_size'] / self.policy.sequence_length), 1)
value_total, policy_total, forward_total, inverse_total = [], [], [], []
advantages = self.training_buffer.update_buffer['advantages'].get_batch()
self.training_buffer.update_buffer['advantages'].set(
(advantages - advantages.mean()) / (advantages.std() + 1e-10))
num_epoch = self.trainer_parameters['num_epoch']
for _ in range(num_epoch):
self.training_buffer.update_buffer.shuffle()
buffer = self.training_buffer.update_buffer
for l in range(len(self.training_buffer.update_buffer['actions']) // n_sequences):
start = l * n_sequences
end = (l + 1) * n_sequences
run_out = self.policy.update(buffer.make_mini_batch(start, end), n_sequences)
value_total.append(run_out['value_loss'])
policy_total.append(np.abs(run_out['policy_loss']))
if self.use_curiosity:
inverse_total.append(run_out['inverse_loss'])
forward_total.append(run_out['forward_loss'])
self.stats['Losses/Value Loss'].append(np.mean(value_total))
self.stats['Losses/Policy Loss'].append(np.mean(policy_total))
if self.use_curiosity:
self.stats['Losses/Forward Loss'].append(np.mean(forward_total))
self.stats['Losses/Inverse Loss'].append(np.mean(inverse_total))
self.training_buffer.reset_update_buffer()
self.trainer_metrics.end_policy_update() | [
"\n Uses demonstration_buffer to update the policy.\n "
] |
Please provide a description of the function:def reset(self):
info = self._env.reset()[self.brain_name]
n_agents = len(info.agents)
self._check_agents(n_agents)
self.game_over = False
if not self._multiagent:
obs, reward, done, info = self._single_step(info)
else:
obs, reward, done, info = self._multi_step(info)
return obs | [
"Resets the state of the environment and returns an initial observation.\n In the case of multi-agent environments, this is a list.\n Returns: observation (object/list): the initial observation of the\n space.\n "
] |
Please provide a description of the function:def step(self, action):
# Use random actions for all other agents in environment.
if self._multiagent:
if not isinstance(action, list):
raise UnityGymException("The environment was expecting `action` to be a list.")
if len(action) != self._n_agents:
raise UnityGymException(
"The environment was expecting a list of {} actions.".format(self._n_agents))
else:
if self._flattener is not None:
# Action space is discrete and flattened - we expect a list of scalars
action = [self._flattener.lookup_action(_act) for _act in action]
action = np.array(action)
else:
if self._flattener is not None:
# Translate action into list
action = self._flattener.lookup_action(action)
info = self._env.step(action)[self.brain_name]
n_agents = len(info.agents)
self._check_agents(n_agents)
self._current_state = info
if not self._multiagent:
obs, reward, done, info = self._single_step(info)
self.game_over = done
else:
obs, reward, done, info = self._multi_step(info)
self.game_over = all(done)
return obs, reward, done, info | [
"Run one timestep of the environment's dynamics. When end of\n episode is reached, you are responsible for calling `reset()`\n to reset this environment's state.\n Accepts an action and returns a tuple (observation, reward, done, info).\n In the case of multi-agent environments, these are lists.\n Args:\n action (object/list): an action provided by the environment\n Returns:\n observation (object/list): agent's observation of the current environment\n reward (float/list) : amount of reward returned after previous action\n done (boolean/list): whether the episode has ended.\n info (dict): contains auxiliary diagnostic information, including BrainInfo.\n "
] |
Please provide a description of the function:def _create_lookup(self, branched_action_space):
possible_vals = [range(_num) for _num in branched_action_space]
all_actions = [list(_action) for _action in itertools.product(*possible_vals)]
# Dict should be faster than List for large action spaces
action_lookup = {_scalar: _action for (_scalar, _action) in enumerate(all_actions)}
return action_lookup | [
"\n Creates a Dict that maps discrete actions (scalars) to branched actions (lists).\n Each key in the Dict maps to one unique set of branched actions, and each value\n contains the List of branched actions.\n "
] |
Please provide a description of the function:def create_server(self):
self.check_port(self.port)
try:
# Establish communication grpc
self.server = grpc.server(ThreadPoolExecutor(max_workers=10))
self.unity_to_external = UnityToExternalServicerImplementation()
add_UnityToExternalServicer_to_server(self.unity_to_external, self.server)
