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train | PPOModel.create_reward_encoder | Creates TF ops to track and increment recent average cumulative reward. | ml-agents/mlagents/trainers/ppo/models.py | def create_reward_encoder():
"""Creates TF ops to track and increment recent average cumulative reward."""
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.ass... | def create_reward_encoder():
"""Creates TF ops to track and increment recent average cumulative reward."""
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.ass... | [
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train | PPOModel.create_curiosity_encoders | Creates state encoders for current and future observations.
Used for implementation of Curiosity-driven Exploration by Self-supervised Prediction
See https://arxiv.org/abs/1705.05363 for more details.
:return: current and future state encoder tensors. | ml-agents/mlagents/trainers/ppo/models.py | def create_curiosity_encoders(self):
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Creates state encoders for current and future observations.
Used for implementation of Curiosity-driven Exploration by Self-supervised Prediction
See https://arxiv.org/abs/1705.05363 for more details.
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Creates state encoders for current and future observations.
Used for implementation of Curiosity-driven Exploration by Self-supervised Prediction
See https://arxiv.org/abs/1705.05363 for more details.
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train | PPOModel.create_inverse_model | Creates inverse model TensorFlow ops for Curiosity module.
Predicts action taken given current and future encoded states.
:param encoded_state: Tensor corresponding to encoded current state.
:param encoded_next_state: Tensor corresponding to encoded next state. | ml-agents/mlagents/trainers/ppo/models.py | def create_inverse_model(self, encoded_state, encoded_next_state):
"""
Creates inverse model TensorFlow ops for Curiosity module.
Predicts action taken given current and future encoded states.
:param encoded_state: Tensor corresponding to encoded current state.
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Creates inverse model TensorFlow ops for Curiosity module.
Predicts action taken given current and future encoded states.
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train | PPOModel.create_forward_model | Creates forward model TensorFlow ops for Curiosity module.
Predicts encoded future state based on encoded current state and given action.
:param encoded_state: Tensor corresponding to encoded current state.
:param encoded_next_state: Tensor corresponding to encoded next state. | ml-agents/mlagents/trainers/ppo/models.py | def create_forward_model(self, encoded_state, encoded_next_state):
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Creates forward model TensorFlow ops for Curiosity module.
Predicts encoded future state based on encoded current state and given action.
:param encoded_state: Tensor corresponding to encoded current state.
:p... | def create_forward_model(self, encoded_state, encoded_next_state):
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Creates forward model TensorFlow ops for Curiosity module.
Predicts encoded future state based on encoded current state and given action.
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train | PPOModel.create_ppo_optimizer | Creates training-specific Tensorflow ops for PPO models.
:param probs: Current policy probabilities
:param old_probs: Past policy probabilities
:param value: Current value estimate
:param beta: Entropy regularization strength
:param entropy: Current policy entropy
:param ... | ml-agents/mlagents/trainers/ppo/models.py | def create_ppo_optimizer(self, probs, old_probs, value, entropy, beta, epsilon, lr, max_step):
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Creates training-specific Tensorflow ops for PPO models.
:param probs: Current policy probabilities
:param old_probs: Past policy probabilities
:param value: Current value estimate
... | def create_ppo_optimizer(self, probs, old_probs, value, entropy, beta, epsilon, lr, max_step):
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Creates training-specific Tensorflow ops for PPO models.
:param probs: Current policy probabilities
:param old_probs: Past policy probabilities
:param value: Current value estimate
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train | PPOPolicy.evaluate | Evaluates policy for the agent experiences provided.
:param brain_info: BrainInfo object containing inputs.
:return: Outputs from network as defined by self.inference_dict. | ml-agents/mlagents/trainers/ppo/policy.py | def evaluate(self, brain_info):
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Evaluates policy for the agent experiences provided.
:param brain_info: BrainInfo object containing inputs.
:return: Outputs from network as defined by self.inference_dict.
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Evaluates policy for the agent experiences provided.
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train | PPOPolicy.update | Updates model using buffer.
:param num_sequences: Number of trajectories in batch.
