code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
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def train(self, states, actions, next_states, rewards, done, weights=None):
"""
Train DDPG
Args:
states
actions
next_states
rewards
done
weights (optional): Weights for importance sampling
"""
if weights is ... |
Train DDPG
Args:
states
actions
next_states
rewards
done
weights (optional): Weights for importance sampling
| train | python | keiohta/tf2rl | tf2rl/algos/ddpg.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/ddpg.py | MIT |
def compute_td_error(self, states, actions, next_states, rewards, dones):
"""
Compute TD error
Args:
states
actions
next_states
rewars
dones
Returns
tf.Tensor: TD error
"""
if isinstance(actions, tf... |
Compute TD error
Args:
states
actions
next_states
rewars
dones
Returns
tf.Tensor: TD error
| compute_td_error | python | keiohta/tf2rl | tf2rl/algos/ddpg.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/ddpg.py | MIT |
def __init__(
self,
state_shape,
action_dim,
q_func=None,
name="DQN",
lr=0.001,
adam_eps=1e-07,
units=(32, 32),
epsilon=0.1,
epsilon_min=None,
epsilon_decay_step=int(1e6),
n_wa... |
Initialize DQN agent
Args:
state_shape (iterable of int): Observation space shape
action_dim (int): Dimension of discrete action
q_function (QFunc): Custom Q function class. If ``None`` (default), Q function is constructed with ``QFunc``.
name (str): Nam... | __init__ | python | keiohta/tf2rl | tf2rl/algos/dqn.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/dqn.py | MIT |
def get_action(self, state, test=False, tensor=False):
"""
Get action
Args:
state: Observation state
test (bool): When ``False`` (default), policy returns exploratory action.
tensor (bool): When ``True``, return type is ``tf.Tensor``
Returns:
... |
Get action
Args:
state: Observation state
test (bool): When ``False`` (default), policy returns exploratory action.
tensor (bool): When ``True``, return type is ``tf.Tensor``
Returns:
tf.Tensor or np.ndarray or float: Selected action
| get_action | python | keiohta/tf2rl | tf2rl/algos/dqn.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/dqn.py | MIT |
def train(self, states, actions, next_states, rewards, done, weights=None):
"""
Train DQN
Args:
states
actions
next_states
rewards
done
weights (optional): Weights for importance sampling
"""
if weights is N... |
Train DQN
Args:
states
actions
next_states
rewards
done
weights (optional): Weights for importance sampling
| train | python | keiohta/tf2rl | tf2rl/algos/dqn.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/dqn.py | MIT |
def compute_td_error(self, states, actions, next_states, rewards, dones):
"""
Compute TD error
Args:
states
actions
next_states
rewars
dones
Returns
tf.Tensor: TD error
"""
if isinstance(actions, tf... |
Compute TD error
Args:
states
actions
next_states
rewars
dones
Returns
tf.Tensor: TD error
| compute_td_error | python | keiohta/tf2rl | tf2rl/algos/dqn.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/dqn.py | MIT |
def get_argument(parser=None):
"""
Create or update argument parser for command line program
Args:
parser (argparse.ArgParser, optional): argument parser
Returns:
argparse.ArgParser: argument parser
"""
parser = OffPolicyAgent.get_argument(parser... |
Create or update argument parser for command line program
Args:
parser (argparse.ArgParser, optional): argument parser
Returns:
argparse.ArgParser: argument parser
| get_argument | python | keiohta/tf2rl | tf2rl/algos/dqn.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/dqn.py | MIT |
def __init__(
self,
state_shape,
units=(32, 32),
lr=0.001,
enable_sn=False,
name="GAIfO",
**kwargs):
"""
Initialize GAIfO
Args:
state_shape (iterable of int):
action_dim (int):
... |
Initialize GAIfO
Args:
state_shape (iterable of int):
action_dim (int):
units (iterable of int): The default is ``(32, 32)``
lr (float): Learning rate. The default is ``0.001``
enable_sn (bool): Whether enable Spectral Normalization. The defa... | __init__ | python | keiohta/tf2rl | tf2rl/algos/gaifo.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/gaifo.py | MIT |
def inference(self, states, actions, next_states):
"""
Infer Reward with GAIfO
Args:
states
actions
next_states
Returns:
tf.Tensor: Reward
"""
assert states.shape == next_states.shape
if states.ndim == 1:
... |
Infer Reward with GAIfO
Args:
states
actions
next_states
Returns:
tf.Tensor: Reward
| inference | python | keiohta/tf2rl | tf2rl/algos/gaifo.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/gaifo.py | MIT |
def __init__(
self,
state_shape,
action_dim,
units=[32, 32],
lr=0.001,
enable_sn=False,
name="GAIL",
**kwargs):
"""
Initialize GAIL
Args:
state_shape (iterable of int):
action... |
Initialize GAIL
Args:
state_shape (iterable of int):
action_dim (int):
units (iterable of int): The default is ``[32, 32]``
lr (float): Learning rate. The default is ``0.001``
enable_sn (bool): Whether enable Spectral Normalization. The defai... | __init__ | python | keiohta/tf2rl | tf2rl/algos/gail.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/gail.py | MIT |
def inference(self, states, actions, next_states):
"""
Infer Reward with GAIL
Args:
states
actions
next_states
Returns:
tf.Tensor: Reward
"""
if states.ndim == actions.ndim == 1:
states = np.expand_dims(states,... |
Infer Reward with GAIL
Args:
states
actions
next_states
Returns:
tf.Tensor: Reward
| inference | python | keiohta/tf2rl | tf2rl/algos/gail.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/gail.py | MIT |
def __init__(
self,
clip=True,
clip_ratio=0.2,
name="PPO",
**kwargs):
"""
Initialize PPO
Args:
clip (bool): Whether clip or not. The default is ``True``.
clip_ratio (float): Probability ratio is clipped between ... |
Initialize PPO
Args:
clip (bool): Whether clip or not. The default is ``True``.
clip_ratio (float): Probability ratio is clipped between ``1-clip_ratio`` and ``1+clip_ratio``.
name (str): Name of agent. The default is ``"PPO"``.
state_shape (iterable of ... | __init__ | python | keiohta/tf2rl | tf2rl/algos/ppo.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/ppo.py | MIT |
def train(self, states, actions, advantages, logp_olds, returns):
"""
Train PPO
Args:
states
actions
advantages
logp_olds
returns
"""
# Train actor and critic
if self.actor_critic is not None:
actor_... |
Train PPO
Args:
states
actions
advantages
logp_olds
returns
| train | python | keiohta/tf2rl | tf2rl/algos/ppo.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/ppo.py | MIT |
def __init__(
self,
state_shape,
action_dim,
name="SAC",
max_action=1.,
lr=3e-4,
lr_alpha=3e-4,
actor_units=(256, 256),
critic_units=(256, 256),
tau=5e-3,
alpha=.2,
auto_alpha=... |
Initialize SAC
Args:
state_shape (iterable of int):
action_dim (int):
name (str): Name of network. The default is ``"SAC"``
max_action (float):
lr (float): Learning rate. The default is ``3e-4``.
lr_alpha (alpha): Learning rate fo... | __init__ | python | keiohta/tf2rl | tf2rl/algos/sac.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/sac.py | MIT |
def get_action(self, state, test=False):
"""
Get action
Args:
state: Observation state
test (bool): When ``False`` (default), policy returns exploratory action.
