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slundberg/shap | shap/benchmark/models.py | cric__lasso | def cric__lasso():
""" Lasso Regression
"""
model = sklearn.linear_model.LogisticRegression(penalty="l1", C=0.002)
# we want to explain the raw probability outputs of the trees
model.predict = lambda X: model.predict_proba(X)[:,1]
return model | python | def cric__lasso():
""" Lasso Regression
"""
model = sklearn.linear_model.LogisticRegression(penalty="l1", C=0.002)
# we want to explain the raw probability outputs of the trees
model.predict = lambda X: model.predict_proba(X)[:,1]
return model | [
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slundberg/shap | shap/benchmark/models.py | cric__ridge | def cric__ridge():
""" Ridge Regression
"""
model = sklearn.linear_model.LogisticRegression(penalty="l2")
# we want to explain the raw probability outputs of the trees
model.predict = lambda X: model.predict_proba(X)[:,1]
return model | python | def cric__ridge():
""" Ridge Regression
"""
model = sklearn.linear_model.LogisticRegression(penalty="l2")
# we want to explain the raw probability outputs of the trees
model.predict = lambda X: model.predict_proba(X)[:,1]
return model | [
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slundberg/shap | shap/benchmark/models.py | cric__decision_tree | def cric__decision_tree():
""" Decision Tree
"""
model = sklearn.tree.DecisionTreeClassifier(random_state=0, max_depth=4)
# we want to explain the raw probability outputs of the trees
model.predict = lambda X: model.predict_proba(X)[:,1]
return model | python | def cric__decision_tree():
""" Decision Tree
"""
model = sklearn.tree.DecisionTreeClassifier(random_state=0, max_depth=4)
# we want to explain the raw probability outputs of the trees
model.predict = lambda X: model.predict_proba(X)[:,1]
return model | [
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slundberg/shap | shap/benchmark/models.py | cric__random_forest | def cric__random_forest():
""" Random Forest
"""
model = sklearn.ensemble.RandomForestClassifier(100, random_state=0)
# we want to explain the raw probability outputs of the trees
model.predict = lambda X: model.predict_proba(X)[:,1]
return model | python | def cric__random_forest():
""" Random Forest
"""
model = sklearn.ensemble.RandomForestClassifier(100, random_state=0)
# we want to explain the raw probability outputs of the trees
model.predict = lambda X: model.predict_proba(X)[:,1]
return model | [
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slundberg/shap | shap/benchmark/models.py | cric__gbm | def cric__gbm():
""" Gradient Boosted Trees
"""
import xgboost
# max_depth and subsample match the params used for the full cric data in the paper
# learning_rate was set a bit higher to allow for faster runtimes
# n_estimators was chosen based on a train/test split of the data
model = xgbo... | python | def cric__gbm():
""" Gradient Boosted Trees
"""
import xgboost
# max_depth and subsample match the params used for the full cric data in the paper
# learning_rate was set a bit higher to allow for faster runtimes
# n_estimators was chosen based on a train/test split of the data
model = xgbo... | [
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slundberg/shap | shap/benchmark/models.py | human__decision_tree | def human__decision_tree():
""" Decision Tree
"""
# build data
N = 1000000
M = 3
X = np.zeros((N,M))
X.shape
y = np.zeros(N)
X[0, 0] = 1
y[0] = 8
X[1, 1] = 1
y[1] = 8
X[2, 0:2] = 1
y[2] = 4
# fit model
xor_model = sklearn.tree.DecisionTreeRegressor(max_d... | python | def human__decision_tree():
""" Decision Tree
"""
# build data
N = 1000000
M = 3
X = np.zeros((N,M))
X.shape
y = np.zeros(N)
X[0, 0] = 1
y[0] = 8
X[1, 1] = 1
y[1] = 8
X[2, 0:2] = 1
y[2] = 4
# fit model
xor_model = sklearn.tree.DecisionTreeRegressor(max_d... | [
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slundberg/shap | shap/plots/summary.py | summary_plot | def summary_plot(shap_values, features=None, feature_names=None, max_display=None, plot_type="dot",
color=None, axis_color="#333333", title=None, alpha=1, show=True, sort=True,
color_bar=True, auto_size_plot=True, layered_violin_max_num_bins=20, class_names=None):
"""Create a SHAP ... | python | def summary_plot(shap_values, features=None, feature_names=None, max_display=None, plot_type="dot",
color=None, axis_color="#333333", title=None, alpha=1, show=True, sort=True,
color_bar=True, auto_size_plot=True, layered_violin_max_num_bins=20, class_names=None):
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slundberg/shap | shap/benchmark/methods.py | kernel_shap_1000_meanref | def kernel_shap_1000_meanref(model, data):
""" Kernel SHAP 1000 mean ref.
