id int32 0 252k | repo stringlengths 7 55 | path stringlengths 4 127 | func_name stringlengths 1 88 | original_string stringlengths 75 19.8k | language stringclasses 1
value | code stringlengths 75 19.8k | code_tokens list | docstring stringlengths 3 17.3k | docstring_tokens list | sha stringlengths 40 40 | url stringlengths 87 242 |
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24,200 | slundberg/shap | shap/benchmark/plots.py | _human_score_map | def _human_score_map(human_consensus, methods_attrs):
""" Converts human agreement differences to numerical scores for coloring.
"""
v = 1 - min(np.sum(np.abs(methods_attrs - human_consensus)) / (np.abs(human_consensus).sum() + 1), 1.0)
return v | python | def _human_score_map(human_consensus, methods_attrs):
""" Converts human agreement differences to numerical scores for coloring.
"""
v = 1 - min(np.sum(np.abs(methods_attrs - human_consensus)) / (np.abs(human_consensus).sum() + 1), 1.0)
return v | [
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24,201 | slundberg/shap | shap/plots/force_matplotlib.py | draw_bars | def draw_bars(out_value, features, feature_type, width_separators, width_bar):
"""Draw the bars and separators."""
rectangle_list = []
separator_list = []
pre_val = out_value
for index, features in zip(range(len(features)), features):
if feature_type == 'positive':
left_boun... | python | def draw_bars(out_value, features, feature_type, width_separators, width_bar):
"""Draw the bars and separators."""
rectangle_list = []
separator_list = []
pre_val = out_value
for index, features in zip(range(len(features)), features):
if feature_type == 'positive':
left_boun... | [
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24,202 | slundberg/shap | shap/plots/force_matplotlib.py | format_data | def format_data(data):
"""Format data."""
# Format negative features
neg_features = np.array([[data['features'][x]['effect'],
data['features'][x]['value'],
data['featureNames'][x]]
for x in data['features'].keys() if da... | python | def format_data(data):
"""Format data."""
# Format negative features
neg_features = np.array([[data['features'][x]['effect'],
data['features'][x]['value'],
data['featureNames'][x]]
for x in data['features'].keys() if da... | [
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24,203 | slundberg/shap | shap/plots/force_matplotlib.py | draw_additive_plot | def draw_additive_plot(data, figsize, show, text_rotation=0):
"""Draw additive plot."""
# Turn off interactive plot
if show == False:
plt.ioff()
# Format data
neg_features, total_neg, pos_features, total_pos = format_data(data)
# Compute overall metrics
base_value = data['b... | python | def draw_additive_plot(data, figsize, show, text_rotation=0):
"""Draw additive plot."""
# Turn off interactive plot
if show == False:
plt.ioff()
# Format data
neg_features, total_neg, pos_features, total_pos = format_data(data)
# Compute overall metrics
base_value = data['b... | [
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24,204 | slundberg/shap | setup.py | try_run_setup | def try_run_setup(**kwargs):
""" Fails gracefully when various install steps don't work.
"""
try:
run_setup(**kwargs)
except Exception as e:
print(str(e))
if "xgboost" in str(e).lower():
kwargs["test_xgboost"] = False
print("Couldn't install XGBoost for t... | python | def try_run_setup(**kwargs):
""" Fails gracefully when various install steps don't work.
"""
try:
run_setup(**kwargs)
except Exception as e:
print(str(e))
if "xgboost" in str(e).lower():
kwargs["test_xgboost"] = False
print("Couldn't install XGBoost for t... | [
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24,205 | slundberg/shap | shap/explainers/deep/deep_pytorch.py | deeplift_grad | def deeplift_grad(module, grad_input, grad_output):
"""The backward hook which computes the deeplift
gradient for an nn.Module
"""
# first, get the module type
module_type = module.__class__.__name__
# first, check the module is supported
if module_type in op_handler:
if op_handler[m... | python | def deeplift_grad(module, grad_input, grad_output):
"""The backward hook which computes the deeplift
gradient for an nn.Module
"""
# first, get the module type
module_type = module.__class__.__name__
# first, check the module is supported
if module_type in op_handler:
if op_handler[m... | [
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24,206 | slundberg/shap | shap/explainers/deep/deep_pytorch.py | add_interim_values | def add_interim_values(module, input, output):
"""The forward hook used to save interim tensors, detached
from the graph. Used to calculate the multipliers
"""
try:
del module.x
except AttributeError:
pass
try:
del module.y
except AttributeError:
pass
modu... | python | def add_interim_values(module, input, output):
"""The forward hook used to save interim tensors, detached
from the graph. Used to calculate the multipliers
"""
try:
del module.x
except AttributeError:
pass
try:
del module.y
except AttributeError:
pass
modu... | [
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24,207 | slundberg/shap | shap/explainers/deep/deep_pytorch.py | get_target_input | def get_target_input(module, input, output):
"""A forward hook which saves the tensor - attached to its graph.