Returns:
tf.Tensor or float: Selected action
"""
assert isinstance(state,... |
Get action
Args:
state: Observation state
test (bool): When ``False`` (default), policy returns exploratory action.
Returns:
tf.Tensor or float: Selected action
| get_action | python | keiohta/tf2rl | tf2rl/algos/sac.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/sac.py | MIT |
def train(self, states, actions, next_states, rewards, dones, weights=None):
"""
Train SAC
Args:
states
actions
next_states
rewards
done
weights (optional): Weights for importance sampling
"""
if weights is ... |
Train SAC
Args:
states
actions
next_states
rewards
done
weights (optional): Weights for importance sampling
| train | python | keiohta/tf2rl | tf2rl/algos/sac.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/sac.py | MIT |
def compute_td_error(self, states, actions, next_states, rewards, dones):
"""
Compute TD error
Args:
states
actions
next_states
rewars
dones
Returns
np.ndarray: TD error
"""
if isinstance(actions, t... |
Compute TD error
Args:
states
actions
next_states
rewars
dones
Returns
np.ndarray: TD error
| compute_td_error | python | keiohta/tf2rl | tf2rl/algos/sac.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/sac.py | MIT |
def get_argument(parser=None):
"""
Create or update argument parser for command line program
Args:
parser (argparse.ArgParser, optional): argument parser
Returns:
argparse.ArgParser: argument parser
"""
parser = OffPolicyAgent.get_argument(parser... |
Create or update argument parser for command line program
Args:
parser (argparse.ArgParser, optional): argument parser
Returns:
argparse.ArgParser: argument parser
| get_argument | python | keiohta/tf2rl | tf2rl/algos/sac.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/sac.py | MIT |
def __init__(self,
action_dim,
obs_shape=(84, 84, 9),
n_conv_layers=4,
n_conv_filters=32,
feature_dim=50,
tau_encoder=0.05,
tau_critic=0.01,
auto_alpha=True,
lr_sac=1e... |
Initialize SAC+AE
Args:
action_dim (int):
obs_shape: (iterable of int): The default is ``(84, 84, 9)``
n_conv_layers (int): Number of convolutional layers at encoder. The default is ``4``
n_conv_filters (int): Number of filters in convolutional layers. T... | __init__ | python | keiohta/tf2rl | tf2rl/algos/sac_ae.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/sac_ae.py | MIT |
def get_action(self, state, test=False):
"""
Get action
Args:
state: Observation state
test (bool): When ``False`` (default), policy returns exploratory action.
Returns:
tf.Tensor or float: Selected action
Notes:
When the input i... |
Get action
Args:
state: Observation state
test (bool): When ``False`` (default), policy returns exploratory action.
Returns:
tf.Tensor or float: Selected action
Notes:
When the input image have different size, cropped image is used
... | get_action | python | keiohta/tf2rl | tf2rl/algos/sac_ae.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/sac_ae.py | MIT |
def train(self, states, actions, next_states, rewards, dones, weights=None):
"""
Train SAC+AE
Args:
states
actions
next_states
rewards
done
weights (optional): Weights for importance sampling
"""
if weights ... |
Train SAC+AE
Args:
states
actions
next_states
rewards
done
weights (optional): Weights for importance sampling
| train | python | keiohta/tf2rl | tf2rl/algos/sac_ae.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/sac_ae.py | MIT |
def get_argument(parser=None):
"""
Create or update argument parser for command line program
Args:
parser (argparse.ArgParser, optional): argument parser
Returns:
argparse.ArgParser: argument parser
"""
parser = SAC.get_argument(parser)
p... |
Create or update argument parser for command line program
Args:
parser (argparse.ArgParser, optional): argument parser
Returns:
argparse.ArgParser: argument parser
| get_argument | python | keiohta/tf2rl | tf2rl/algos/sac_ae.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/sac_ae.py | MIT |
def __init__(
self,
state_shape,
action_dim,
name="TD3",
actor_update_freq=2,
policy_noise=0.2,
noise_clip=0.5,
critic_units=(400, 300),
**kwargs):
"""
Initialize TD3
Args:
sh... |
Initialize TD3
Args:
shate_shape (iterable of ints): Observation state shape
action_dim (int): Action dimension
name (str): Network name. The default is ``"TD3"``.
actor_update_freq (int): Number of critic updates per one actor upate.
policy_... | __init__ | python | keiohta/tf2rl | tf2rl/algos/td3.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/td3.py | MIT |
def compute_td_error(self, states, actions, next_states, rewards, dones):
"""
Compute TD error
Args:
states
actions
next_states
rewars
dones
Returns:
np.ndarray: Sum of two TD errors.
"""
td_errors1... |
Compute TD error
Args:
states
actions
next_states
rewars
dones
Returns:
np.ndarray: Sum of two TD errors.
| compute_td_error | python | keiohta/tf2rl | tf2rl/algos/td3.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/td3.py | MIT |
def __init__(
self,
state_shape,
action_dim,
units=(32, 32),
n_latent_unit=32,
lr=5e-5,
kl_target=0.5,
reg_param=0.,
enable_sn=False,
enable_gp=False,
name="VAIL",
**kwargs):
... |
Initialize VAIL
Args:
state_shape (iterable of int):
action_dim (int):
units (iterable of int): The default is ``(32, 32)``
lr (float): Learning rate. The default is ``5e-5``
kl_target (float): The default is ``0.5``
reg_param (fl... | __init__ | python | keiohta/tf2rl | tf2rl/algos/vail.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/vail.py | MIT |
def _compute_kl_latent(self, means, log_stds):
r"""
Compute KL divergence of latent spaces over standard Normal
distribution to compute loss in eq.5. The formulation of
KL divergence between two normal distributions is as follows:
ln(\sigma_2 / \sigma_1) + {(\mu_1 - \mu_2)^2... |
Compute KL divergence of latent spaces over standard Normal
distribution to compute loss in eq.5. The formulation of
KL divergence between two normal distributions is as follows:
ln(\sigma_2 / \sigma_1) + {(\mu_1 - \mu_2)^2 + \sigma_1^2 - \sigma_2^2} / (2 * \sigma_2^2)
Sinc... | _compute_kl_latent | python | keiohta/tf2rl | tf2rl/algos/vail.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/vail.py | MIT |
def __init__(
self,
state_shape,
action_dim,
is_discrete,
actor=None,
critic=None,
actor_critic=None,
max_action=1.,
actor_units=(256, 256),
critic_units=(256, 256),
lr_actor=1e-3,
... |
Initialize VPG
Args:
state_shape (iterable of int):
action_dim (int):
is_discrete (bool):
actor:
critic:
actor_critic:
max_action (float): maximum action size.