:param mini_batch: Experience batch.
:return: Output from update process. | ml-agents/mlagents/trainers/ppo/policy.py | def update(self, mini_batch, num_sequences):
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Updates model using buffer.
:param num_sequences: Number of trajectories in batch.
:param mini_batch: Experience batch.
:return: Output from update process.
"""
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... | def update(self, mini_batch, num_sequences):
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Updates model using buffer.
:param num_sequences: Number of trajectories in batch.
:param mini_batch: Experience batch.
:return: Output from update process.
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feed_dict = {self.model.batch_size: num_sequences,
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train | PPOPolicy.get_intrinsic_rewards | Generates intrinsic reward used for Curiosity-based training.
:BrainInfo curr_info: Current BrainInfo.
:BrainInfo next_info: Next BrainInfo.
:return: Intrinsic rewards for all agents. | ml-agents/mlagents/trainers/ppo/policy.py | def get_intrinsic_rewards(self, curr_info, next_info):
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Generates intrinsic reward used for Curiosity-based training.
:BrainInfo curr_info: Current BrainInfo.
:BrainInfo next_info: Next BrainInfo.
:return: Intrinsic rewards for all agents.
"""
if self.use_curio... | def get_intrinsic_rewards(self, curr_info, next_info):
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Generates intrinsic reward used for Curiosity-based training.
:BrainInfo curr_info: Current BrainInfo.
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train | PPOPolicy.get_value_estimate | Generates value estimates for bootstrapping.
:param brain_info: BrainInfo to be used for bootstrapping.
:param idx: Index in BrainInfo of agent.
:return: Value estimate. | ml-agents/mlagents/trainers/ppo/policy.py | def get_value_estimate(self, brain_info, idx):
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Generates value estimates for bootstrapping.
:param brain_info: BrainInfo to be used for bootstrapping.
:param idx: Index in BrainInfo of agent.
:return: Value estimate.
"""
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Generates value estimates for bootstrapping.
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:param idx: Index in BrainInfo of agent.
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train | PPOPolicy.update_reward | Updates reward value for policy.
:param new_reward: New reward to save. | ml-agents/mlagents/trainers/ppo/policy.py | def update_reward(self, new_reward):
"""
Updates reward value for policy.
:param new_reward: New reward to save.
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Updates reward value for policy.
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train | BCTrainer.add_experiences | Adds experiences to each agent's experience history.
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:param next_info: Next AllBrainInfo (Dictionary of all current brains and corresponding BrainInfo).
:param take_action_outputs: The outputs ... | ml-agents/mlagents/trainers/bc/trainer.py | def add_experiences(self, curr_info: AllBrainInfo, next_info: AllBrainInfo,
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Adds experiences to each agent's experience history.
:param curr_info: Current AllBrainInfo (Dictionary of all current brains and corresponding BrainInfo).
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Adds experiences to each agent's experience history.
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train | BCTrainer.process_experiences | Checks agent histories for processing condition, and processes them as necessary.
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Checks agent histories for processing condition, and processes them as necessary.
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train | BCTrainer.end_episode | A signal that the Episode has ended. The buffer must be reset.
Get only called when the academy resets. | ml-agents/mlagents/trainers/bc/trainer.py | def end_episode(self):
"""
A signal that the Episode has ended. The buffer must be reset.
Get only called when the academy resets.
"""
self.evaluation_buffer.reset_local_buffers()
for agent_id in self.cumulative_rewards:
self.cumulative_rewards[agent_id] = 0
... | def end_episode(self):
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A signal that the Episode has ended. The buffer must be reset.
Get only called when the academy resets.
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self.evaluation_buffer.reset_local_buffers()
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train | BCTrainer.update_policy | Updates the policy. | ml-agents/mlagents/trainers/bc/trainer.py | def update_policy(self):
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train | LearningModel.create_global_steps | Creates TF ops to track and increment global training step. | ml-agents/mlagents/trainers/models.py | def create_global_steps():
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"""Creates TF ops to track and increment global training step."""