color = red_blue_circle(0.5)
linestyle = solid
"""
return lambda X: KernelExplainer(model.predict, kmeans(data, 1)).shap_values(X, nsamples=1000, l1_reg=0) | python | def kernel_shap_1000_meanref(model, data):
""" Kernel SHAP 1000 mean ref.
color = red_blue_circle(0.5)
linestyle = solid
"""
return lambda X: KernelExplainer(model.predict, kmeans(data, 1)).shap_values(X, nsamples=1000, l1_reg=0) | [
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slundberg/shap | shap/benchmark/methods.py | sampling_shap_1000 | def sampling_shap_1000(model, data):
""" IME 1000
color = red_blue_circle(0.5)
linestyle = dashed
"""
return lambda X: SamplingExplainer(model.predict, data).shap_values(X, nsamples=1000) | python | def sampling_shap_1000(model, data):
""" IME 1000
color = red_blue_circle(0.5)
linestyle = dashed
"""
return lambda X: SamplingExplainer(model.predict, data).shap_values(X, nsamples=1000) | [
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slundberg/shap | shap/benchmark/methods.py | tree_shap_independent_200 | def tree_shap_independent_200(model, data):
""" TreeExplainer (independent)
color = red_blue_circle(0)
linestyle = dashed
"""
data_subsample = sklearn.utils.resample(data, replace=False, n_samples=min(200, data.shape[0]), random_state=0)
return TreeExplainer(model, data_subsample, feature_depend... | python | def tree_shap_independent_200(model, data):
""" TreeExplainer (independent)
color = red_blue_circle(0)
linestyle = dashed
"""
data_subsample = sklearn.utils.resample(data, replace=False, n_samples=min(200, data.shape[0]), random_state=0)
return TreeExplainer(model, data_subsample, feature_depend... | [
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slundberg/shap | shap/benchmark/methods.py | mean_abs_tree_shap | def mean_abs_tree_shap(model, data):
""" mean(|TreeExplainer|)
color = red_blue_circle(0.25)
linestyle = solid
"""
def f(X):
v = TreeExplainer(model).shap_values(X)
if isinstance(v, list):
return [np.tile(np.abs(sv).mean(0), (X.shape[0], 1)) for sv in v]
else:
... | python | def mean_abs_tree_shap(model, data):
""" mean(|TreeExplainer|)
color = red_blue_circle(0.25)
linestyle = solid
"""
def f(X):
v = TreeExplainer(model).shap_values(X)
if isinstance(v, list):
return [np.tile(np.abs(sv).mean(0), (X.shape[0], 1)) for sv in v]
else:
... | [
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slundberg/shap | shap/benchmark/methods.py | saabas | def saabas(model, data):
""" Saabas
color = red_blue_circle(0)
linestyle = dotted
"""
return lambda X: TreeExplainer(model).shap_values(X, approximate=True) | python | def saabas(model, data):
""" Saabas
color = red_blue_circle(0)
linestyle = dotted
"""
return lambda X: TreeExplainer(model).shap_values(X, approximate=True) | [
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slundberg/shap | shap/benchmark/methods.py | lime_tabular_regression_1000 | def lime_tabular_regression_1000(model, data):
""" LIME Tabular 1000
"""
return lambda X: other.LimeTabularExplainer(model.predict, data, mode="regression").attributions(X, nsamples=1000) | python | def lime_tabular_regression_1000(model, data):
""" LIME Tabular 1000
"""
return lambda X: other.LimeTabularExplainer(model.predict, data, mode="regression").attributions(X, nsamples=1000) | [
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slundberg/shap | shap/benchmark/methods.py | deep_shap | def deep_shap(model, data):
""" Deep SHAP (DeepLIFT)
"""
if isinstance(model, KerasWrap):
model = model.model
explainer = DeepExplainer(model, kmeans(data, 1).data)
def f(X):
phi = explainer.shap_values(X)
if type(phi) is list and len(phi) == 1:
return phi[0]
... | python | def deep_shap(model, data):
""" Deep SHAP (DeepLIFT)
"""
if isinstance(model, KerasWrap):
model = model.model
explainer = DeepExplainer(model, kmeans(data, 1).data)
def f(X):
phi = explainer.shap_values(X)
if type(phi) is list and len(phi) == 1:
return phi[0]
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slundberg/shap | shap/benchmark/methods.py | expected_gradients | def expected_gradients(model, data):
""" Expected Gradients
"""
if isinstance(model, KerasWrap):
model = model.model
explainer = GradientExplainer(model, data)
def f(X):
phi = explainer.shap_values(X)
if type(phi) is list and len(phi) == 1:
return phi[0]
e... | python | def expected_gradients(model, data):
""" Expected Gradients
"""
if isinstance(model, KerasWrap):
model = model.model
explainer = GradientExplainer(model, data)
def f(X):
phi = explainer.shap_values(X)
if type(phi) is list and len(phi) == 1:
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slundberg/shap | shap/explainers/deep/__init__.py | DeepExplainer.shap_values | def shap_values(self, X, ranked_outputs=None, output_rank_order='max'):
""" Return approximate SHAP values for the model applied to the data given by X.