actor_units (iterable of int): Numbers of unit... | __init__ | python | keiohta/tf2rl | tf2rl/algos/vpg.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/vpg.py | MIT |
def get_action(self, state, test=False):
"""
Get action and probability
Args:
state: Observation state
test (bool): When ``False`` (default), policy returns exploratory action.
Returns:
np.ndarray or float: Selected action
np.ndarray or f... |
Get action and probability
Args:
state: Observation state
test (bool): When ``False`` (default), policy returns exploratory action.
Returns:
np.ndarray or float: Selected action
np.ndarray or float: Log(p)
| get_action | python | keiohta/tf2rl | tf2rl/algos/vpg.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/vpg.py | MIT |
def get_action_and_val(self, state, test=False):
"""
Get action, probability, and critic value
Args:
state: Observation state
test (bool): When ``False`` (default), policy returns exploratory action.
Returns:
np.ndarray: Selected action
n... |
Get action, probability, and critic value
Args:
state: Observation state
test (bool): When ``False`` (default), policy returns exploratory action.
Returns:
np.ndarray: Selected action
np.ndarray: Log(p)
np.ndarray: Critic value
... | get_action_and_val | python | keiohta/tf2rl | tf2rl/algos/vpg.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/vpg.py | MIT |
def train(self, states, actions, advantages, logp_olds, returns):
"""
Train VPG
Args:
states
actions
advantages
logp_olds
returns
"""
# Train actor and critic
actor_loss, logp_news = self._train_actor_body(
... |
Train VPG
Args:
states
actions
advantages
logp_olds
returns
| train | python | keiohta/tf2rl | tf2rl/algos/vpg.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/algos/vpg.py | MIT |
def __init__(self, env, noop_max=30):
"""
Sample initial states by taking random number of no-ops on reset.
No-op is assumed to be action 0.
"""
gym.Wrapper.__init__(self, env)
self.noop_max = noop_max
self.override_num_noops = None
self.noop_action = 0
... |
Sample initial states by taking random number of no-ops on reset.
No-op is assumed to be action 0.
| __init__ | python | keiohta/tf2rl | tf2rl/envs/atari_wrapper.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/envs/atari_wrapper.py | MIT |
def reset(self, **kwargs):
"""
Do no-op action for a number of steps in [1, noop_max].
"""
self.env.reset(**kwargs)
if self.override_num_noops is not None:
noops = self.override_num_noops
else:
noops = self.unwrapped.np_random.randint(
... |
Do no-op action for a number of steps in [1, noop_max].
| reset | python | keiohta/tf2rl | tf2rl/envs/atari_wrapper.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/envs/atari_wrapper.py | MIT |
def __init__(self, env):
"""
Take action on reset for environments that are fixed until firing.
"""
gym.Wrapper.__init__(self, env)
assert env.unwrapped.get_action_meanings()[1] == 'FIRE'
assert len(env.unwrapped.get_action_meanings()) >= 3 |
Take action on reset for environments that are fixed until firing.
| __init__ | python | keiohta/tf2rl | tf2rl/envs/atari_wrapper.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/envs/atari_wrapper.py | MIT |
def __init__(self, env):
"""
Make end-of-life == end-of-episode, but only reset on true game over.
Done by DeepMind for the DQN and co. since it helps value estimation.
"""
gym.Wrapper.__init__(self, env)
self.lives = 0
self.was_real_done = True |
Make end-of-life == end-of-episode, but only reset on true game over.
Done by DeepMind for the DQN and co. since it helps value estimation.
| __init__ | python | keiohta/tf2rl | tf2rl/envs/atari_wrapper.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/envs/atari_wrapper.py | MIT |
def reset(self, **kwargs):
"""
Reset only when lives are exhausted.
This way all states are still reachable even though lives are episodic,
and the learner need not know about any of this behind-the-scenes.
"""
if self.was_real_done:
obs = self.env.reset(**kwa... |
Reset only when lives are exhausted.
This way all states are still reachable even though lives are episodic,
and the learner need not know about any of this behind-the-scenes.
| reset | python | keiohta/tf2rl | tf2rl/envs/atari_wrapper.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/envs/atari_wrapper.py | MIT |
def __init__(self, env, skip=4):
"""
Return only every `skip`-th frame
"""
gym.Wrapper.__init__(self, env)
# most recent raw observations (for max pooling across time steps)
self._obs_buffer = np.zeros(
(2,)+env.observation_space.shape, dtype=np.uint8)
... |
Return only every `skip`-th frame
| __init__ | python | keiohta/tf2rl | tf2rl/envs/atari_wrapper.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/envs/atari_wrapper.py | MIT |
def step(self, action):
"""
Repeat action, sum reward, and max over last observations.
"""
total_reward = 0.0
done = None
for i in range(self._skip):
obs, reward, done, info = self.env.step(action)
if i == self._skip - 2:
self._obs_... |
Repeat action, sum reward, and max over last observations.
| step | python | keiohta/tf2rl | tf2rl/envs/atari_wrapper.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/envs/atari_wrapper.py | MIT |
def __init__(self, env, width=84, height=84, grayscale=True, dict_space_key=None):
"""
Warp frames to 84x84 as done in the Nature paper and later work.
If the environment uses dictionary observations, `dict_space_key` can be specified which indicates which
observation should be warped.
... |
Warp frames to 84x84 as done in the Nature paper and later work.
If the environment uses dictionary observations, `dict_space_key` can be specified which indicates which
observation should be warped.
| __init__ | python | keiohta/tf2rl | tf2rl/envs/atari_wrapper.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/envs/atari_wrapper.py | MIT |
def __init__(self, env, k):
"""
Stack k last frames.
Returns lazy array, which is much more memory efficient.
See also baselines.common.atari_wrappers.LazyFrames
"""
gym.Wrapper.__init__(self, env)
self.k = k
self.frames = deque([], maxlen=k)
shp =... |
Stack k last frames.