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train | LearningModel.create_visual_input | Creates image input op.
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train | LearningModel.create_vector_input | Creates ops for vector observation input.
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Creates ops for vector observation input.
:param name: Name of the placeholder op.
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Creates ops for vector observation input.
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train | LearningModel.create_vector_observation_encoder | Builds a set of hidden state encoders.
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Builds a set of hidden state encoders.
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train | LearningModel.create_visual_observation_encoder | Builds a set of visual (CNN) encoders.
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Builds a set of visual (CNN) encoders.
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train | LearningModel.create_discrete_action_masking_layer | Creates a masking layer for the discrete actions
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:param action_masks: The mask for the logits. Must be of dimension [None x total_number_of_action]
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Creates a masking layer for the discrete actions
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Creates a masking layer for the discrete actions
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train | LearningModel.create_observation_streams | Creates encoding stream for observations.
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:param num_layers: Number of hidden linear layers in stream.
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Creates encoding stream for observations.
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Creates encoding stream for observations.
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train | LearningModel.create_recurrent_encoder | Builds a recurrent encoder for either state or observations (LSTM).
:param sequence_length: Length of sequence to unroll.
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:param memory_in: The input memory to the LSTM cell.
:param name: The scope of the LSTM cell. | ml-agents/mlagents/trainers/models.py | def create_recurrent_encoder(input_state, memory_in, sequence_length, name='lstm'):
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Builds a recurrent encoder for either state or observations (LSTM).
:param sequence_length: Length of sequence to unroll.
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train | LearningModel.create_cc_actor_critic | Creates Continuous control actor-critic model.
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:param num_layers: Number of hidden linear layers. | ml-agents/mlagents/trainers/models.py | def create_cc_actor_critic(self, h_size, num_layers):
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Creates Continuous control actor-critic model.
:param h_size: Size of hidden linear layers.
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hidden_streams = self.create_observation_streams(2, h_size, num_lay... | def create_cc_actor_critic(self, h_size, num_layers):
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train | LearningModel.create_dc_actor_critic | Creates Discrete control actor-critic model.
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Creates Discrete control actor-critic model.
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train | OnlineBCTrainer.add_experiences | Adds experiences to each agent's experience history.
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:param next_info: Next AllBrainInfo (Dictionary of all current brains and corresponding BrainInfo).
:param take_action_outputs: The outputs ... | ml-agents/mlagents/trainers/bc/online_trainer.py | def add_experiences(self, curr_info: AllBrainInfo, next_info: AllBrainInfo,
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Adds experiences to each agent's experience history.
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Adds experiences to each agent's experience history.
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train | OnlineBCTrainer.process_experiences | Checks agent histories for processing condition, and processes them as necessary.
Processing involves calculating value and advantage targets for model updating step.
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:param next_info: Next AllBrainInfo | ml-agents/mlagents/trainers/bc/online_trainer.py | def process_experiences(self, current_info: AllBrainInfo, next_info: AllBrainInfo):
"""
Checks agent histories for processing condition, and processes them as necessary.
Processing involves calculating value and advantage targets for model updating step.
:param current_info: Current AllB... | def process_experiences(self, current_info: AllBrainInfo, next_info: AllBrainInfo):
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Checks agent histories for processing condition, and processes them as necessary.
Processing involves calculating value and advantage targets for model updating step.
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train | flatten | Yield items from any nested iterable; see REF. | ml-agents/mlagents/trainers/tensorflow_to_barracuda.py | def flatten(items,enter=lambda x:isinstance(x, list)):
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train | replace_strings_in_list | A value in replace_with_strings can be either single string or list of strings | ml-agents/mlagents/trainers/tensorflow_to_barracuda.py | def replace_strings_in_list(array_of_strigs, replace_with_strings):
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train | remove_duplicates_from_list | Preserves the order of elements in the list | ml-agents/mlagents/trainers/tensorflow_to_barracuda.py | def remove_duplicates_from_list(array):
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train | pool_to_HW | Convert from NHWC|NCHW => HW | ml-agents/mlagents/trainers/tensorflow_to_barracuda.py | def pool_to_HW(shape, data_frmt):
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train | convert | Converts a TensorFlow model into a Barracuda model.