Parameters
----------
X : list,
if framework == 'tensorflow': numpy.array, or pandas.DataFrame
if framework ==... | python | def shap_values(self, X, ranked_outputs=None, output_rank_order='max'):
""" Return approximate SHAP values for the model applied to the data given by X.
Parameters
----------
X : list,
if framework == 'tensorflow': numpy.array, or pandas.DataFrame
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ray-project/ray | python/ray/rllib/agents/mock.py | _agent_import_failed | def _agent_import_failed(trace):
"""Returns dummy agent class for if PyTorch etc. is not installed."""
class _AgentImportFailed(Trainer):
_name = "AgentImportFailed"
_default_config = with_common_config({})
def _setup(self, config):
raise ImportError(trace)
return _Age... | python | def _agent_import_failed(trace):
"""Returns dummy agent class for if PyTorch etc. is not installed."""
class _AgentImportFailed(Trainer):
_name = "AgentImportFailed"
_default_config = with_common_config({})
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ray-project/ray | python/ray/tune/tune.py | run | def run(run_or_experiment,
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ray-project/ray | python/ray/tune/tune.py | run_experiments | def run_experiments(experiments,
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verbose=2,
resume=False,
queue_trials=False,
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verbose=2,
resume=False,
queue_trials=False,
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ray-project/ray | python/ray/experimental/streaming/communication.py | DataOutput._flush | def _flush(self, close=False):
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ray-project/ray | python/ray/rllib/models/preprocessors.py | get_preprocessor | def get_preprocessor(space):
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if isinstance(space, gym.spaces.Discrete):
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"""Returns an appropriate preprocessor class for the given space."""
legacy_patch_shapes(space)
obs_shape = space.shape
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ray-project/ray | python/ray/rllib/models/preprocessors.py | legacy_patch_shapes | def legacy_patch_shapes(space):
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ray-project/ray | python/ray/rllib/models/preprocessors.py | GenericPixelPreprocessor.transform | def transform(self, observation):
"""Downsamples images from (210, 160, 3) by the configured factor."""
self.check_shape(observation)
scaled = observation[25:-25, :, :]
if self._dim < 84:
scaled = cv2.resize(scaled, (84, 84))
# OpenAI: Resize by half, then down to 42x... | python | def transform(self, observation):
"""Downsamples images from (210, 160, 3) by the configured factor."""
self.check_shape(observation)
scaled = observation[25:-25, :, :]
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ray-project/ray | python/ray/rllib/optimizers/aso_minibatch_buffer.py | MinibatchBuffer.get | def get(self):
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buf: Data item saved from inqueue.
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ray-project/ray | python/ray/tune/trainable.py | Trainable.train | def train(self):
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ray-project/ray | python/ray/tune/trainable.py | Trainable.delete_checkpoint | def delete_checkpoint(self, checkpoint_dir):
"""Removes subdirectory within checkpoint_folder
Parameters
----------
checkpoint_dir : path to checkpoint
"""
if os.path.isfile(checkpoint_dir):
shutil.rmtree(os.path.dirname(checkpoint_dir))
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... | python | def delete_checkpoint(self, checkpoint_dir):
"""Removes subdirectory within checkpoint_folder
Parameters
----------
checkpoint_dir : path to checkpoint
"""
if os.path.isfile(checkpoint_dir):
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ray-project/ray | python/ray/tune/trainable.py | Trainable.save | def save(self, checkpoint_dir=None):
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Subclasses should override ``_save()`` instead to save state.