# Using unspecified address, which means that grpc is communicating on all IPs
# This is so that the docker container can connect.
self.server.add_insecure_port('[::]:' + str(self.port))
self.server.start()
self.is_open = True
except:
raise UnityWorkerInUseException(self.worker_id) | [
"\n Creates the GRPC server.\n "
] |
Please provide a description of the function:def check_port(self, port):
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
try:
s.bind(("localhost", port))
except socket.error:
raise UnityWorkerInUseException(self.worker_id)
finally:
s.close() | [
"\n Attempts to bind to the requested communicator port, checking if it is already in use.\n "
] |
Please provide a description of the function:def close(self):
if self.is_open:
message_input = UnityMessage()
message_input.header.status = 400
self.unity_to_external.parent_conn.send(message_input)
self.unity_to_external.parent_conn.close()
self.server.stop(False)
self.is_open = False | [
"\n Sends a shutdown signal to the unity environment, and closes the grpc connection.\n "
] |
Please provide a description of the function:def process_pixels(image_bytes, gray_scale):
s = bytearray(image_bytes)
image = Image.open(io.BytesIO(s))
s = np.array(image) / 255.0
if gray_scale:
s = np.mean(s, axis=2)
s = np.reshape(s, [s.shape[0], s.shape[1], 1])
return s | [
"\n Converts byte array observation image into numpy array, re-sizes it,\n and optionally converts it to grey scale\n :param gray_scale: Whether to convert the image to grayscale.\n :param image_bytes: input byte array corresponding to image\n :return: processed numpy array of observation from environment\n "
] |
Please provide a description of the function:def from_agent_proto(agent_info_list, brain_params):
vis_obs = []
for i in range(brain_params.number_visual_observations):
obs = [BrainInfo.process_pixels(x.visual_observations[i],
brain_params.camera_resolutions[i]['blackAndWhite'])
for x in agent_info_list]
vis_obs += [obs]
if len(agent_info_list) == 0:
memory_size = 0
else:
memory_size = max([len(x.memories) for x in agent_info_list])
if memory_size == 0:
memory = np.zeros((0, 0))
else:
[x.memories.extend([0] * (memory_size - len(x.memories))) for x in agent_info_list]
memory = np.array([list(x.memories) for x in agent_info_list])
total_num_actions = sum(brain_params.vector_action_space_size)
mask_actions = np.ones((len(agent_info_list), total_num_actions))
for agent_index, agent_info in enumerate(agent_info_list):
if agent_info.action_mask is not None:
if len(agent_info.action_mask) == total_num_actions:
mask_actions[agent_index, :] = [
0 if agent_info.action_mask[k] else 1 for k in range(total_num_actions)]
if any([np.isnan(x.reward) for x in agent_info_list]):
logger.warning("An agent had a NaN reward for brain " + brain_params.brain_name)
if any([np.isnan(x.stacked_vector_observation).any() for x in agent_info_list]):
logger.warning("An agent had a NaN observation for brain " + brain_params.brain_name)
if len(agent_info_list) == 0:
vector_obs = np.zeros(
(0, brain_params.vector_observation_space_size * brain_params.num_stacked_vector_observations)
)
else:
vector_obs = np.nan_to_num(
np.array([x.stacked_vector_observation for x in agent_info_list])
)
brain_info = BrainInfo(