Used if we want to explain the interim outputs of a model
"""
try:
del module.target_input
except AttributeError:
pass
setattr(module, 'target_input', input) | python | def get_target_input(module, input, output):
"""A forward hook which saves the tensor - attached to its graph.
Used if we want to explain the interim outputs of a model
"""
try:
del module.target_input
except AttributeError:
pass
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24,208 | slundberg/shap | shap/explainers/deep/deep_pytorch.py | PyTorchDeepExplainer.add_handles | def add_handles(self, model, forward_handle, backward_handle):
"""
Add handles to all non-container layers in the model.
Recursively for non-container layers
"""
handles_list = []
for child in model.children():
if 'nn.modules.container' in str(type(child)):
... | python | def add_handles(self, model, forward_handle, backward_handle):
"""
Add handles to all non-container layers in the model.
Recursively for non-container layers
"""
handles_list = []
for child in model.children():
if 'nn.modules.container' in str(type(child)):
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24,209 | slundberg/shap | shap/explainers/deep/deep_pytorch.py | PyTorchDeepExplainer.remove_attributes | def remove_attributes(self, model):
"""
Removes the x and y attributes which were added by the forward handles
Recursively searches for non-container layers
"""
for child in model.children():
if 'nn.modules.container' in str(type(child)):
self.remove_a... | python | def remove_attributes(self, model):
"""
Removes the x and y attributes which were added by the forward handles
Recursively searches for non-container layers
"""
for child in model.children():
if 'nn.modules.container' in str(type(child)):
self.remove_a... | [
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24,210 | slundberg/shap | shap/explainers/tree.py | get_xgboost_json | def get_xgboost_json(model):
""" This gets a JSON dump of an XGBoost model while ensuring the features names are their indexes.
"""
fnames = model.feature_names
model.feature_names = None
json_trees = model.get_dump(with_stats=True, dump_format="json")
model.feature_names = fnames
# this fi... | python | def get_xgboost_json(model):
""" This gets a JSON dump of an XGBoost model while ensuring the features names are their indexes.
"""
fnames = model.feature_names
model.feature_names = None
json_trees = model.get_dump(with_stats=True, dump_format="json")
model.feature_names = fnames
# this fi... | [
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24,211 | slundberg/shap | shap/explainers/tree.py | TreeExplainer.__dynamic_expected_value | def __dynamic_expected_value(self, y):
""" This computes the expected value conditioned on the given label value.
"""
return self.model.predict(self.data, np.ones(self.data.shape[0]) * y, output=self.model_output).mean(0) | python | def __dynamic_expected_value(self, y):
""" This computes the expected value conditioned on the given label value.
"""
return self.model.predict(self.data, np.ones(self.data.shape[0]) * y, output=self.model_output).mean(0) | [
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24,212 | slundberg/shap | shap/explainers/gradient.py | GradientExplainer.shap_values | def shap_values(self, X, nsamples=200, ranked_outputs=None, output_rank_order="max", rseed=None):
""" Return the values for the model applied to X.
Parameters
----------
X : list,
if framework == 'tensorflow': numpy.array, or pandas.DataFrame
if framework == 'pyt... | python | def shap_values(self, X, nsamples=200, ranked_outputs=None, output_rank_order="max", rseed=None):
""" Return the values for the model applied to X.
Parameters
----------
X : list,
if framework == 'tensorflow': numpy.array, or pandas.DataFrame
if framework == 'pyt... | [
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24,213 | slundberg/shap | shap/plots/force.py | save_html | def save_html(out_file, plot_html):
""" Save html plots to an output file.
"""
internal_open = False
if type(out_file) == str:
out_file = open(out_file, "w")
internal_open = True
out_file.write("<html><head><script>\n")
# dump the js code
bundle_path = os.path.join(os.path.... | python | def save_html(out_file, plot_html):
""" Save html plots to an output file.
"""
internal_open = False
if type(out_file) == str:
out_file = open(out_file, "w")
internal_open = True
out_file.write("<html><head><script>\n")
# dump the js code
bundle_path = os.path.join(os.path.... | [
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24,214 | slundberg/shap | shap/explainers/deep/deep_tf.py | tensors_blocked_by_false | def tensors_blocked_by_false(ops):
""" Follows a set of ops assuming their value is False and find blocked Switch paths.