:param source_file: The TensorFlow Model
:param target_file: The name of the file the converted model will be saved to
:param trim_unused_by_output: The regexp to match output nodes to remain in the model. All other uconnected nodes will be removed.
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:param source_file: The TensorFlow Model
:param target_file: The name of the file the converted model will be saved to
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Converts a TensorFlow model into a Barracuda model.
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train | demo_to_buffer | Loads demonstration file and uses it to fill training buffer.
:param file_path: Location of demonstration file (.demo).
:param sequence_length: Length of trajectories to fill buffer.
:return: | ml-agents/mlagents/trainers/demo_loader.py | def demo_to_buffer(file_path, sequence_length):
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train | load_demonstration | Loads and parses a demonstration file.
:param file_path: Location of demonstration file (.demo).
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Loads and parses a demonstration file.
:param file_path: Location of demonstration file (.demo).
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Loads and parses a demonstration file.
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train | TrainerController._save_model | Saves current model to checkpoint folder.
:param steps: Current number of steps in training process.
:param saver: Tensorflow saver for session. | ml-agents/mlagents/trainers/trainer_controller.py | def _save_model(self, steps=0):
"""
Saves current model to checkpoint folder.
:param steps: Current number of steps in training process.
:param saver: Tensorflow saver for session.
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Saves current model to checkpoint folder.
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train | TrainerController._write_training_metrics | Write all CSV metrics
:return: | ml-agents/mlagents/trainers/trainer_controller.py | def _write_training_metrics(self):
"""
Write all CSV metrics
:return:
"""
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Write all CSV metrics
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train | TrainerController._export_graph | Exports latest saved models to .nn format for Unity embedding. | ml-agents/mlagents/trainers/trainer_controller.py | def _export_graph(self):
"""
Exports latest saved models to .nn format for Unity embedding.
"""
for brain_name in self.trainers.keys():
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Exports latest saved models to .nn format for Unity embedding.
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train | TrainerController.initialize_trainers | Initialization of the trainers
:param trainer_config: The configurations of the trainers | ml-agents/mlagents/trainers/trainer_controller.py | def initialize_trainers(self, trainer_config: Dict[str, Dict[str, str]]):
"""
Initialization of the trainers
:param trainer_config: The configurations of the trainers
"""
trainer_parameters_dict = {}
for brain_name in self.external_brains:
trainer_parameters =... | def initialize_trainers(self, trainer_config: Dict[str, Dict[str, str]]):
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Initialization of the trainers
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trainer_parameters_dict = {}
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train | TrainerController._reset_env | Resets the environment.
Returns:
A Data structure corresponding to the initial reset state of the
environment. | ml-agents/mlagents/trainers/trainer_controller.py | def _reset_env(self, env: BaseUnityEnvironment):
"""Resets the environment.
Returns:
A Data structure corresponding to the initial reset state of the
environment.
"""
if self.meta_curriculum is not None:
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A Data structure corresponding to the initial reset state of the
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train | SocketCommunicator.close | Sends a shutdown signal to the unity environment, and closes the socket connection. | ml-agents-envs/mlagents/envs/socket_communicator.py | def close(self):
"""
Sends a shutdown signal to the unity environment, and closes the socket connection.
"""
if self._socket is not None and self._conn is not None:
message_input = UnityMessage()
message_input.header.status = 400
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Sends a shutdown signal to the unity environment, and closes the socket connection.
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train | fuse_batchnorm_weights | float sqrt_var = sqrt(var_data[i]);
a_data[i] = bias_data[i] - slope_data[i] * mean_data[i] / sqrt_var;
b_data[i] = slope_data[i] / sqrt_var;
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a_data[i] = bias_data[i] - slope_data[i] * mean_data[i] / sqrt_var;
b_data[i] = slope_data[i] / sqrt_var;
...
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train | get_gae | Computes generalized advantage estimate for use in updating policy.