Returns lazy array, which is much more memory efficient.
See also baselines.common.atari_wrappers.LazyFrames
| __init__ | python | keiohta/tf2rl | tf2rl/envs/atari_wrapper.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/envs/atari_wrapper.py | MIT |
def __init__(self, frames):
"""
This object ensures that common frames between the observations are only stored once.
It exists purely to optimize memory usage which can be huge for DQN's 1M frames replay
buffers.
This object should only be converted to numpy array before being p... |
This object ensures that common frames between the observations are only stored once.
It exists purely to optimize memory usage which can be huge for DQN's 1M frames replay
buffers.
This object should only be converted to numpy array before being passed to the model.
You'd not b... | __init__ | python | keiohta/tf2rl | tf2rl/envs/atari_wrapper.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/envs/atari_wrapper.py | MIT |
def wrap_deepmind(env, episode_life=True, clip_rewards=True, frame_stack=False, scale=False):
"""
Configure environment for DeepMind-style Atari.
"""
if episode_life:
env = EpisodicLifeEnv(env)
if 'FIRE' in env.unwrapped.get_action_meanings():
env = FireResetEnv(env)
env = WarpFr... |
Configure environment for DeepMind-style Atari.
| wrap_deepmind | python | keiohta/tf2rl | tf2rl/envs/atari_wrapper.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/envs/atari_wrapper.py | MIT |
def wrap_dqn(env, stack_frames=4, episodic_life=True,
reward_clipping=True, wrap_ndarray=False):
"""
Apply a common set of wrappers for Atari games.
"""
assert 'NoFrameskip' in env.spec.id
if episodic_life:
env = EpisodicLifeEnv(env)
env = NoopResetEnv(env, noop_max=30)
... |
Apply a common set of wrappers for Atari games.
| wrap_dqn | python | keiohta/tf2rl | tf2rl/envs/atari_wrapper.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/envs/atari_wrapper.py | MIT |
def __init__(self, env_fn, batch_size, thread_pool=4, max_episode_steps=1000):
"""
Args:
env_fn: function
Function to make an environment
batch_size: int
Batch size
thread_pool: int
Thread pool size
max_epis... |
Args:
env_fn: function
Function to make an environment
batch_size: int
Batch size
thread_pool: int
Thread pool size
max_episode_steps: int
Maximum step of an episode
| __init__ | python | keiohta/tf2rl | tf2rl/envs/multi_thread_env.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/envs/multi_thread_env.py | MIT |
def step(self, actions, name=None):
"""
Args:
actions: tf.Tensor
Actions whose shape is float32[batch_size, dim_action]
name: str
Operator name
Returns:
obs: tf.Tensor
[batch_size, dim_obs]
reward: ... |
Args:
actions: tf.Tensor
Actions whose shape is float32[batch_size, dim_action]
name: str
Operator name
Returns:
obs: tf.Tensor
[batch_size, dim_obs]
reward: tf.Tensor
[batch_size]
... | step | python | keiohta/tf2rl | tf2rl/envs/multi_thread_env.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/envs/multi_thread_env.py | MIT |
def py_step(self, actions):
"""
Args:
actions: np.array
Actions whose shape is [batch_size, dim_action]
Returns:
obs: np.array
reward: np.array
done: np.array
"""
def _process(offset):
for idx_env in ra... |
Args:
actions: np.array
Actions whose shape is [batch_size, dim_action]
Returns:
obs: np.array
reward: np.array
done: np.array
| py_step | python | keiohta/tf2rl | tf2rl/envs/multi_thread_env.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/envs/multi_thread_env.py | MIT |
def experience(self, x):
"""Learn input values without computing the output values of them"""
if self.until is not None and self.count >= self.until:
return
count_x = x.shape[self.batch_axis]
if count_x == 0:
return
self.count += count_x
rate = ... | Learn input values without computing the output values of them | experience | python | keiohta/tf2rl | tf2rl/envs/normalizer.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/envs/normalizer.py | MIT |
def __call__(self, x, update=True):
"""Normalize mean and variance of values based on emprical values.
Args:
x (ndarray or Variable): Input values
update (bool): Flag to learn the input values
Returns:
ndarray or Variable: Normalized output values
"""
... | Normalize mean and variance of values based on emprical values.
Args:
x (ndarray or Variable): Input values
update (bool): Flag to learn the input values
Returns:
ndarray or Variable: Normalized output values
| __call__ | python | keiohta/tf2rl | tf2rl/envs/normalizer.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/envs/normalizer.py | MIT |
def make(id, **kwargs):
r"""
Make gym.Env with version tolerance
Args:
id (str) : Id specifying `gym.Env` registered to `gym.env.registry`.
Valid format is `"^(?:[\w:-]+\/)?([\w:.-]+)-v(\d+)$"`
See https://github.com/openai/gym/blob/v0.21.0/gym/envs/registratio... |
Make gym.Env with version tolerance
Args:
id (str) : Id specifying `gym.Env` registered to `gym.env.registry`.
Valid format is `"^(?:[\w:-]+\/)?([\w:.-]+)-v(\d+)$"`
See https://github.com/openai/gym/blob/v0.21.0/gym/envs/registration.py#L17-L19
Returns:
... | make | python | keiohta/tf2rl | tf2rl/envs/utils.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/envs/utils.py | MIT |
def __init__(
self,
policy,
env,
args,
irl,
expert_obs,
expert_next_obs,
expert_act,
test_env=None):
"""
Initialize Trainer class
Args:
policy: Policy to be trained
... |
Initialize Trainer class
Args:
policy: Policy to be trained
env (gym.Env): Environment for train
args (Namespace or dict): config parameters specified with command line
irl
expert_obs
expert_next_obs
expert_act
... | __init__ | python | keiohta/tf2rl | tf2rl/experiments/irl_trainer.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/experiments/irl_trainer.py | MIT |
def get_argument(parser=None):
"""
Create or update argument parser for command line program
Args:
parser (argparse.ArgParser, optional): argument parser
Returns:
argparse.ArgParser: argument parser
"""
parser = Trainer.get_argument(parser)
... |
Create or update argument parser for command line program
Args:
parser (argparse.ArgParser, optional): argument parser
Returns:
argparse.ArgParser: argument parser
| get_argument | python | keiohta/tf2rl | tf2rl/experiments/irl_trainer.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/experiments/irl_trainer.py | MIT |
def __init__(self, *args, n_eval_episodes_per_model=5, **kwargs):
"""
Initialize ME-TRPO
Args:
policy: Policy to be trained
env (gym.Env): Environment for train
args (Namespace or dict): config parameters specified with command line
test_env (gym.... |
Initialize ME-TRPO
Args:
policy: Policy to be trained
env (gym.Env): Environment for train
args (Namespace or dict): config parameters specified with command line
test_env (gym.Env): Environment for test.
reward_fn (callable): Reward function... | __init__ | python | keiohta/tf2rl | tf2rl/experiments/me_trpo_trainer.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/experiments/me_trpo_trainer.py | MIT |
def predict_next_state(self, obses, acts, idx=None):
"""
Predict Next State
Args:
obses
acts
idx (int): Index number of dynamics mode to use. If ``None`` (default), choose randomly.