visual_observation=vis_obs,
vector_observation=vector_obs,
text_observations=[x.text_observation for x in agent_info_list],
memory=memory,
reward=[x.reward if not np.isnan(x.reward) else 0 for x in agent_info_list],
agents=[x.id for x in agent_info_list],
local_done=[x.done for x in agent_info_list],
vector_action=np.array([x.stored_vector_actions for x in agent_info_list]),
text_action=[list(x.stored_text_actions) for x in agent_info_list],
max_reached=[x.max_step_reached for x in agent_info_list],
custom_observations=[x.custom_observation for x in agent_info_list],
action_mask=mask_actions
)
return brain_info | [
"\n Converts list of agent infos to BrainInfo.\n "
] |
Please provide a description of the function:def from_proto(brain_param_proto):
resolution = [{
"height": x.height,
"width": x.width,
"blackAndWhite": x.gray_scale
} for x in brain_param_proto.camera_resolutions]
brain_params = BrainParameters(brain_param_proto.brain_name,
brain_param_proto.vector_observation_size,
brain_param_proto.num_stacked_vector_observations,
resolution,
list(brain_param_proto.vector_action_size),
list(brain_param_proto.vector_action_descriptions),
brain_param_proto.vector_action_space_type)
return brain_params | [
"\n Converts brain parameter proto to BrainParameter object.\n :param brain_param_proto: protobuf object.\n :return: BrainParameter object.\n "
] |
Please provide a description of the function:def new(self):
new_dashboard = models.Dashboard(
dashboard_title='[ untitled dashboard ]',
owners=[g.user],
)
db.session.add(new_dashboard)
db.session.commit()
return redirect(f'/superset/dashboard/{new_dashboard.id}/?edit=true') | [
"Creates a new, blank dashboard and redirects to it in edit mode"
] |
Please provide a description of the function:def get(self, object_type, object_id):
if object_id == 0:
return json_success(json.dumps([]))
query = db.session.query(TaggedObject).filter(and_(
TaggedObject.object_type == object_type,
TaggedObject.object_id == object_id))
tags = [{'id': obj.tag.id, 'name': obj.tag.name} for obj in query]
return json_success(json.dumps(tags)) | [
"List all tags a given object has."
] |
Please provide a description of the function:def post(self, object_type, object_id):
if object_id == 0:
return Response(status=404)
tagged_objects = []
for name in request.get_json(force=True):
if ':' in name:
type_name = name.split(':', 1)[0]
type_ = TagTypes[type_name]
else:
type_ = TagTypes.custom
tag = db.session.query(Tag).filter_by(name=name, type=type_).first()
if not tag:
tag = Tag(name=name, type=type_)
tagged_objects.append(
TaggedObject(
object_id=object_id,
object_type=object_type,
tag=tag,
),
)
db.session.add_all(tagged_objects)
db.session.commit()
return Response(status=201) | [
"Add new tags to an object."
] |
Please provide a description of the function:def delete(self, object_type, object_id):
tag_names = request.get_json(force=True)
if not tag_names:
return Response(status=403)
db.session.query(TaggedObject).filter(and_(
TaggedObject.object_type == object_type,
TaggedObject.object_id == object_id),
TaggedObject.tag.has(Tag.name.in_(tag_names)),
).delete(synchronize_session=False)
db.session.commit()
return Response(status=204) | [
"Remove tags from an object."