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train | PPOTrainer.increment_step_and_update_last_reward | Increment the step count of the trainer and Updates the last reward | ml-agents/mlagents/trainers/ppo/trainer.py | def increment_step_and_update_last_reward(self):
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train | PPOTrainer.construct_curr_info | Constructs a BrainInfo which contains the most recent previous experiences for all agents info
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train | BrainInfo.from_agent_proto | Converts list of agent infos to BrainInfo. | ml-agents-envs/mlagents/envs/brain.py | def from_agent_proto(agent_info_list, brain_params):
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train | Dashboard.new | Creates a new, blank dashboard and redirects to it in edit mode | superset/views/dashboard.py | def new(self):
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return redirect(f'... | def new(self):
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db.session.add(new_dashboard)
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train | TagView.get | List all tags a given object has. | superset/views/tags.py | def get(self, object_type, object_id):
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train | TagView.delete | Remove tags from an object. | superset/views/tags.py | def delete(self, object_type, object_id):
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This function can be used to import/export dashboards between multiple
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train | run_migrations_online | Run migrations in 'online' mode.
In this scenario we need to create an Engine
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"""Run migrations in 'online' mode.
In this scenario we need to create an Engine
and associate a connection with the context.
"""
# this callback is used to prevent an auto-migration from being generated
# when there are no changes to the schema
# reference: h... | def run_migrations_online():
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train | BaseViz.get_df | Returns a pandas dataframe based on the query object | superset/viz.py | def get_df(self, query_obj=None):
"""Returns a pandas dataframe based on the query object"""
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 == 'tabl... | def get_df(self, query_obj=None):
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train | BaseViz.query_obj | Building a query object | superset/viz.py | def query_obj(self):
"""Building a query object"""
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 = []
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metrics = self.all_metrics or []
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train | BaseViz.data | This is the data object serialized to the js layer | superset/viz.py | def data(self):
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train | HistogramViz.query_obj | Returns the query object for this visualization | superset/viz.py | def query_obj(self):
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train | HistogramViz.get_data | Returns the chart data | superset/viz.py | def get_data(self, df):
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train | PartitionViz.levels_for | Compute the partition at each `level` from the dataframe. | superset/viz.py | def levels_for(self, time_op, groups, df):
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train | PartitionViz.nest_values | Nest values at each level on the back-end with
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train | BaseDatasource.short_data | Data representation of the datasource sent to the frontend | superset/connectors/base/models.py | def short_data(self):
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train | BaseDatasource.data | Data representation of the datasource sent to the frontend | superset/connectors/base/models.py | def data(self):
"""Data representation of the datasource sent to the frontend"""
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]'))
... | def data(self):
"""Data representation of the datasource sent to the frontend"""
order_by_choices = []
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for s in self.column_names:
s = str(s or '')
order_by_choices.append((json.dumps([s, True]), s + ' [asc]'))
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train | BaseDatasource.get_fk_many_from_list | Update ORM one-to-many list from object list
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train | BaseDatasource.update_from_object | Update datasource from a data structure
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an array of metrics and columns objects. This method
receives the object from the UI and syncs the datasource to
match it. S... | superset/connectors/base/models.py | def update_from_object(self, obj):
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The UI's table editor crafts a complex data structure that
contains most of the datasource's properties as well as
an array of metrics and columns objects. This method
receives the object from the UI a... | def update_from_object(self, obj):
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train | QueryContext.get_query_result | Returns a pandas dataframe based on the query object | superset/common/query_context.py | def get_query_result(self, query_object):
"""Returns a pandas dataframe based on the 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
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train | QueryContext.df_metrics_to_num | Converting metrics to numeric when pandas.read_sql cannot | superset/common/query_context.py | def df_metrics_to_num(self, df, query_object):
"""Converting metrics to numeric when pandas.read_sql cannot"""
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_nu... | def df_metrics_to_num(self, df, query_object):
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train | QueryContext.get_single_payload | Returns a payload of metadata and data | superset/common/query_context.py | def get_single_payload(self, query_obj):
"""Returns a payload of metadata and data"""
payload = self.get_df_payload(query_obj)
df = payload.get('df')
status = payload.get('status')
if status != utils.QueryStatus.FAILED:
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... | def get_single_payload(self, query_obj):
"""Returns a payload of metadata and data"""
payload = self.get_df_payload(query_obj)
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status = payload.get('status')
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train | QueryContext.get_df_payload | Handles caching around the df paylod retrieval | superset/common/query_context.py | def get_df_payload(self, query_obj, **kwargs):
"""Handles caching around the df paylod retrieval"""
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
stac... | def get_df_payload(self, query_obj, **kwargs):
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cache_key = query_obj.cache_key(
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train | Slice.data | Data used to render slice in templates | superset/models/core.py | def data(self):
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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 {
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train | Slice.get_viz | Creates :py:class:viz.BaseViz object from the url_params_multidict.