This is used to prune away parts of the model graph that are only used during the training
phase (like dropout, batch norm, etc.).
"""
blocked = []
def recurse(op):
i... | python | def tensors_blocked_by_false(ops):
""" Follows a set of ops assuming their value is False and find blocked Switch paths.
This is used to prune away parts of the model graph that are only used during the training
phase (like dropout, batch norm, etc.).
"""
blocked = []
def recurse(op):
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24,215 | slundberg/shap | shap/explainers/deep/deep_tf.py | TFDeepExplainer.phi_symbolic | def phi_symbolic(self, i):
""" Get the SHAP value computation graph for a given model output.
"""
if self.phi_symbolics[i] is None:
# replace the gradients for all the non-linear activations
# we do this by hacking our way into the registry (TODO: find a public API for t... | python | def phi_symbolic(self, i):
""" Get the SHAP value computation graph for a given model output.
"""
if self.phi_symbolics[i] is None:
# replace the gradients for all the non-linear activations
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24,216 | slundberg/shap | shap/explainers/deep/deep_tf.py | TFDeepExplainer.run | def run(self, out, model_inputs, X):
""" Runs the model while also setting the learning phase flags to False.
"""
feed_dict = dict(zip(model_inputs, X))
for t in self.learning_phase_flags:
feed_dict[t] = False
return self.session.run(out, feed_dict) | python | def run(self, out, model_inputs, X):
""" Runs the model while also setting the learning phase flags to False.
"""
feed_dict = dict(zip(model_inputs, X))
for t in self.learning_phase_flags:
feed_dict[t] = False
return self.session.run(out, feed_dict) | [
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24,217 | slundberg/shap | shap/explainers/deep/deep_tf.py | TFDeepExplainer.custom_grad | def custom_grad(self, op, *grads):
""" Passes a gradient op creation request to the correct handler.
"""
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24,218 | slundberg/shap | shap/benchmark/experiments.py | run_remote_experiments | def run_remote_experiments(experiments, thread_hosts, rate_limit=10):
""" Use ssh to run the experiments on remote machines in parallel.
Parameters
----------
experiments : iterable
Output of shap.benchmark.experiments(...).
thread_hosts : list of strings
Each host has the format "... | python | def run_remote_experiments(experiments, thread_hosts, rate_limit=10):
""" Use ssh to run the experiments on remote machines in parallel.
Parameters
----------
experiments : iterable
Output of shap.benchmark.experiments(...).
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24,219 | slundberg/shap | shap/explainers/kernel.py | kmeans | def kmeans(X, k, round_values=True):
""" Summarize a dataset with k mean samples weighted by the number of data points they
each represent.
Parameters
----------
X : numpy.array or pandas.DataFrame
Matrix of data samples to summarize (# samples x # features)
k : int
Number of m... | python | def kmeans(X, k, round_values=True):
""" Summarize a dataset with k mean samples weighted by the number of data points they
each represent.
Parameters
----------
X : numpy.array or pandas.DataFrame
Matrix of data samples to summarize (# samples x # features)
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24,220 | slundberg/shap | shap/plots/embedding.py | embedding_plot | def embedding_plot(ind, shap_values, feature_names=None, method="pca", alpha=1.0, show=True):
""" Use the SHAP values as an embedding which we project to 2D for visualization.
Parameters
----------
ind : int or string
If this is an int it is the index of the feature to use to color the embeddin... | python | def embedding_plot(ind, shap_values, feature_names=None, method="pca", alpha=1.0, show=True):
""" Use the SHAP values as an embedding which we project to 2D for visualization.
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24,221 | slundberg/shap | shap/benchmark/metrics.py | runtime | def runtime(X, y, model_generator, method_name):
""" Runtime
transform = "negate"
sort_order = 1
"""
old_seed = np.random.seed()
np.random.seed(3293)
# average the method scores over several train/test splits
method_reps = []
for i in range(1):
X_train, X_test, y_train, _ =... | python | def runtime(X, y, model_generator, method_name):
""" Runtime
transform = "negate"
sort_order = 1
"""
old_seed = np.random.seed()
np.random.seed(3293)
# average the method scores over several train/test splits
method_reps = []
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24,222 | slundberg/shap | shap/benchmark/metrics.py | local_accuracy | def local_accuracy(X, y, model_generator, method_name):
""" Local Accuracy
transform = "identity"
sort_order = 2
"""
def score_map(true, pred):
""" Converts local accuracy from % of standard deviation to numerical scores for coloring.