Returns:
np.ndarray: next state
"""
is_s... |
Predict Next State
Args:
obses
acts
idx (int): Index number of dynamics mode to use. If ``None`` (default), choose randomly.
Returns:
np.ndarray: next state
| predict_next_state | python | keiohta/tf2rl | tf2rl/experiments/me_trpo_trainer.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/experiments/me_trpo_trainer.py | MIT |
def finish_horizon(self, last_val=0):
"""
TODO: These codes are completly identical to the ones defined in on_policy_trainer.py. Use it.
"""
samples = self.local_buffer._encode_sample(
np.arange(self.local_buffer.get_stored_size()))
rews = np.append(samples["rew"], la... |
TODO: These codes are completly identical to the ones defined in on_policy_trainer.py. Use it.
| finish_horizon | python | keiohta/tf2rl | tf2rl/experiments/me_trpo_trainer.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/experiments/me_trpo_trainer.py | MIT |
def __init__(self, input_dim, output_dim, units=[32, 32], name="DymamicsModel", gpu=0):
"""
Initialize DynamicsModel
Args:
input_dim (int)
output_dim (int)
units (iterable of int): The default is ``[32, 32]``
name (str): The default is ``"Dynamics... |
Initialize DynamicsModel
Args:
input_dim (int)
output_dim (int)
units (iterable of int): The default is ``[32, 32]``
name (str): The default is ``"DynamicsModel"``
gpu (int): The default is ``0``.
| __init__ | python | keiohta/tf2rl | tf2rl/experiments/mpc_trainer.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/experiments/mpc_trainer.py | MIT |
def call(self, inputs):
"""
Call Dynamics Model
Args:
inputs (tf.Tensor)
Returns:
tf.Tensor
"""
features = self.l1(inputs)
features = self.l2(features)
return self.l3(features) |
Call Dynamics Model
Args:
inputs (tf.Tensor)
Returns:
tf.Tensor
| call | python | keiohta/tf2rl | tf2rl/experiments/mpc_trainer.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/experiments/mpc_trainer.py | MIT |
def get_action(self, obs):
"""
Get random action
Args:
obs
Returns:
float: action
"""
return np.random.uniform(
low=-self._max_action,
high=self._max_action,
size=self._act_dim) |
Get random action
Args:
obs
Returns:
float: action
| get_action | python | keiohta/tf2rl | tf2rl/experiments/mpc_trainer.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/experiments/mpc_trainer.py | MIT |
def get_actions(self, obses):
"""
Get batch actions
Args:
obses
Returns:
np.dnarray: batch actions
"""
batch_size = obses.shape[0]
return np.random.uniform(
low=-self._max_action,
high=self._max_action,
... |
Get batch actions
Args:
obses
Returns:
np.dnarray: batch actions
| get_actions | python | keiohta/tf2rl | tf2rl/experiments/mpc_trainer.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/experiments/mpc_trainer.py | MIT |
def __init__(
self,
policy,
env,
args,
reward_fn,
buffer_size=int(1e6),
n_dynamics_model=1,
lr=0.001,
**kwargs):
"""
Initialize MPCTrainer class
Args:
policy: Policy to be tra... |
Initialize MPCTrainer class
Args:
policy: Policy to be trained
env (gym.Env): Environment for train
args (Namespace or dict): config parameters specified with command line
test_env (gym.Env): Environment for test.
reward_fn (callable): Reward... | __init__ | python | keiohta/tf2rl | tf2rl/experiments/mpc_trainer.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/experiments/mpc_trainer.py | MIT |
def predict_next_state(self, obses, acts):
"""
Predict Next State
Args:
obses
acts
Returns:
np.ndarray: next state
"""
obs_diffs = np.zeros_like(obses)
inputs = np.concatenate([obses, acts], axis=1)
for dynamics_model ... |
Predict Next State
Args:
obses
acts
Returns:
np.ndarray: next state
| predict_next_state | python | keiohta/tf2rl | tf2rl/experiments/mpc_trainer.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/experiments/mpc_trainer.py | MIT |
def collect_episodes(self, n_rollout=1):
"""
Collect Episodes
Args:
n_rollout (int): Number of rollout. The default is ``1``
"""
for _ in range(n_rollout):
obs = self._env.reset()
for _ in range(self._episode_max_steps):
act = ... |
Collect Episodes
Args:
n_rollout (int): Number of rollout. The default is ``1``
| collect_episodes | python | keiohta/tf2rl | tf2rl/experiments/mpc_trainer.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/experiments/mpc_trainer.py | MIT |
def fit_dynamics(self, n_epoch=1):
"""
Fit dynamics
Args:
n_epocs (int): Number of epocs to fit
"""
inputs, labels = self._make_inputs_output_pairs(n_epoch)
dataset = tf.data.Dataset.from_tensor_slices((inputs, labels))
dataset = dataset.batch(self._... |
Fit dynamics
Args:
n_epocs (int): Number of epocs to fit
| fit_dynamics | python | keiohta/tf2rl | tf2rl/experiments/mpc_trainer.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/experiments/mpc_trainer.py | MIT |
def get_argument(parser=None):
"""
Create or update argument parser for command line program
Args:
parser (argparse.ArgParser, optional): argument parser
Returns:
argparse.ArgParser: argument parser
"""
parser = Trainer.get_argument(parser)
... |
Create or update argument parser for command line program
Args:
parser (argparse.ArgParser, optional): argument parser
Returns:
argparse.ArgParser: argument parser
| get_argument | python | keiohta/tf2rl | tf2rl/experiments/mpc_trainer.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/experiments/mpc_trainer.py | MIT |
def evaluate_policy(self, total_steps):
"""
Evaluate policy
Args:
total_steps (int): Current total steps of training
"""
avg_test_return = 0.
avg_test_steps = 0
if self._save_test_path:
replay_buffer = get_replay_buffer(
se... |
Evaluate policy
Args:
total_steps (int): Current total steps of training
| evaluate_policy | python | keiohta/tf2rl | tf2rl/experiments/on_policy_trainer.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/experiments/on_policy_trainer.py | MIT |
def __init__(
self,
policy,
env,
args,
test_env=None):
"""
Initialize Trainer class
Args:
policy: Policy to be trained
env (gym.Env): Environment for train
args (Namespace or dict): config parameters... |
Initialize Trainer class
Args:
policy: Policy to be trained
env (gym.Env): Environment for train
args (Namespace or dict): config parameters specified with command line
test_env (gym.Env): Environment for test.
| __init__ | python | keiohta/tf2rl | tf2rl/experiments/trainer.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/experiments/trainer.py | MIT |
def evaluate_policy_continuously(self):
"""
Periodically search the latest checkpoint, and keep evaluating with the latest model until user kills process.