:return: object of the 'viz_type' type that is taken from the
url_params_multidict or self.params.
:rtype: :py:class:viz.BaseViz | superset/models/core.py | def get_viz(self, force=False):
"""Creates :py:class:viz.BaseViz object from the url_params_multidict.
:return: object of the 'viz_type' type that is taken from the
url_params_multidict or self.params.
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slice_params = json.loads(self.... | def get_viz(self, force=False):
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train | Slice.import_obj | Inserts or overrides slc in the database.
remote_id and import_time fields in params_dict are set to track the
slice origin and ensure correct overrides for multiple imports.
Slice.perm is used to find the datasources and connect them.
:param Slice slc_to_import: Slice object to import... | superset/models/core.py | def import_obj(cls, slc_to_import, slc_to_override, import_time=None):
"""Inserts or overrides slc in the database.
remote_id and import_time fields in params_dict are set to track the
slice origin and ensure correct overrides for multiple imports.
Slice.perm is used to find the datasou... | def import_obj(cls, slc_to_import, slc_to_override, import_time=None):
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train | Dashboard.import_obj | Imports the dashboard from the object to the database.
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train | Database.get_effective_user | Get the effective user, especially during impersonation.
:param url: SQL Alchemy URL object
:param user_name: Default username
:return: The effective username | superset/models/core.py | def get_effective_user(self, url, user_name=None):
"""
Get the effective user, especially during impersonation.
:param url: SQL Alchemy URL object
:param user_name: Default username
:return: The effective username
"""
effective_username = None
if self.impe... | def get_effective_user(self, url, user_name=None):
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:param user_name: Default username
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train | Database.select_star | Generates a ``select *`` statement in the proper dialect | superset/models/core.py | def select_star(
self, table_name, schema=None, limit=100, show_cols=False,
indent=True, latest_partition=False, cols=None):
"""Generates a ``select *`` statement in the proper dialect"""
eng = self.get_sqla_engine(
schema=schema, source=utils.sources.get('sql_lab', N... | def select_star(
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"""Generates a ``select *`` statement in the proper dialect"""
eng = self.get_sqla_engine(
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train | Database.all_table_names_in_database | Parameters need to be passed as keyword arguments. | superset/models/core.py | def all_table_names_in_database(self, cache=False,
cache_timeout=None, force=False):
"""Parameters need to be passed as keyword arguments."""
if not self.allow_multi_schema_metadata_fetch:
return []
return self.db_engine_spec.fetch_result_sets(self... | def all_table_names_in_database(self, cache=False,
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"""Parameters need to be passed as keyword arguments."""
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train | Database.all_table_names_in_schema | Parameters need to be passed as keyword arguments.
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cache_util.memoized_func decorator.
:param schema: schema name
:type schema: str
:param cache: whether cache is enabled for the function
:type cache: bool
:param cac... | superset/models/core.py | def all_table_names_in_schema(self, schema, cache=False,
cache_timeout=None, force=False):
"""Parameters need to be passed as keyword arguments.
For unused parameters, they are referenced in
cache_util.memoized_func decorator.