"""
v = min(1.0, np.std(pred - true) / ... | python | def local_accuracy(X, y, model_generator, method_name):
""" Local Accuracy
transform = "identity"
sort_order = 2
"""
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24,223 | slundberg/shap | shap/benchmark/metrics.py | __score_method | def __score_method(X, y, fcounts, model_generator, score_function, method_name, nreps=10, test_size=100, cache_dir="/tmp"):
""" Test an explanation method.
"""
old_seed = np.random.seed()
np.random.seed(3293)
# average the method scores over several train/test splits
method_reps = []
data... | python | def __score_method(X, y, fcounts, model_generator, score_function, method_name, nreps=10, test_size=100, cache_dir="/tmp"):
""" Test an explanation method.
"""
old_seed = np.random.seed()
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24,224 | slundberg/shap | shap/explainers/linear.py | LinearExplainer._estimate_transforms | def _estimate_transforms(self, nsamples):
""" Uses block matrix inversion identities to quickly estimate transforms.
After a bit of matrix math we can isolate a transform matrix (# features x # features)
that is independent of any sample we are explaining. It is the result of averaging over
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24,225 | slundberg/shap | shap/benchmark/models.py | independentlinear60__ffnn | def independentlinear60__ffnn():
""" 4-Layer Neural Network
"""
from keras.models import Sequential
from keras.layers import Dense
model = Sequential()
model.add(Dense(32, activation='relu', input_dim=60))
model.add(Dense(20, activation='relu'))
model.add(Dense(20, activation='relu'))
... | python | def independentlinear60__ffnn():
""" 4-Layer Neural Network
"""
from keras.models import Sequential
from keras.layers import Dense
model = Sequential()
model.add(Dense(32, activation='relu', input_dim=60))
model.add(Dense(20, activation='relu'))
model.add(Dense(20, activation='relu'))
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24,226 | slundberg/shap | shap/benchmark/models.py | cric__gbm | def cric__gbm():
""" Gradient Boosted Trees
"""
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""" 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
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24,227 | 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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24,228 | 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'):
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----------
X : list,
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24,229 | 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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24,230 | ray-project/ray | python/ray/tune/tune.py | run | def run(run_or_experiment,
name=None,
stop=None,
config=None,
resources_per_trial=None,
num_samples=1,
local_dir=None,
upload_dir=None,
trial_name_creator=None,
loggers=None,
sync_function=None,
checkpoint_freq=0,
checkpoint... | python | def run(run_or_experiment,
name=None,
stop=None,
config=None,
resources_per_trial=None,
num_samples=1,
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24,231 | ray-project/ray | python/ray/tune/tune.py | run_experiments | def run_experiments(experiments,
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24,232 | ray-project/ray | python/ray/experimental/streaming/communication.py | DataOutput._flush | def _flush(self, close=False):
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24,233 | ray-project/ray | python/ray/rllib/models/preprocessors.py | get_preprocessor | def get_preprocessor(space):
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"""Returns an appropriate preprocessor class for the given space."""
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24,234 | ray-project/ray | python/ray/rllib/models/preprocessors.py | legacy_patch_shapes | def legacy_patch_shapes(space):
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24,235 | ray-project/ray | python/ray/rllib/optimizers/aso_minibatch_buffer.py | MinibatchBuffer.get | def get(self):
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Returns:
buf: Data item saved from inqueue.
released: True if the item is now removed from the ring buffer.
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Returns:
buf: Data item saved from inqueue.
released: True if the item is now removed from the ring buffer.
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24,236 | ray-project/ray | python/ray/tune/trainable.py | Trainable.train | def train(self):
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Subclasses should override ``_train()`` instead to return results.
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`done` (bool): training is terminated. Filled only if not provided.
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24,237 | ray-project/ray | python/ray/tune/trainable.py | Trainable.save | def save(self, checkpoint_dir=None):
"""Saves the current model state to a checkpoint.
Subclasses should override ``_save()`` instead to save state.
This method dumps additional metadata alongside the saved path.
Args:
checkpoint_dir (str): Optional dir to place the checkpo... | python | def save(self, checkpoint_dir=None):
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24,238 | 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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24,239 | ray-project/ray | python/ray/tune/trainable.py | Trainable.restore_from_object | def restore_from_object(self, obj):
"""Restores training state from a checkpoint object.
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"""
info = pickle.loads(obj)
data = info["data"]
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... | python | def restore_from_object(self, obj):
"""Restores training state from a checkpoint object.