"""
if self._model_dir is None:
self.logger.error("Please specify model directory by passing command line argument `--mod... |
Periodically search the latest checkpoint, and keep evaluating with the latest model until user kills process.
| evaluate_policy_continuously | python | keiohta/tf2rl | tf2rl/experiments/trainer.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/experiments/trainer.py | MIT |
def get_argument(parser=None):
"""
Create or update argument parser for command line program
Args:
parser (argparse.ArgParser, optional): argument parser
Returns:
argparse.ArgParser: argument parser
"""
if parser is None:
parser = arg... |
Create or update argument parser for command line program
Args:
parser (argparse.ArgParser, optional): argument parser
Returns:
argparse.ArgParser: argument parser
| get_argument | python | keiohta/tf2rl | tf2rl/experiments/trainer.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/experiments/trainer.py | MIT |
def discount_cumsum(x, discount):
"""Forked from rllab for computing discounted cumulative sums of vectors.
Args:
x: np.ndarray or tf.Tensor
Vector of inputs
discount: float
Discount factor
Returns:
Discounted cumulative summation. If input is [x0, x1, x2], ... | Forked from rllab for computing discounted cumulative sums of vectors.
Args:
x: np.ndarray or tf.Tensor
Vector of inputs
discount: float
Discount factor
Returns:
Discounted cumulative summation. If input is [x0, x1, x2], then the output is:
[x0 + dis... | discount_cumsum | python | keiohta/tf2rl | tf2rl/misc/discount_cumsum.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/misc/discount_cumsum.py | MIT |
def huber_loss(x, delta=1.):
"""
Args:
x: np.ndarray or tf.Tensor
Values to compute the huber loss.
delta: float
Positive floating point value. Represents the
maximum possible gradient magnitude.
Returns: tf.Tensor
The huber loss.
"""
del... |
Args:
x: np.ndarray or tf.Tensor
Values to compute the huber loss.
delta: float
Positive floating point value. Represents the
maximum possible gradient magnitude.
Returns: tf.Tensor
The huber loss.
| huber_loss | python | keiohta/tf2rl | tf2rl/misc/huber_loss.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/misc/huber_loss.py | MIT |
def observe(self, x):
"""Compute next mean and std
Args:
x: float
Input data.
"""
self._n.assign_add(1)
numerator = x - self._mean
self._mean.assign_add((x - self._mean) / self._n)
self._mean_diff.assign_add(numerator * (x - self._mean... | Compute next mean and std
Args:
x: float
Input data.
| observe | python | keiohta/tf2rl | tf2rl/misc/normalizer.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/misc/normalizer.py | MIT |
def periodically(body, period, name="periodically"):
"""
Periodically performs a tensorflow op.
The body tensorflow op will be executed every `period` times the periodically
op is executed. More specifically, with `n` the number of times the op has
been executed, the body will be executed when `n` ... |
Periodically performs a tensorflow op.
The body tensorflow op will be executed every `period` times the periodically
op is executed. More specifically, with `n` the number of times the op has
been executed, the body will be executed when `n` is a non zero positive
multiple of `period` (i.e. there ... | periodically | python | keiohta/tf2rl | tf2rl/misc/periodic_ops.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/misc/periodic_ops.py | MIT |
def is_return_code_zero(args):
"""
Return true if the given command's return code is zero.
All the messages to stdout or stderr are suppressed.
forked from https://github.com/chainer/chainerrl/blob/master/chainerrl/misc/is_return_code_zero.py
"""
with open(os.devnull, 'wb') as FNULL:
try... |
Return true if the given command's return code is zero.
All the messages to stdout or stderr are suppressed.
forked from https://github.com/chainer/chainerrl/blob/master/chainerrl/misc/is_return_code_zero.py
| is_return_code_zero | python | keiohta/tf2rl | tf2rl/misc/prepare_output_dir.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/misc/prepare_output_dir.py | MIT |
def prepare_output_dir(args, user_specified_dir=None, argv=None,
time_format='%Y%m%dT%H%M%S.%f', suffix=""):
"""
Prepare a directory for outputting training results.
An output directory, which ends with the current datetime string,
is created. Then the following infomation is save... |
Prepare a directory for outputting training results.
An output directory, which ends with the current datetime string,
is created. Then the following infomation is saved into the directory:
args.txt: command line arguments
command.txt: command itself
environ.txt: environmental varia... | prepare_output_dir | python | keiohta/tf2rl | tf2rl/misc/prepare_output_dir.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/misc/prepare_output_dir.py | MIT |
def _compute_dist(self, states):
"""
Args:
states: np.ndarray or tf.Tensor
Inputs to neural network.
Returns:
tfp.distributions.Categorical
Categorical distribution whose probabilities are
computed using softmax activation... |
Args:
states: np.ndarray or tf.Tensor
Inputs to neural network.
Returns:
tfp.distributions.Categorical
Categorical distribution whose probabilities are
computed using softmax activation of a neural network
| _compute_dist | python | keiohta/tf2rl | tf2rl/policies/tfp_categorical_actor.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/policies/tfp_categorical_actor.py | MIT |
def _compute_dist(self, states):
"""
Args:
states: np.ndarray or tf.Tensor
Inputs to neural network.
Returns:
tfp.distributions.MultivariateNormalDiag
Multivariate normal distribution object whose mean and
standard deviati... |
Args:
states: np.ndarray or tf.Tensor
Inputs to neural network.
Returns:
tfp.distributions.MultivariateNormalDiag
Multivariate normal distribution object whose mean and
standard deviation is output of a neural network
| _compute_dist | python | keiohta/tf2rl | tf2rl/policies/tfp_gaussian_actor.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/policies/tfp_gaussian_actor.py | MIT |
def call(self, states, test=False):
"""
Compute actions and log probabilities of the selected action
"""
dist = self._compute_dist(states)
if test:
raw_actions = dist.mean()
else:
raw_actions = dist.sample()
log_pis = dist.log_prob(raw_acti... |
Compute actions and log probabilities of the selected action
| call | python | keiohta/tf2rl | tf2rl/policies/tfp_gaussian_actor.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/policies/tfp_gaussian_actor.py | MIT |
def random_crop(input_imgs, output_size):
"""
Args:
input_imgs: np.ndarray
Images whose shape is (batch_size, width, height, channels)
output_size: Int
Output width and height size.