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checkpoint_dir (str): Optional dir to place the checkpo... | python | def save(self, checkpoint_dir=None):
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ray-project/ray | python/ray/tune/trainable.py | Trainable.save_to_object | def save_to_object(self):
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Returns:
Object holding checkpoint data.
"""
tmpdir = tempfile.mkdtemp("save_to_object", dir=self.logdir)
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Object holding checkpoint data.
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ray-project/ray | python/ray/tune/trainable.py | Trainable.restore | def restore(self, checkpoint_path):
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ray-project/ray | python/ray/tune/trainable.py | Trainable.restore_from_object | def restore_from_object(self, obj):
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info = pickle.loads(obj)
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ray-project/ray | python/ray/tune/trainable.py | Trainable.export_model | def export_model(self, export_formats, export_dir=None):
"""Exports model based on export_formats.
Subclasses should override _export_model() to actually
export model to local directory.
Args:
export_formats (list): List of formats that should be exported.
expor... | python | def export_model(self, export_formats, export_dir=None):
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ray-project/ray | python/ray/rllib/utils/schedules.py | LinearSchedule.value | def value(self, t):
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ray-project/ray | python/ray/tune/automlboard/common/utils.py | dump_json | def dump_json(json_info, json_file, overwrite=True):
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ray-project/ray | python/ray/tune/automlboard/common/utils.py | unicode2str | def unicode2str(content):
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ray-project/ray | examples/lbfgs/driver.py | LinearModel.loss | def loss(self, xs, ys):
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ray-project/ray | examples/lbfgs/driver.py | LinearModel.grad | def grad(self, xs, ys):
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ray-project/ray | examples/resnet/cifar_input.py | build_data | def build_data(data_path, size, dataset):
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data_path: Filename for cifar10 data.
size: The number of images in the dataset.
dataset: The dataset we are using.
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ray-project/ray | examples/resnet/cifar_input.py | build_input | def build_input(data, batch_size, dataset, train):
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batch_size: Input batch size.
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ray-project/ray | python/ray/scripts/scripts.py | create_or_update | def create_or_update(cluster_config_file, min_workers, max_workers, no_restart,
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ray-project/ray | python/ray/scripts/scripts.py | teardown | def teardown(cluster_config_file, yes, workers_only, cluster_name):
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ray-project/ray | python/ray/scripts/scripts.py | kill_random_node | def kill_random_node(cluster_config_file, yes, cluster_name):
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ray-project/ray | python/ray/scripts/scripts.py | submit | def submit(cluster_config_file, docker, screen, tmux, stop, start,
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"""Uploads and runs a script on the specified cluster.
The script is automatically synced to the following location:
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ray-project/ray | examples/resnet/resnet_model.py | ResNet.build_graph | def build_graph(self):
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self.... | python | def build_graph(self):
"""Build a whole graph for the model."""
self.global_step = tf.Variable(0, trainable=False)
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ray-project/ray | examples/resnet/resnet_model.py | ResNet._build_model | def _build_model(self):
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ray-project/ray | examples/resnet/resnet_model.py | ResNet._build_train_op | def _build_train_op(self):
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"""Build training specific ops for the graph."""
num_gpus = self.hps.num_gpus if self.hps.num_gpus != 0 else 1
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ray-project/ray | examples/resnet/resnet_model.py | ResNet._batch_norm | def _batch_norm(self, name, x):
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"""Batch normalization."""
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ray-project/ray | examples/resnet/resnet_model.py | ResNet._decay | def _decay(self):
"""L2 weight decay loss."""