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"""
info = pickle.loads(obj)
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24,240 | 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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24,241 | ray-project/ray | python/ray/rllib/utils/schedules.py | LinearSchedule.value | def value(self, t):
"""See Schedule.value"""
fraction = min(float(t) / max(1, self.schedule_timesteps), 1.0)
return self.initial_p + fraction * (self.final_p - self.initial_p) | python | def value(self, t):
"""See Schedule.value"""
fraction = min(float(t) / max(1, self.schedule_timesteps), 1.0)
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24,242 | 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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Args:
json_info (dict): Information dict to be dumped.
json_file (str): File path to be dumped to.
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24,243 | ray-project/ray | python/ray/tune/automlboard/common/utils.py | parse_json | def parse_json(json_file):
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Args:
json_file (str): File path to be parsed.
Returns:
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24,244 | ray-project/ray | python/ray/tune/automlboard/common/utils.py | parse_multiple_json | def parse_multiple_json(json_file, offset=None):
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24,245 | ray-project/ray | python/ray/tune/automlboard/common/utils.py | unicode2str | def unicode2str(content):
"""Convert the unicode element of the content to str recursively."""
if isinstance(content, dict):
result = {}
for key in content.keys():
result[unicode2str(key)] = unicode2str(content[key])
return result
elif isinstance(content, list):
r... | python | def unicode2str(content):
"""Convert the unicode element of the content to str recursively."""
if isinstance(content, dict):
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result[unicode2str(key)] = unicode2str(content[key])
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24,246 | ray-project/ray | examples/lbfgs/driver.py | LinearModel.loss | def loss(self, xs, ys):
"""Computes the loss of the network."""
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24,247 | ray-project/ray | examples/lbfgs/driver.py | LinearModel.grad | def grad(self, xs, ys):
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24,248 | ray-project/ray | examples/resnet/cifar_input.py | build_data | def build_data(data_path, size, dataset):
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Args:
data_path: Filename for cifar10 data.
size: The number of images in the dataset.
dataset: The dataset we are using.
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queue: A Tensorflow queue for extr... | python | def build_data(data_path, size, dataset):
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24,249 | ray-project/ray | examples/resnet/cifar_input.py | build_input | def build_input(data, batch_size, dataset, train):
"""Build CIFAR image and labels.
Args:
data_path: Filename for cifar10 data.
batch_size: Input batch size.
train: True if we are training and false if we are testing.
Returns:
images: Batches of images of size
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"""Build CIFAR image and labels.
Args:
data_path: Filename for cifar10 data.
batch_size: Input batch size.
train: True if we are training and false if we are testing.
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24,250 | ray-project/ray | python/ray/scripts/scripts.py | create_or_update | def create_or_update(cluster_config_file, min_workers, max_workers, no_restart,
restart_only, yes, cluster_name):
"""Create or update a Ray cluster."""
if restart_only or no_restart:
assert restart_only != no_restart, "Cannot set both 'restart_only' " \
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"""Create or update a Ray cluster."""
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24,251 | ray-project/ray | python/ray/scripts/scripts.py | teardown | def teardown(cluster_config_file, yes, workers_only, cluster_name):
"""Tear down the Ray cluster."""
teardown_cluster(cluster_config_file, yes, workers_only, cluster_name) | python | def teardown(cluster_config_file, yes, workers_only, cluster_name):
"""Tear down the Ray cluster."""
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24,252 | ray-project/ray | python/ray/scripts/scripts.py | kill_random_node | def kill_random_node(cluster_config_file, yes, cluster_name):
"""Kills a random Ray node. For testing purposes only."""
click.echo("Killed node with IP " +
kill_node(cluster_config_file, yes, cluster_name)) | python | def kill_random_node(cluster_config_file, yes, cluster_name):
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24,253 | ray-project/ray | python/ray/scripts/scripts.py | submit | def submit(cluster_config_file, docker, screen, tmux, stop, start,
cluster_name, port_forward, script, script_args):
"""Uploads and runs a script on the specified cluster.
The script is automatically synced to the following location:
os.path.join("~", os.path.basename(script))
"""
a... | python | def submit(cluster_config_file, docker, screen, tmux, stop, start,
cluster_name, port_forward, script, script_args):
"""Uploads and runs a script on the specified cluster.