Returns:
"""
assert input_imgs.ndim == 4, f"The dimension of input images m... |
Args:
input_imgs: np.ndarray
Images whose shape is (batch_size, width, height, channels)
output_size: Int
Output width and height size.
Returns:
| random_crop | python | keiohta/tf2rl | tf2rl/tools/img_tools.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/tools/img_tools.py | MIT |
def center_crop(img, output_size):
"""
Args:
img: np.ndarray
Input image array. The shape is (width, height, channel)
output_size: int
Width and height size for output image
Returns:
"""
is_single_img = img.ndim == 3
h, w = img.shape[:2] if is_single_i... |
Args:
img: np.ndarray
Input image array. The shape is (width, height, channel)
output_size: int
Width and height size for output image
Returns:
| center_crop | python | keiohta/tf2rl | tf2rl/tools/img_tools.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/tools/img_tools.py | MIT |
def preprocess_img(img, bits=5):
"""Preprocessing image, see https://arxiv.org/abs/1807.03039."""
bins = 2 ** bits
if bits < 8:
obs = tf.cast(tf.floor(img / 2 ** (8 - bits)), dtype=tf.float32)
obs = obs / bins
obs = obs + tf.random.uniform(shape=obs.shape) / bins
obs = obs - 0.5
retu... | Preprocessing image, see https://arxiv.org/abs/1807.03039. | preprocess_img | python | keiohta/tf2rl | tf2rl/tools/img_tools.py | https://github.com/keiohta/tf2rl/blob/master/tf2rl/tools/img_tools.py | MIT |
def resolve_snakefile(path: Optional[Path], allow_missing: bool = False):
"""Get path to the snakefile.
Arguments
---------
path: Optional[Path] -- The path to the snakefile. If not provided, default locations will be tried.
"""
if path is None:
for p in SNAKEFILE_CHOICES:
i... | Get path to the snakefile.
Arguments
---------
path: Optional[Path] -- The path to the snakefile. If not provided, default locations will be tried.
| resolve_snakefile | python | snakemake/snakemake | src/snakemake/api.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/api.py | MIT |
def workflow(
self,
resource_settings: ResourceSettings,
config_settings: Optional[ConfigSettings] = None,
storage_settings: Optional[StorageSettings] = None,
workflow_settings: Optional[WorkflowSettings] = None,
deployment_settings: Optional[DeploymentSettings] = None,
... | Create the workflow API.
Note that if provided, this also changes to the provided workdir.
It will change back to the previous working directory when the workflow API object is deleted.
Arguments
---------
config_settings: ConfigSettings -- The config settings for the workflow.... | workflow | python | snakemake/snakemake | src/snakemake/api.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/api.py | MIT |
def print_exception(self, ex: Exception):
"""Print an exception during workflow execution in a human readable way
(with adjusted line numbers for exceptions raised in Snakefiles and stack
traces that hide Snakemake internals for better readability).
Arguments
---------
e... | Print an exception during workflow execution in a human readable way
(with adjusted line numbers for exceptions raised in Snakefiles and stack
traces that hide Snakemake internals for better readability).
Arguments
---------
ex: Exception -- The exception to print.
| print_exception | python | snakemake/snakemake | src/snakemake/api.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/api.py | MIT |
def dag(
self,
dag_settings: Optional[DAGSettings] = None,
):
"""Create a DAG API.
Arguments
---------
dag_settings: DAGSettings -- The DAG settings for the DAG API.
"""
if dag_settings is None:
dag_settings = DAGSettings()
return... | Create a DAG API.
Arguments
---------
dag_settings: DAGSettings -- The DAG settings for the DAG API.
| dag | python | snakemake/snakemake | src/snakemake/api.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/api.py | MIT |
def lint(self, json: bool = False):
"""Lint the workflow.
Arguments
---------
json: bool -- Whether to print the linting results as JSON.
Returns
-------
True if any lints were printed
"""
workflow = self._get_workflow(check_envvars=False)
... | Lint the workflow.
Arguments
---------
json: bool -- Whether to print the linting results as JSON.
Returns
-------
True if any lints were printed
| lint | python | snakemake/snakemake | src/snakemake/api.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/api.py | MIT |
def execute_workflow(
self,
executor: str = "local",
execution_settings: Optional[ExecutionSettings] = None,
remote_execution_settings: Optional[RemoteExecutionSettings] = None,
scheduling_settings: Optional[SchedulingSettings] = None,
group_settings: Optional[GroupSettin... | Execute the workflow.
Arguments
---------
executor: str -- The executor to use.
execution_settings: ExecutionSettings -- The execution settings for the workflow.
resource_settings: ResourceSettings -- The resource settings for the workflow.
remote_execution_settings: Rem... | execute_workflow | python | snakemake/snakemake | src/snakemake/api.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/api.py | MIT |
def create_report(
self,
reporter: str = "html",
report_settings: Optional[ReportSettingsBase] = None,
):
"""Create a report for the workflow.
Arguments
---------
report: Path -- The path to the report.
report_stylesheet: Optional[Path] -- The path to... | Create a report for the workflow.
Arguments
---------
report: Path -- The path to the report.
report_stylesheet: Optional[Path] -- The path to the report stylesheet.
reporter: str -- report plugin to use (default: html)
| create_report | python | snakemake/snakemake | src/snakemake/api.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/api.py | MIT |
def conda_cleanup_envs(self):
"""Cleanup the conda environments of the workflow."""
self.workflow_api.deployment_settings.imply_deployment_method(
DeploymentMethod.CONDA
)
self.workflow_api._workflow.conda_cleanup_envs() | Cleanup the conda environments of the workflow. | conda_cleanup_envs | python | snakemake/snakemake | src/snakemake/api.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/api.py | MIT |
def conda_create_envs(self):
"""Only create the conda environments of the workflow."""
self.workflow_api.deployment_settings.imply_deployment_method(
DeploymentMethod.CONDA
)
self.workflow_api._workflow.conda_create_envs() | Only create the conda environments of the workflow. | conda_create_envs | python | snakemake/snakemake | src/snakemake/api.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/api.py | MIT |
def conda_list_envs(self):
"""List the conda environments of the workflow."""