] |
Please provide a description of the function:def import_datasource(
session,
i_datasource,
lookup_database,
lookup_datasource,
import_time):
make_transient(i_datasource)
logging.info('Started import of the datasource: {}'.format(
i_datasource.to_json()))
i_datasource.id = None
i_datasource.database_id = lookup_database(i_datasource).id
i_datasource.alter_params(import_time=import_time)
# override the datasource
datasource = lookup_datasource(i_datasource)
if datasource:
datasource.override(i_datasource)
session.flush()
else:
datasource = i_datasource.copy()
session.add(datasource)
session.flush()
for m in i_datasource.metrics:
new_m = m.copy()
new_m.table_id = datasource.id
logging.info('Importing metric {} from the datasource: {}'.format(
new_m.to_json(), i_datasource.full_name))
imported_m = i_datasource.metric_class.import_obj(new_m)
if (imported_m.metric_name not in
[m.metric_name for m in datasource.metrics]):
datasource.metrics.append(imported_m)
for c in i_datasource.columns:
new_c = c.copy()
new_c.table_id = datasource.id
logging.info('Importing column {} from the datasource: {}'.format(
new_c.to_json(), i_datasource.full_name))
imported_c = i_datasource.column_class.import_obj(new_c)
if (imported_c.column_name not in
[c.column_name for c in datasource.columns]):
datasource.columns.append(imported_c)
session.flush()
return datasource.id | [
"Imports the datasource from the object to the database.\n\n Metrics and columns and datasource will be overrided if exists.\n This function can be used to import/export dashboards between multiple\n superset instances. Audit metadata isn't copies over.\n "
] |
Please provide a description of the function:def run_migrations_online():
# this callback is used to prevent an auto-migration from being generated
# when there are no changes to the schema
# reference: https://alembic.sqlalchemy.org/en/latest/cookbook.html
def process_revision_directives(context, revision, directives):
if getattr(config.cmd_opts, 'autogenerate', False):
script = directives[0]
if script.upgrade_ops.is_empty():
directives[:] = []
logger.info('No changes in schema detected.')
engine = engine_from_config(config.get_section(config.config_ini_section),
prefix='sqlalchemy.',
poolclass=pool.NullPool)
connection = engine.connect()
kwargs = {}
if engine.name in ('sqlite', 'mysql'):
kwargs = {
'transaction_per_migration': True,
'transactional_ddl': True,
}
configure_args = current_app.extensions['migrate'].configure_args
if configure_args:
kwargs.update(configure_args)
context.configure(connection=connection,
target_metadata=target_metadata,
# compare_type=True,
process_revision_directives=process_revision_directives,
**kwargs)
try:
with context.begin_transaction():
context.run_migrations()
finally:
connection.close() | [
"Run migrations in 'online' mode.\n\n In this scenario we need to create an Engine\n and associate a connection with the context.\n\n "
] |
Please provide a description of the function:def get_df(self, query_obj=None):
if not query_obj:
query_obj = self.query_obj()
if not query_obj:
return None
self.error_msg = ''
timestamp_format = None
if self.datasource.type == 'table':
dttm_col = self.datasource.get_col(query_obj['granularity'])
if dttm_col:
timestamp_format = dttm_col.python_date_format
# The datasource here can be different backend but the interface is common
self.results = self.datasource.query(query_obj)
self.query = self.results.query
self.status = self.results.status
self.error_message = self.results.error_message
df = self.results.df
# Transform the timestamp we received from database to pandas supported
# datetime format. If no python_date_format is specified, the pattern will
# be considered as the default ISO date format
# If the datetime format is unix, the parse will use the corresponding
# parsing logic.
if df is not None and not df.empty:
if DTTM_ALIAS in df.columns:
if timestamp_format in ('epoch_s', 'epoch_ms'):