The script is automatically synced to the following location:
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24,254 | ray-project/ray | examples/resnet/resnet_model.py | ResNet.build_graph | def build_graph(self):
"""Build a whole graph for the model."""
self.global_step = tf.Variable(0, trainable=False)
self._build_model()
if self.mode == "train":
self._build_train_op()
else:
# Additional initialization for the test network.
self.... | python | def build_graph(self):
"""Build a whole graph for the model."""
self.global_step = tf.Variable(0, trainable=False)
self._build_model()
if self.mode == "train":
self._build_train_op()
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# Additional initialization for the test network.
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24,255 | ray-project/ray | python/ray/rllib/agents/qmix/qmix_policy_graph.py | _mac | def _mac(model, obs, h):
"""Forward pass of the multi-agent controller.
Arguments:
model: TorchModel class
obs: Tensor of shape [B, n_agents, obs_size]
h: List of tensors of shape [B, n_agents, h_size]
Returns:
q_vals: Tensor of shape [B, n_agents, n_actions]
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Arguments:
model: TorchModel class
obs: Tensor of shape [B, n_agents, obs_size]
h: List of tensors of shape [B, n_agents, h_size]
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q_vals: Tensor of shape [B, n_agents, n_actions]
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24,256 | ray-project/ray | python/ray/rllib/agents/qmix/qmix_policy_graph.py | QMixLoss.forward | def forward(self, rewards, actions, terminated, mask, obs, action_mask):
"""Forward pass of the loss.
Arguments:
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]
mask: T... | python | def forward(self, rewards, actions, terminated, mask, obs, action_mask):
"""Forward pass of the loss.
Arguments:
rewards: Tensor of shape [B, T-1, n_agents]
actions: Tensor of shape [B, T-1, n_agents]
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24,257 | 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.
"""
actor_name = _calculate_key(name)
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24,258 | ray-project/ray | python/ray/experimental/named_actors.py | register_actor | def register_actor(name, actor_handle):
"""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
"""
if not isinstance(name, str):
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... | python | def register_actor(name, actor_handle):
"""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
"""
if not isinstance(name, str):
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24,259 | ray-project/ray | python/ray/autoscaler/autoscaler.py | check_extraneous | def check_extraneous(config, schema):
"""Make sure all items of config are in schema"""
if not isinstance(config, dict):
raise ValueError("Config {} is not a dictionary".format(config))
for k in config:
if k not in schema:
raise ValueError("Unexpected config key `{}` not in {}".f... | python | def check_extraneous(config, schema):
"""Make sure all items of config are in schema"""
if not isinstance(config, dict):
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24,260 | ray-project/ray | python/ray/autoscaler/autoscaler.py | validate_config | def validate_config(config, schema=CLUSTER_CONFIG_SCHEMA):
"""Required Dicts indicate that no extra fields can be introduced."""
if not isinstance(config, dict):
raise ValueError("Config {} is not a dictionary".format(config))
check_required(config, schema)
check_extraneous(config, schema) | python | def validate_config(config, schema=CLUSTER_CONFIG_SCHEMA):
"""Required Dicts indicate that no extra fields can be introduced."""
if not isinstance(config, dict):
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24,261 | 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.
"""
for arg in kwargs:
if hasattr(self, arg):
setattr(self, arg, kwargs[arg])
else:... | python | 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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24,262 | 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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24,263 | ray-project/ray | python/ray/actor.py | compute_actor_handle_id | def compute_actor_handle_id(actor_handle_id, num_forks):
"""Deterministically compute an actor handle ID.
A new actor handle ID is generated when it is forked from another actor
handle. The new handle ID is computed as hash(old_handle_id || num_forks).
Args:
actor_handle_id (common.ObjectID): ... | python | def compute_actor_handle_id(actor_handle_id, num_forks):
"""Deterministically compute an actor handle ID.
A new actor handle ID is generated when it is forked from another actor
handle. The new handle ID is computed as hash(old_handle_id || num_forks).
Args:
actor_handle_id (common.ObjectID): ... | [
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24,264 | 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):
"""Deterministically compute an actor handle ID in the non-forked case.
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"""Deterministically compute an actor handle ID in the non-forked case.
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24,265 | 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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24,266 | ray-project/ray | python/ray/actor.py | exit_actor | def exit_actor():
"""Intentionally exit the current actor.
This function is used to disconnect an actor and exit the worker.
Raises:
Exception: An exception is raised if this is a driver or this
worker is not an actor.
"""
worker = ray.worker.global_worker
if worker.mode ==... | python | def exit_actor():
"""Intentionally exit the current actor.
This function is used to disconnect an actor and exit the worker.
Raises:
Exception: An exception is raised if this is a driver or this
worker is not an actor.