self.workflow_api.deployment_settings.imply_deployment_method(
DeploymentMethod.CONDA
)
self.workflow_api._workflow.conda_list_envs() | List the conda environments of the workflow. | conda_list_envs | python | snakemake/snakemake | src/snakemake/api.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/api.py | MIT |
def container_cleanup_images(self):
"""Cleanup the container images of the workflow."""
self.workflow_api.deployment_settings.imply_deployment_method(
DeploymentMethod.APPTAINER
)
self.workflow_api._workflow.container_cleanup_images() | Cleanup the container images of the workflow. | container_cleanup_images | python | snakemake/snakemake | src/snakemake/api.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/api.py | MIT |
def timedelta_to_str(self, x):
"""Conversion of timedelta to str without fractions of seconds"""
mm, ss = divmod(x.seconds, 60)
hh, mm = divmod(mm, 60)
s = "%d:%02d:%02d" % (hh, mm, ss)
if x.days:
def plural(n):
return n, abs(n) != 1 and "s" or ""
... | Conversion of timedelta to str without fractions of seconds | timedelta_to_str | python | snakemake/snakemake | src/snakemake/benchmark.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/benchmark.py | MIT |
def to_tsv(self, extended_fmt):
"""Return ``str`` with the TSV representation of this record"""
def to_tsv_str(x):
"""Conversion of value to str for TSV (None becomes "-")"""
if x is None:
return "-"
elif isinstance(x, float):
return f... | Return ``str`` with the TSV representation of this record | to_tsv | python | snakemake/snakemake | src/snakemake/benchmark.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/benchmark.py | MIT |
def to_tsv_str(x):
"""Conversion of value to str for TSV (None becomes "-")"""
if x is None:
return "-"
elif isinstance(x, float):
return f"{x:.2f}"
else:
return str(x) | Conversion of value to str for TSV (None becomes "-") | to_tsv_str | python | snakemake/snakemake | src/snakemake/benchmark.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/benchmark.py | MIT |
def to_json(self, extended_fmt):
"""Return ``str`` with the JSON representation of this record"""
import json
return json.dumps(
dict(zip(self.get_header(extended_fmt), self.get_benchmarks(extended_fmt))),
sort_keys=True,
) | Return ``str`` with the JSON representation of this record | to_json | python | snakemake/snakemake | src/snakemake/benchmark.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/benchmark.py | MIT |
def benchmarked(pid=None, benchmark_record=None, interval=BENCHMARK_INTERVAL):
"""Measure benchmark parameters while within the context manager
Yields a ``BenchmarkRecord`` with the results (values are set after
leaving context).
If ``pid`` is ``None`` then the PID of the current process will be used.... | Measure benchmark parameters while within the context manager
Yields a ``BenchmarkRecord`` with the results (values are set after
leaving context).
If ``pid`` is ``None`` then the PID of the current process will be used.
If ``benchmark_record`` is ``None`` then a new ``BenchmarkRecord`` is
created... | benchmarked | python | snakemake/snakemake | src/snakemake/benchmark.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/benchmark.py | MIT |
def print_benchmark_tsv(records, file_, extended_fmt):
"""Write benchmark records to file-like the object"""
logger.debug("Benchmarks in TSV format")
print("\t".join(BenchmarkRecord.get_header(extended_fmt)), file=file_)
for r in records:
print(r.to_tsv(extended_fmt), file=file_) | Write benchmark records to file-like the object | print_benchmark_tsv | python | snakemake/snakemake | src/snakemake/benchmark.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/benchmark.py | MIT |
def print_benchmark_jsonl(records, file_, extended_fmt):
"""Write benchmark records to file-like the object"""
logger.debug("Benchmarks in JSONL format")
for r in records:
print(r.to_json(extended_fmt), file=file_) | Write benchmark records to file-like the object | print_benchmark_jsonl | python | snakemake/snakemake | src/snakemake/benchmark.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/benchmark.py | MIT |
def write_benchmark_records(records, path, extended_fmt):
"""Write benchmark records to file at path"""
with open(path, "wt") as f:
if path.endswith(".jsonl"):
print_benchmark_jsonl(records, f, extended_fmt)
else:
print_benchmark_tsv(records, f, extended_fmt) | Write benchmark records to file at path | write_benchmark_records | python | snakemake/snakemake | src/snakemake/benchmark.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/benchmark.py | MIT |
def parse_consider_ancient(
args: Optional[List[str]],
) -> Mapping[str, Set[Union[str, int]]]:
"""Parse command line arguments for marking input files as ancient.
Args:
args: List of RULE=INPUTITEMS pairs, where INPUTITEMS is a comma-separated list
of input item names or indices (0-b... | Parse command line arguments for marking input files as ancient.
Args:
args: List of RULE=INPUTITEMS pairs, where INPUTITEMS is a comma-separated list
of input item names or indices (0-based).
Returns:
A mapping of rules to sets of their ancient input items.
Raises:
... | parse_consider_ancient | python | snakemake/snakemake | src/snakemake/cli.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/cli.py | MIT |
def generate_parser_metadata(parser, args):
"""Given a populated parser, generate the original command along with
metadata that can be handed to a logger to use as needed.
"""
command = "snakemake %s" % " ".join(
parser._source_to_settings["command_line"][""][1]
)
metadata = args.__dict_... | Given a populated parser, generate the original command along with
metadata that can be handed to a logger to use as needed.
| generate_parser_metadata | python | snakemake/snakemake | src/snakemake/cli.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/cli.py | MIT |
def args_to_api(args, parser):
"""Convert argparse args to API calls."""
# handle legacy executor names
if args.dryrun:
args.executor = "dryrun"
elif args.touch:
args.executor = "touch"
elif args.executor is None:
args.executor = "local"
if args.report:
args.rep... | Convert argparse args to API calls. | args_to_api | python | snakemake/snakemake | src/snakemake/cli.py | https://github.com/snakemake/snakemake/blob/master/src/snakemake/cli.py | MIT |
Subsets and Splits
Django Code with Docstrings
Filters Python code examples from Django repository that contain Django-related code, helping identify relevant code snippets for understanding Django framework usage patterns.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves specific code examples from the Flask repository but doesn't provide meaningful analysis or patterns beyond basic data retrieval.
HTTPX Repo Code and Docstrings
Retrieves specific code examples from the httpx repository, which is useful for understanding how particular libraries are used but doesn't provide broader analytical insights about the dataset.
Requests Repo Docstrings & Code
Retrieves code examples with their docstrings and file paths from the requests repository, providing basic filtering but limited analytical value beyond finding specific code samples.
Quart Repo Docstrings & Code
Retrieves code examples with their docstrings from the Quart repository, providing basic code samples but offering limited analytical value for understanding broader patterns or relationships in the dataset.