# Column has already been formatted as a timestamp.
dttm_col = df[DTTM_ALIAS]
one_ts_val = dttm_col[0]
# convert time column to pandas Timestamp, but different
# ways to convert depending on string or int types
try:
int(one_ts_val)
is_integral = True
except ValueError:
is_integral = False
if is_integral:
unit = 's' if timestamp_format == 'epoch_s' else 'ms'
df[DTTM_ALIAS] = pd.to_datetime(dttm_col, utc=False, unit=unit,
origin='unix')
else:
df[DTTM_ALIAS] = dttm_col.apply(pd.Timestamp)
else:
df[DTTM_ALIAS] = pd.to_datetime(
df[DTTM_ALIAS], utc=False, format=timestamp_format)
if self.datasource.offset:
df[DTTM_ALIAS] += timedelta(hours=self.datasource.offset)
df[DTTM_ALIAS] += self.time_shift
if self.enforce_numerical_metrics:
self.df_metrics_to_num(df)
df.replace([np.inf, -np.inf], np.nan, inplace=True)
return df | [
"Returns a pandas dataframe based on the query object"
] |
Please provide a description of the function:def query_obj(self):
form_data = self.form_data
self.process_query_filters()
gb = form_data.get('groupby') or []
metrics = self.all_metrics or []
columns = form_data.get('columns') or []
groupby = []
for o in gb + columns:
if o not in groupby:
groupby.append(o)
is_timeseries = self.is_timeseries
if DTTM_ALIAS in groupby:
groupby.remove(DTTM_ALIAS)
is_timeseries = True
granularity = (
form_data.get('granularity') or
form_data.get('granularity_sqla')
)
limit = int(form_data.get('limit') or 0)
timeseries_limit_metric = form_data.get('timeseries_limit_metric')
row_limit = int(form_data.get('row_limit') or config.get('ROW_LIMIT'))
# default order direction
order_desc = form_data.get('order_desc', True)
since, until = utils.get_since_until(relative_end=relative_end,
time_range=form_data.get('time_range'),
since=form_data.get('since'),
until=form_data.get('until'))
time_shift = form_data.get('time_shift', '')
self.time_shift = utils.parse_human_timedelta(time_shift)
from_dttm = None if since is None else (since - self.time_shift)
to_dttm = None if until is None else (until - self.time_shift)
if from_dttm and to_dttm and from_dttm > to_dttm:
raise Exception(_('From date cannot be larger than to date'))
self.from_dttm = from_dttm
self.to_dttm = to_dttm
# extras are used to query elements specific to a datasource type
# for instance the extra where clause that applies only to Tables
extras = {
'where': form_data.get('where', ''),
'having': form_data.get('having', ''),
'having_druid': form_data.get('having_filters', []),
'time_grain_sqla': form_data.get('time_grain_sqla', ''),
'druid_time_origin': form_data.get('druid_time_origin', ''),
}
d = {
'granularity': granularity,
'from_dttm': from_dttm,
'to_dttm': to_dttm,
'is_timeseries': is_timeseries,
'groupby': groupby,
'metrics': metrics,
'row_limit': row_limit,
'filter': self.form_data.get('filters', []),
'timeseries_limit': limit,
'extras': extras,
'timeseries_limit_metric': timeseries_limit_metric,
'order_desc': order_desc,
'prequeries': [],
'is_prequery': False,
}
return d | [
"Building a query object"
] |
Please provide a description of the function:def cache_key(self, query_obj, **extra):
cache_dict = copy.copy(query_obj)
cache_dict.update(extra)
for k in ['from_dttm', 'to_dttm']:
del cache_dict[k]
cache_dict['time_range'] = self.form_data.get('time_range')
cache_dict['datasource'] = self.datasource.uid
json_data = self.json_dumps(cache_dict, sort_keys=True)
return hashlib.md5(json_data.encode('utf-8')).hexdigest() | [
"\n The cache key is made out of the key/values in `query_obj`, plus any\n other key/values in `extra`.\n\n We remove datetime bounds that are hard values, and replace them with\n the use-provided inputs to bounds, which may be time-relative (as in\n \"5 days ago\" or \"now\").\n\n The `extra` arguments are currently used by time shift queries, since\n different time shifts wil differ only in the `from_dttm` and `to_dttm`\n values which are stripped.\n "
] |
Please provide a description of the function:def data(self):
content = {
'form_data': self.form_data,