:param schema: schema nam... | def all_table_names_in_schema(self, schema, cache=False,
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train | Database.all_view_names_in_schema | Parameters need to be passed as keyword arguments.
For unused parameters, they are referenced in
cache_util.memoized_func decorator.
:param schema: schema name
:type schema: str
:param cache: whether cache is enabled for the function
:type cache: bool
:param cac... | superset/models/core.py | def all_view_names_in_schema(self, schema, cache=False,
cache_timeout=None, force=False):
"""Parameters need to be passed as keyword arguments.
For unused parameters, they are referenced in
cache_util.memoized_func decorator.
:param schema: schema name
... | def all_view_names_in_schema(self, schema, cache=False,
cache_timeout=None, force=False):
"""Parameters need to be passed as keyword arguments.
For unused parameters, they are referenced in
cache_util.memoized_func decorator.
:param schema: schema name
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train | Database.all_schema_names | Parameters need to be passed as keyword arguments.
For unused parameters, they are referenced in
cache_util.memoized_func decorator.
:param cache: whether cache is enabled for the function
:type cache: bool
:param cache_timeout: timeout in seconds for the cache
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"""Parameters need to be passed as keyword arguments.
For unused parameters, they are referenced in
cache_util.memoized_func decorator.
:param cache: whether cache is enabled for the function
:type cache:... | def all_schema_names(self, cache=False, cache_timeout=None, force=False):
"""Parameters need to be passed as keyword arguments.
For unused parameters, they are referenced in
cache_util.memoized_func decorator.
:param cache: whether cache is enabled for the function
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train | Database.grains_dict | Allowing to lookup grain by either label or duration
For backward compatibility | superset/models/core.py | def grains_dict(self):
"""Allowing to lookup grain by either label or duration
For backward compatibility"""
d = {grain.duration: grain for grain in self.grains()}
d.update({grain.label: grain for grain in self.grains()})
return d | def grains_dict(self):
"""Allowing to lookup grain by either label or duration
For backward compatibility"""
d = {grain.duration: grain for grain in self.grains()}
d.update({grain.label: grain for grain in self.grains()})
return d | [
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train | Log.log_this | Decorator to log user actions | superset/models/core.py | def log_this(cls, f):
"""Decorator to log user actions"""
@functools.wraps(f)
def wrapper(*args, **kwargs):
user_id = None
if g.user:
user_id = g.user.get_id()
d = request.form.to_dict() or {}
# request parameters can overwrite pos... | def log_this(cls, f):
"""Decorator to log user actions"""
@functools.wraps(f)
def wrapper(*args, **kwargs):
user_id = None
if g.user:
user_id = g.user.get_id()
d = request.form.to_dict() or {}
# request parameters can overwrite pos... | [
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train | api | A decorator to label an endpoint as an API. Catches uncaught exceptions and
return the response in the JSON format | superset/views/base.py | def api(f):
"""
A decorator to label an endpoint as an API. Catches uncaught exceptions and
return the response in the JSON format
"""
def wraps(self, *args, **kwargs):
try:
return f(self, *args, **kwargs)
except Exception as e:
logging.exception(e)
... | def api(f):
"""
A decorator to label an endpoint as an API. Catches uncaught exceptions and
return the response in the JSON format
"""
def wraps(self, *args, **kwargs):
try:
return f(self, *args, **kwargs)
except Exception as e:
logging.exception(e)
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train | handle_api_exception | A decorator to catch superset exceptions. Use it after the @api decorator above
so superset exception handler is triggered before the handler for generic exceptions. | superset/views/base.py | def handle_api_exception(f):
"""
A decorator to catch superset exceptions. Use it after the @api decorator above
so superset exception handler is triggered before the handler for generic exceptions.
"""
def wraps(self, *args, **kwargs):
try:
return f(self, *args, **kwargs)
... | def handle_api_exception(f):
"""
A decorator to catch superset exceptions. Use it after the @api decorator above
so superset exception handler is triggered before the handler for generic exceptions.
"""
def wraps(self, *args, **kwargs):
try:
return f(self, *args, **kwargs)
... | [
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