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if var.op.name.find(r"DW") > 0:
costs.append(tf.nn.l2_loss(var))
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"""L2 weight decay loss."""
costs = []
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ray-project/ray | examples/resnet/resnet_model.py | ResNet._conv | def _conv(self, name, x, filter_size, in_filters, out_filters, strides):
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ray-project/ray | examples/resnet/resnet_model.py | ResNet._fully_connected | def _fully_connected(self, x, out_dim):
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ray-project/ray | python/ray/rllib/agents/qmix/qmix_policy_graph.py | _mac | def _mac(model, obs, h):
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ray-project/ray | python/ray/rllib/agents/qmix/qmix_policy_graph.py | QMixLoss.forward | def forward(self, rewards, actions, terminated, mask, obs, action_mask):
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rewards: Tensor of shape [B, T-1, n_agents]
actions: Tensor of shape [B, T-1, n_agents]
terminated: Tensor of shape [B, T-1, n_agents]
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rewards: Tensor of shape [B, T-1, n_agents]
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ray-project/ray | python/ray/rllib/agents/qmix/qmix_policy_graph.py | QMixPolicyGraph._unpack_observation | def _unpack_observation(self, obs_batch):
"""Unpacks the action mask / tuple obs from agent grouping.
Returns:
obs (Tensor): flattened obs tensor of shape [B, n_agents, obs_size]
mask (Tensor): action mask, if any
"""
unpacked = _unpack_obs(
np.array(... | python | def _unpack_observation(self, obs_batch):
"""Unpacks the action mask / tuple obs from agent grouping.
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obs (Tensor): flattened obs tensor of shape [B, n_agents, obs_size]
mask (Tensor): action mask, if any
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ray-project/ray | python/ray/experimental/named_actors.py | get_actor | def get_actor(name):
"""Get a named actor which was previously created.
If the actor doesn't exist, an exception will be raised.
Args:
name: The name of the named actor.
Returns:
The ActorHandle object corresponding to the name.
"""
actor_name = _calculate_key(name)
pickle... | python | def get_actor(name):
"""Get a named actor which was previously created.
If the actor doesn't exist, an exception will be raised.
Args:
name: The name of the named actor.
Returns:
The ActorHandle object corresponding to the name.
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actor_name = _calculate_key(name)
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ray-project/ray | python/ray/experimental/named_actors.py | register_actor | def register_actor(name, actor_handle):
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Args:
name: The name of the named actor.
actor_handle: The actor object to be associated with this name
"""
if not isinstance(name, str):
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"""Register a named actor under a string key.
Args:
name: The name of the named actor.
actor_handle: The actor object to be associated with this name
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if not isinstance(name, str):
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ray-project/ray | python/ray/autoscaler/autoscaler.py | check_extraneous | def check_extraneous(config, schema):
"""Make sure all items of config are in schema"""
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if k not in schema:
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ray-project/ray | python/ray/autoscaler/autoscaler.py | validate_config | def validate_config(config, schema=CLUSTER_CONFIG_SCHEMA):
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check_extraneous(config, schema) | python | def validate_config(config, schema=CLUSTER_CONFIG_SCHEMA):
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ray-project/ray | python/ray/parameter.py | RayParams.update | def update(self, **kwargs):
"""Update the settings according to the keyword arguments.
Args:
kwargs: The keyword arguments to set corresponding fields.
"""
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setattr(self, arg, kwargs[arg])
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kwargs: The keyword arguments to set corresponding fields.
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ray-project/ray | python/ray/parameter.py | RayParams.update_if_absent | def update_if_absent(self, **kwargs):
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kwargs: The keyword arguments to set corresponding fields.
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ray-project/ray | python/ray/actor.py | compute_actor_handle_id | def compute_actor_handle_id(actor_handle_id, num_forks):
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ray-project/ray | python/ray/actor.py | compute_actor_handle_id_non_forked | def compute_actor_handle_id_non_forked(actor_handle_id, current_task_id):
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ray-project/ray | python/ray/actor.py | method | def method(*args, **kwargs):
"""Annotate an actor method.
.. code-block:: python
@ray.remote
class Foo(object):
@ray.method(num_return_vals=2)
def bar(self):
return 1, 2
f = Foo.remote()
_, _ = f.bar.remote()
Args:
num_retu... | python | def method(*args, **kwargs):
"""Annotate an actor method.
.. code-block:: python
@ray.remote
class Foo(object):
@ray.method(num_return_vals=2)
def bar(self):
return 1, 2
f = Foo.remote()
_, _ = f.bar.remote()
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ray-project/ray | python/ray/actor.py | exit_actor | def exit_actor():
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"""
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This function is used to disconnect an actor and exit the worker.