'token': self.token,
'viz_name': self.viz_type,
'filter_select_enabled': self.datasource.filter_select_enabled,
}
return content | [
"This is the data object serialized to the js layer"
] |
Please provide a description of the function:def query_obj(self):
d = super().query_obj()
d['row_limit'] = self.form_data.get(
'row_limit', int(config.get('VIZ_ROW_LIMIT')))
numeric_columns = self.form_data.get('all_columns_x')
if numeric_columns is None:
raise Exception(_('Must have at least one numeric column specified'))
self.columns = numeric_columns
d['columns'] = numeric_columns + self.groupby
# override groupby entry to avoid aggregation
d['groupby'] = []
return d | [
"Returns the query object for this visualization"
] |
Please provide a description of the function:def get_data(self, df):
chart_data = []
if len(self.groupby) > 0:
groups = df.groupby(self.groupby)
else:
groups = [((), df)]
for keys, data in groups:
chart_data.extend([{
'key': self.labelify(keys, column),
'values': data[column].tolist()}
for column in self.columns])
return chart_data | [
"Returns the chart data"
] |
Please provide a description of the function:def levels_for(self, time_op, groups, df):
levels = {}
for i in range(0, len(groups) + 1):
agg_df = df.groupby(groups[:i]) if i else df
levels[i] = (
agg_df.mean() if time_op == 'agg_mean'
else agg_df.sum(numeric_only=True))
return levels | [
"\n Compute the partition at each `level` from the dataframe.\n "
] |
Please provide a description of the function:def nest_values(self, levels, level=0, metric=None, dims=()):
if not level:
return [{
'name': m,
'val': levels[0][m],
'children': self.nest_values(levels, 1, m),
} for m in levels[0].index]
if level == 1:
return [{
'name': i,
'val': levels[1][metric][i],
'children': self.nest_values(levels, 2, metric, (i,)),
} for i in levels[1][metric].index]
if level >= len(levels):
return []
return [{
'name': i,
'val': levels[level][metric][dims][i],
'children': self.nest_values(
levels, level + 1, metric, dims + (i,),
),
} for i in levels[level][metric][dims].index] | [
"\n Nest values at each level on the back-end with\n access and setting, instead of summing from the bottom.\n "
] |
Please provide a description of the function:def short_data(self):
return {
'edit_url': self.url,
'id': self.id,
'uid': self.uid,
'schema': self.schema,
'name': self.name,
'type': self.type,
'connection': self.connection,
'creator': str(self.created_by),
} | [
"Data representation of the datasource sent to the frontend"
] |
Please provide a description of the function:def data(self):
order_by_choices = []
# self.column_names return sorted column_names
for s in self.column_names:
s = str(s or '')
order_by_choices.append((json.dumps([s, True]), s + ' [asc]'))
order_by_choices.append((json.dumps([s, False]), s + ' [desc]'))
verbose_map = {'__timestamp': 'Time'}
verbose_map.update({
o.metric_name: o.verbose_name or o.metric_name
for o in self.metrics
})
verbose_map.update({
o.column_name: o.verbose_name or o.column_name
for o in self.columns
})
return {
# simple fields
'id': self.id,
'column_formats': self.column_formats,
'description': self.description,
'database': self.database.data, # pylint: disable=no-member
'default_endpoint': self.default_endpoint,
'filter_select': self.filter_select_enabled, # TODO deprecate
'filter_select_enabled': self.filter_select_enabled,
'name': self.name,
'datasource_name': self.datasource_name,
'type': self.type,
'schema': self.schema,
'offset': self.offset,
'cache_timeout': self.cache_timeout,
'params': self.params,
'perm': self.perm,
'edit_url': self.url,
# sqla-specific
'sql': self.sql,
# one to many
'columns': [o.data for o in self.columns],
'metrics': [o.data for o in self.metrics],
# TODO deprecate, move logic to JS
'order_by_choices': order_by_choices,
'owners': [owner.id for owner in self.owners],
'verbose_map': verbose_map,
'select_star': self.select_star,
} | [
"Data representation of the datasource sent to the frontend"
] |