"""
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24,267 | ray-project/ray | python/ray/actor.py | get_checkpoints_for_actor | def get_checkpoints_for_actor(actor_id):
"""Get the available checkpoints for the given actor ID, return a list
sorted by checkpoint timestamp in descending order.
"""
checkpoint_info = ray.worker.global_state.actor_checkpoint_info(actor_id)
if checkpoint_info is None:
return []
checkpoi... | python | def get_checkpoints_for_actor(actor_id):
"""Get the available checkpoints for the given actor ID, return a list
sorted by checkpoint timestamp in descending order.
"""
checkpoint_info = ray.worker.global_state.actor_checkpoint_info(actor_id)
if checkpoint_info is None:
return []
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24,268 | ray-project/ray | python/ray/actor.py | ActorHandle._actor_method_call | def _actor_method_call(self,
method_name,
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This is the function that executes when
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24,269 | ray-project/ray | python/ray/rllib/optimizers/multi_gpu_impl.py | LocalSyncParallelOptimizer.optimize | def optimize(self, sess, batch_index):
"""Run a single step of SGD.
Runs a SGD step over a slice of the preloaded batch with size given by
self._loaded_per_device_batch_size and offset given by the batch_index
argument.
Updates shared model weights based on the averaged per-dev... | python | def optimize(self, sess, batch_index):
"""Run a single step of SGD.
Runs a SGD step over a slice of the preloaded batch with size given by
self._loaded_per_device_batch_size and offset given by the batch_index
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24,270 | 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.
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... | [
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24,271 | ray-project/ray | python/ray/tune/automl/genetic_searcher.py | GeneticSearch._crossover | def _crossover(candidate):
"""Perform crossover action to candidates.
For example, new gene = 60% sample_1 + 40% sample_2.
Args:
candidate: List of candidate genes (encodings).
Examples:
>>> # Genes that represent 3 parameters
>>> gene1 = np.array([... | python | def _crossover(candidate):
"""Perform crossover action to candidates.
For example, new gene = 60% sample_1 + 40% sample_2.
Args:
candidate: List of candidate genes (encodings).
Examples:
>>> # Genes that represent 3 parameters
>>> gene1 = np.array([... | [
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24,272 | ray-project/ray | python/ray/tune/automl/genetic_searcher.py | GeneticSearch._mutation | def _mutation(candidate, rate=0.1):
"""Perform mutation action to candidates.
For example, randomly change 10% of original sample
Args:
candidate: List of candidate genes (encodings).
rate: Percentage of mutation bits
Examples:
>>> # Genes that repr... | python | def _mutation(candidate, rate=0.1):
"""Perform mutation action to candidates.
For example, randomly change 10% of original sample
Args:
candidate: List of candidate genes (encodings).
rate: Percentage of mutation bits
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24,273 | 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
remote = trial.runner.train.remote()
# Local Mode
if isinstance(remote, dict):
remote = _LocalWrapper(remote)
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24,274 | ray-project/ray | python/ray/tune/ray_trial_executor.py | RayTrialExecutor._start_trial | def _start_trial(self, trial, checkpoint=None):
"""Starts trial and restores last result if trial was paused.
Raises:
ValueError if restoring from checkpoint fails.
"""
prior_status = trial.status
self.set_status(trial, Trial.RUNNING)
trial.runner = self._set... | python | def _start_trial(self, trial, checkpoint=None):
"""Starts trial and restores last result if trial was paused.
Raises:
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"""
prior_status = trial.status
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24,275 | 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):
"""Stops this trial.
Stops this trial, releasing all allocating resources. If stopping the
trial fails, the run will be marked as terminated in error, but no
exception will be thrown.
... | python | def _stop_trial(self, trial, error=False, error_msg=None,
stop_logger=True):
"""Stops this trial.
Stops this trial, releasing all allocating resources. If stopping the
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24,276 | ray-project/ray | python/ray/tune/ray_trial_executor.py | RayTrialExecutor.start_trial | def start_trial(self, trial, checkpoint=None):
"""Starts the trial.
Will not return resources if trial repeatedly fails on start.
Args:
trial (Trial): Trial to be started.
checkpoint (Checkpoint): A Python object or path storing the state
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... | python | def start_trial(self, trial, checkpoint=None):
"""Starts the trial.
Will not return resources if trial repeatedly fails on start.
Args:
trial (Trial): Trial to be started.
checkpoint (Checkpoint): A Python object or path storing the state
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24,277 | 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):
"""Only returns resources if resources allocated."""
prior_status = trial.status
self._stop_trial(
trial, error=error, error_msg=error_msg, stop_logger=stop_logger)
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... | python | def stop_trial(self, trial, error=False, error_msg=None, stop_logger=True):
"""Only returns resources if resources allocated."""