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ray-project/ray | python/ray/actor.py | get_checkpoints_for_actor | def get_checkpoints_for_actor(actor_id):
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"""
checkpoint_info = ray.worker.global_state.actor_checkpoint_info(actor_id)
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return []
checkpoi... | python | def get_checkpoints_for_actor(actor_id):
"""Get the available checkpoints for the given actor ID, return a list
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ray-project/ray | python/ray/actor.py | ActorClass.remote | def remote(self, *args, **kwargs):
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ray-project/ray | python/ray/actor.py | ActorClass._remote | def _remote(self,
args=None,
kwargs=None,
num_cpus=None,
num_gpus=None,
resources=None):
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args=None,
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ray-project/ray | python/ray/actor.py | ActorHandle._actor_method_call | def _actor_method_call(self,
method_name,
args=None,
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num_return_vals=None):
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This is the function that executes when
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ray-project/ray | python/ray/actor.py | ActorHandle._serialization_helper | def _serialization_helper(self, ray_forking):
"""This is defined in order to make pickling work.
Args:
ray_forking: True if this is being called because Ray is forking
the actor handle and false if it is being called by pickling.
Returns:
A dictionary of... | python | def _serialization_helper(self, ray_forking):
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ray_forking: True if this is being called because Ray is forking
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ray-project/ray | python/ray/actor.py | ActorHandle._deserialization_helper | def _deserialization_helper(self, state, ray_forking):
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state: The serialized state of the actor handle.
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ray-project/ray | python/ray/rllib/optimizers/multi_gpu_impl.py | LocalSyncParallelOptimizer.load_data | def load_data(self, sess, inputs, state_inputs):
"""Bulk loads the specified inputs into device memory.
The shape of the inputs must conform to the shapes of the input
placeholders this optimizer was constructed with.
The data is split equally across all the devices. If the data is not... | python | def load_data(self, sess, inputs, state_inputs):
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ray-project/ray | python/ray/rllib/optimizers/multi_gpu_impl.py | LocalSyncParallelOptimizer.optimize | def optimize(self, sess, batch_index):
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ray-project/ray | python/ray/tune/automl/genetic_searcher.py | GeneticSearch._next_generation | def _next_generation(self, sorted_trials):
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as candidates to generate the next generation. The action could
be selection, crossover and mutation according corresponding
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"""Generate genes (encodings) for the next generation.
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ray-project/ray | python/ray/tune/automl/genetic_searcher.py | GeneticSearch._selection | def _selection(candidate):
"""Perform selection action to candidates.
For example, new gene = sample_1 + the 5th bit of sample2.
Args:
candidate: List of candidate genes (encodings).
Examples:
>>> # Genes that represent 3 parameters
>>> gene1 = np.a... | python | def _selection(candidate):
"""Perform selection action to candidates.
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ray-project/ray | python/ray/tune/automl/genetic_searcher.py | GeneticSearch._crossover | def _crossover(candidate):
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candidate: List of candidate genes (encodings).
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>>> # Genes that represent 3 parameters
>>> gene1 = np.array([... | python | def _crossover(candidate):
"""Perform crossover action to candidates.
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candidate: List of candidate genes (encodings).
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ray-project/ray | python/ray/tune/automl/genetic_searcher.py | GeneticSearch._mutation | def _mutation(candidate, rate=0.1):
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candidate: List of candidate genes (encodings).
rate: Percentage of mutation bits
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>>> # Genes that repr... | python | def _mutation(candidate, rate=0.1):
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ray-project/ray | python/ray/tune/scripts.py | list_trials | def list_trials(experiment_path, sort, output, filter_op, columns,
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if columns:
columns = columns.split(",")
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commands.list... | python | def list_trials(experiment_path, sort, output, filter_op, columns,
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ray-project/ray | python/ray/tune/scripts.py | list_experiments | def list_experiments(project_path, sort, output, filter_op, columns):
"""Lists experiments in the directory subtree."""
if columns:
columns = columns.split(",")
commands.list_experiments(project_path, sort, output, filter_op, columns) | python | def list_experiments(project_path, sort, output, filter_op, columns):
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ray-project/ray | python/ray/tune/ray_trial_executor.py | RayTrialExecutor._train | def _train(self, trial):
"""Start one iteration of training and save remote id."""
assert trial.status == Trial.RUNNING, trial.status
remote = trial.runner.train.remote()
# Local Mode
if isinstance(remote, dict):
remote = _LocalWrapper(remote)
self._running... | python | def _train(self, trial):
"""Start one iteration of training and save remote id."""
assert trial.status == Trial.RUNNING, trial.status
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# Local Mode
if isinstance(remote, dict):
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ray-project/ray | python/ray/tune/ray_trial_executor.py | RayTrialExecutor._start_trial | def _start_trial(self, trial, checkpoint=None):
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Raises:
ValueError if restoring from checkpoint fails.