Please provide a description of the function:def get_fk_many_from_list(
self, object_list, fkmany, fkmany_class, key_attr):
object_dict = {o.get(key_attr): o for o in object_list}
object_keys = [o.get(key_attr) for o in object_list]
# delete fks that have been removed
fkmany = [o for o in fkmany if getattr(o, key_attr) in object_keys]
# sync existing fks
for fk in fkmany:
obj = object_dict.get(getattr(fk, key_attr))
for attr in fkmany_class.update_from_object_fields:
setattr(fk, attr, obj.get(attr))
# create new fks
new_fks = []
orm_keys = [getattr(o, key_attr) for o in fkmany]
for obj in object_list:
key = obj.get(key_attr)
if key not in orm_keys:
del obj['id']
orm_kwargs = {}
for k in obj:
if (
k in fkmany_class.update_from_object_fields and
k in obj
):
orm_kwargs[k] = obj[k]
new_obj = fkmany_class(**orm_kwargs)
new_fks.append(new_obj)
fkmany += new_fks
return fkmany | [
"Update ORM one-to-many list from object list\n\n Used for syncing metrics and columns using the same code"
] |
Please provide a description of the function:def update_from_object(self, obj):
for attr in self.update_from_object_fields:
setattr(self, attr, obj.get(attr))
self.owners = obj.get('owners', [])
# Syncing metrics
metrics = self.get_fk_many_from_list(
obj.get('metrics'), self.metrics, self.metric_class, 'metric_name')
self.metrics = metrics
# Syncing columns
self.columns = self.get_fk_many_from_list(
obj.get('columns'), self.columns, self.column_class, 'column_name') | [
"Update datasource from a data structure\n\n The UI's table editor crafts a complex data structure that\n contains most of the datasource's properties as well as\n an array of metrics and columns objects. This method\n receives the object from the UI and syncs the datasource to\n match it. Since the fields are different for the different\n connectors, the implementation uses ``update_from_object_fields``\n which can be defined for each connector and\n defines which fields should be synced"
] |
Please provide a description of the function:def get_query_result(self, query_object):
# Here, we assume that all the queries will use the same datasource, which is
# is a valid assumption for current setting. In a long term, we may or maynot
# support multiple queries from different data source.
timestamp_format = None
if self.datasource.type == 'table':
dttm_col = self.datasource.get_col(query_object.granularity)
if dttm_col:
timestamp_format = dttm_col.python_date_format
# The datasource here can be different backend but the interface is common
result = self.datasource.query(query_object.to_dict())
df = result.df
# Transform the timestamp we received from database to pandas supported
# datetime format. If no python_date_format is specified, the pattern will
# be considered as the default ISO date format
# If the datetime format is unix, the parse will use the corresponding
# parsing logic
if df is not None and not df.empty:
if DTTM_ALIAS in df.columns:
if timestamp_format in ('epoch_s', 'epoch_ms'):
# Column has already been formatted as a timestamp.
df[DTTM_ALIAS] = df[DTTM_ALIAS].apply(pd.Timestamp)
else:
df[DTTM_ALIAS] = pd.to_datetime(
df[DTTM_ALIAS], utc=False, format=timestamp_format)
if self.datasource.offset:
df[DTTM_ALIAS] += timedelta(hours=self.datasource.offset)
df[DTTM_ALIAS] += query_object.time_shift
if self.enforce_numerical_metrics:
self.df_metrics_to_num(df, query_object)
df.replace([np.inf, -np.inf], np.nan)
return {
'query': result.query,
'status': result.status,
'error_message': result.error_message,
'df': df,
} | [
"Returns a pandas dataframe based on the query object"
] |
Please provide a description of the function:def df_metrics_to_num(self, df, query_object):
metrics = [metric for metric in query_object.metrics]
for col, dtype in df.dtypes.items():
if dtype.type == np.object_ and col in metrics:
df[col] = pd.to_numeric(df[col], errors='coerce') | [
"Converting metrics to numeric when pandas.read_sql cannot"
] |
Please provide a description of the function:def get_single_payload(self, query_obj):
payload = self.get_df_payload(query_obj)
df = payload.get('df')
status = payload.get('status')
if status != utils.QueryStatus.FAILED:
if df is not None and df.empty:
payload['error'] = 'No data'
else:
payload['data'] = self.get_data(df)
if 'df' in payload:
del payload['df']
return payload | [
"Returns a payload of metadata and data"
] |
Please provide a description of the function:def get_df_payload(self, query_obj, **kwargs):
cache_key = query_obj.cache_key(
datasource=self.datasource.uid, **kwargs) if query_obj else None
logging.info('Cache key: {}'.format(cache_key))
is_loaded = False
stacktrace = None
df = None
cached_dttm = datetime.utcnow().isoformat().split('.')[0]
cache_value = None
status = None
query = ''
error_message = None
if cache_key and cache and not self.force:
cache_value = cache.get(cache_key)
if cache_value:
stats_logger.incr('loaded_from_cache')
try:
cache_value = pkl.loads(cache_value)
df = cache_value['df']
query = cache_value['query']
status = utils.QueryStatus.SUCCESS
is_loaded = True
except Exception as e:
logging.exception(e)
logging.error('Error reading cache: ' +
utils.error_msg_from_exception(e))
logging.info('Serving from cache')
if query_obj and not is_loaded:
try:
query_result = self.get_query_result(query_obj)
status = query_result['status']
query = query_result['query']
error_message = query_result['error_message']
df = query_result['df']
if status != utils.QueryStatus.FAILED:
stats_logger.incr('loaded_from_source')
is_loaded = True
except Exception as e:
logging.exception(e)
if not error_message:
error_message = '{}'.format(e)
status = utils.QueryStatus.FAILED
stacktrace = traceback.format_exc()
if (
is_loaded and
cache_key and
cache and
status != utils.QueryStatus.FAILED):
try:
cache_value = dict(
dttm=cached_dttm,
df=df if df is not None else None,
query=query,
)
cache_value = pkl.dumps(
cache_value, protocol=pkl.HIGHEST_PROTOCOL)
logging.info('Caching {} chars at key {}'.format(
len(cache_value), cache_key))
stats_logger.incr('set_cache_key')
cache.set(
cache_key,
cache_value,
timeout=self.cache_timeout)
except Exception as e:
# cache.set call can fail if the backend is down or if
# the key is too large or whatever other reasons
logging.warning('Could not cache key {}'.format(cache_key))
logging.exception(e)
cache.delete(cache_key)
return {
'cache_key': cache_key,
'cached_dttm': cache_value['dttm'] if cache_value is not None else None,
'cache_timeout': self.cache_timeout,
'df': df,
'error': error_message,
'is_cached': cache_key is not None,
'query': query,
'status': status,
'stacktrace': stacktrace,
'rowcount': len(df.index) if df is not None else 0,
} | [
"Handles caching around the df paylod retrieval"
] |
Please provide a description of the function:def data(self):
d = {}
self.token = ''
try:
d = self.viz.data
self.token = d.get('token')
except Exception as e:
logging.exception(e)
d['error'] = str(e)
return {
'datasource': self.datasource_name,
'description': self.description,
'description_markeddown': self.description_markeddown,
'edit_url': self.edit_url,
'form_data': self.form_data,
'slice_id': self.id,
'slice_name': self.slice_name,
'slice_url': self.slice_url,
'modified': self.modified(),
'changed_on_humanized': self.changed_on_humanized,
'changed_on': self.changed_on.isoformat(),
} | [
"Data used to render slice in templates"
] |
Please provide a description of the function:def get_viz(self, force=False):
slice_params = json.loads(self.params)
slice_params['slice_id'] = self.id
slice_params['json'] = 'false'
slice_params['slice_name'] = self.slice_name
slice_params['viz_type'] = self.viz_type if self.viz_type else 'table'
return viz_types[slice_params.get('viz_type')](
self.datasource,
form_data=slice_params,
force=force,
) | [
"Creates :py:class:viz.BaseViz object from the url_params_multidict.\n\n :return: object of the 'viz_type' type that is taken from the\n url_params_multidict or self.params.\n :rtype: :py:class:viz.BaseViz\n "
] |
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