"""
prior_status = trial.status
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"""Starts trial and restores last result if trial was paused.
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ray-project/ray | python/ray/tune/ray_trial_executor.py | RayTrialExecutor._stop_trial | def _stop_trial(self, trial, error=False, error_msg=None,
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Stops this trial, releasing all allocating resources. If stopping the
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"""Stops this trial.
Stops this trial, releasing all allocating resources. If stopping the
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ray-project/ray | python/ray/tune/ray_trial_executor.py | RayTrialExecutor.start_trial | def start_trial(self, trial, checkpoint=None):
"""Starts the trial.
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Args:
trial (Trial): Trial to be started.
checkpoint (Checkpoint): A Python object or path storing the state
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trial (Trial): Trial to be started.
checkpoint (Checkpoint): A Python object or path storing the state
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ray-project/ray | python/ray/tune/ray_trial_executor.py | RayTrialExecutor.stop_trial | def stop_trial(self, trial, error=False, error_msg=None, stop_logger=True):
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ray-project/ray | python/ray/tune/ray_trial_executor.py | RayTrialExecutor.pause_trial | def pause_trial(self, trial):
"""Pauses the trial.
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"""
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ray-project/ray | python/ray/tune/ray_trial_executor.py | RayTrialExecutor.reset_trial | def reset_trial(self, trial, new_config, new_experiment_tag):
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trial (Trial): Trial to be reset.
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trial (Trial): Trial to be reset.
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ray-project/ray | python/ray/tune/ray_trial_executor.py | RayTrialExecutor.fetch_result | def fetch_result(self, trial):
"""Fetches one result of the running trials.
Returns:
Result of the most recent trial training run."""
trial_future = self._find_item(self._running, trial)
if not trial_future:
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"""Fetches one result of the running trials.
Returns:
Result of the most recent trial training run."""
trial_future = self._find_item(self._running, trial)
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ray-project/ray | python/ray/tune/ray_trial_executor.py | RayTrialExecutor.has_resources | def has_resources(self, resources):
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cluster is not resizing very frequently.
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ray-project/ray | python/ray/tune/ray_trial_executor.py | RayTrialExecutor.debug_string | def debug_string(self):
"""Returns a human readable message for printing to the console."""
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ray-project/ray | python/ray/tune/ray_trial_executor.py | RayTrialExecutor.resource_string | def resource_string(self):
"""Returns a string describing the total resources available."""
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ray-project/ray | python/ray/tune/ray_trial_executor.py | RayTrialExecutor.save | def save(self, trial, storage=Checkpoint.DISK):
"""Saves the trial's state to a checkpoint."""
trial._checkpoint.storage = storage
trial._checkpoint.last_result = trial.last_result
if storage == Checkpoint.MEMORY:
trial._checkpoint.value = trial.runner.save_to_object.remote()... | python | def save(self, trial, storage=Checkpoint.DISK):
"""Saves the trial's state to a checkpoint."""
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trial._checkpoint.last_result = trial.last_result
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ray-project/ray | python/ray/tune/ray_trial_executor.py | RayTrialExecutor._checkpoint_and_erase | def _checkpoint_and_erase(self, trial):
"""Checkpoints the model and erases old checkpoints
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Parameters
----------
trial : trial to save
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trial : trial to save
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ray-project/ray | python/ray/tune/ray_trial_executor.py | RayTrialExecutor.restore | def restore(self, trial, checkpoint=None):
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ray-project/ray | python/ray/tune/ray_trial_executor.py | RayTrialExecutor.export_trial_if_needed | def export_trial_if_needed(self, trial):
"""Exports model of this trial based on trial.export_formats.
Return:
A dict that maps ExportFormats to successfully exported models.
"""
if trial.export_formats and len(trial.export_formats) > 0:
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... | python | def export_trial_if_needed(self, trial):
"""Exports model of this trial based on trial.export_formats.
Return:
A dict that maps ExportFormats to successfully exported models.
"""
if trial.export_formats and len(trial.export_formats) > 0:
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"export_mode... | Exports model of this trial based on trial.export_formats.
Return:
A dict that maps ExportFormats to successfully exported models. | [
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] | 4eade036a0505e244c976f36aaa2d64386b5129b | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/tune/ray_trial_executor.py#L547-L556 | train |
ray-project/ray | python/ray/experimental/streaming/streaming.py | Environment.__generate_actor | def __generate_actor(self, instance_id, operator, input, output):
"""Generates an actor that will execute a particular instance of
the logical operator
Attributes:
instance_id (UUID): The id of the instance the actor will execute.
operator (Operator): The metadata of the... | python | def __generate_actor(self, instance_id, operator, input, output):
"""Generates an actor that will execute a particular instance of
the logical operator
Attributes:
instance_id (UUID): The id of the instance the actor will execute.
operator (Operator): The metadata of the... | [
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ray-project/ray | python/ray/experimental/streaming/streaming.py | Environment.__generate_actors | def __generate_actors(self, operator, upstream_channels,
downstream_channels):
"""Generates one actor for each instance of the given logical
operator.
Attributes:
operator (Operator): The logical operator metadata.
upstream_channels (list): A li... | python | def __generate_actors(self, operator, upstream_channels,
downstream_channels):
"""Generates one actor for each instance of the given logical
operator.
Attributes:
operator (Operator): The logical operator metadata.
upstream_channels (list): A li... | [
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] | 4eade036a0505e244c976f36aaa2d64386b5129b | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/experimental/streaming/streaming.py#L173-L210 | train |
ray-project/ray | python/ray/experimental/streaming/streaming.py | Environment._generate_channels | def _generate_channels(self, operator):
"""Generates all output data channels
(see: DataChannel in communication.py) for all instances of
the given logical operator.
The function constructs one data channel for each pair of
communicating operator instances (instance_1,instance_2... | python | def _generate_channels(self, operator):
"""Generates all output data channels
(see: DataChannel in communication.py) for all instances of
the given logical operator.
The function constructs one data channel for each pair of
communicating operator instances (instance_1,instance_2... | [
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ray-project/ray | python/ray/experimental/streaming/streaming.py | Environment.execute | def execute(self):
"""Deploys and executes the physical dataflow."""
self._collect_garbage() # Make sure everything is clean
# TODO (john): Check if dataflow has any 'logical inconsistencies'
# For example, if there is a forward partitioning strategy but
# the number of downstre... | python | def execute(self):
"""Deploys and executes the physical dataflow."""
self._collect_garbage() # Make sure everything is clean
# TODO (john): Check if dataflow has any 'logical inconsistencies'
# For example, if there is a forward partitioning strategy but
# the number of downstre... | [
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ray-project/ray | python/ray/experimental/streaming/streaming.py | DataStream.__register | def __register(self, operator):
"""Registers the given logical operator to the environment and
connects it to its upstream operator (if any).
A call to this function adds a new edge to the logical topology.
Attributes:
operator (Operator): The metadata of the logical opera... | python | def __register(self, operator):
"""Registers the given logical operator to the environment and
connects it to its upstream operator (if any).
A call to this function adds a new edge to the logical topology.
Attributes:
operator (Operator): The metadata of the logical opera... | [
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ray-project/ray | python/ray/experimental/streaming/streaming.py | DataStream.set_parallelism | def set_parallelism(self, num_instances):
"""Sets the number of instances for the source operator of the stream.
Attributes:
num_instances (int): The level of parallelism for the source
operator of the stream.
"""
assert (num_instances > 0)
self.env._se... | python | def set_parallelism(self, num_instances):
"""Sets the number of instances for the source operator of the stream.
Attributes:
num_instances (int): The level of parallelism for the source
operator of the stream.
"""
assert (num_instances > 0)
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ray-project/ray | python/ray/experimental/streaming/streaming.py | DataStream.map | def map(self, map_fn, name="Map"):
"""Applies a map operator to the stream.
Attributes:
map_fn (function): The user-defined logic of the map.
"""
op = Operator(
_generate_uuid(),
OpType.Map,
name,
map_fn,
num_insta... | python | def map(self, map_fn, name="Map"):
"""Applies a map operator to the stream.
Attributes:
map_fn (function): The user-defined logic of the map.
"""
op = Operator(
_generate_uuid(),
OpType.Map,
name,
map_fn,
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