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24,278 | 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:
raise ValueError("Trial was not running.")
self... | python | 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:
raise ValueError("Trial was not running.")
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24,279 | ray-project/ray | python/ray/tune/ray_trial_executor.py | RayTrialExecutor.has_resources | def has_resources(self, resources):
"""Returns whether this runner has at least the specified resources.
This refreshes the Ray cluster resources if the time since last update
has exceeded self._refresh_period. This also assumes that the
cluster is not resizing very frequently.
... | python | def has_resources(self, resources):
"""Returns whether this runner has at least the specified resources.
This refreshes the Ray cluster resources if the time since last update
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24,280 | 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."""
if self._resources_initialized:
res_str = "{} CPUs, {} GPUs".format(self._avail_resources.cpu,
self._avail_resources.gpu)
if self._avail_re... | python | def resource_string(self):
"""Returns a string describing the total resources available."""
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24,281 | 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."""
trial._checkpoint.storage = storage
trial._checkpoint.last_result = trial.last_result
if storage == Checkpoint.MEMORY:
trial._checkpoint.value = trial.runner.save_to_object.remote()... | [
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24,282 | 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:
return ray.get(
... | 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:
return ray.get(
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24,283 | 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
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instance_id (UUID): The id of the instance the actor will execute.
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24,284 | 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):
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operator (Operator): The logical operator metadata.
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24,285 | 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
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24,286 | 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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24,287 | 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,
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"""Applies a map operator to the stream.
Attributes:
map_fn (function): The user-defined logic of the map.
"""
op = Operator(
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24,288 | ray-project/ray | python/ray/experimental/streaming/streaming.py | DataStream.flat_map | def flat_map(self, flatmap_fn):
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Attributes:
flatmap_fn (function): The user-defined logic of the flatmap
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"""
op = Operator(
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24,289 | ray-project/ray | python/ray/experimental/streaming/streaming.py | DataStream.key_by | def key_by(self, key_selector):
"""Applies a key_by operator to the stream.
Attributes:
key_attribute_index (int): The index of the key attributed
(assuming tuple records).
"""
op = Operator(
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"""Applies a key_by operator to the stream.
Attributes:
key_attribute_index (int): The index of the key attributed
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"""
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24,290 | ray-project/ray | python/ray/experimental/streaming/streaming.py | DataStream.time_window | def time_window(self, window_width_ms):
"""Applies a system time window to the stream.
Attributes:
window_width_ms (int): The length of the window in ms.
"""
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"""Applies a system time window to the stream.
Attributes:
window_width_ms (int): The length of the window in ms.
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24,291 | ray-project/ray | python/ray/experimental/streaming/streaming.py | DataStream.filter | def filter(self, filter_fn):
"""Applies a filter to the stream.
Attributes:
filter_fn (function): The user-defined filter function.
"""
op = Operator(
_generate_uuid(),
OpType.Filter,
"Filter",
filter_fn,
num_insta... | python | def filter(self, filter_fn):
"""Applies a filter to the stream.
Attributes:
filter_fn (function): The user-defined filter function.
"""
op = Operator(
_generate_uuid(),
OpType.Filter,
"Filter",
filter_fn,
num_insta... | [
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] | 4eade036a0505e244c976f36aaa2d64386b5129b | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/experimental/streaming/streaming.py#L620-L632 |
24,292 | ray-project/ray | python/ray/experimental/streaming/streaming.py | DataStream.inspect | def inspect(self, inspect_logic):
"""Inspects the content of the stream.
Attributes:
inspect_logic (function): The user-defined inspect function.
"""
op = Operator(
_generate_uuid(),
OpType.Inspect,
"Inspect",
inspect_logic,
... | python | def inspect(self, inspect_logic):
"""Inspects the content of the stream.
Attributes:
inspect_logic (function): The user-defined inspect function.
"""
op = Operator(
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OpType.Inspect,
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inspect_logic,
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] | 4eade036a0505e244c976f36aaa2d64386b5129b | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/experimental/streaming/streaming.py#L644-L656 |
24,293 | ray-project/ray | python/ray/experimental/streaming/streaming.py | DataStream.sink | def sink(self):
"""Closes the stream with a sink operator."""
op = Operator(
_generate_uuid(),
OpType.Sink,
"Sink",
num_instances=self.env.config.parallelism)
return self.__register(op) | python | def sink(self):
"""Closes the stream with a sink operator."""
op = Operator(
_generate_uuid(),
OpType.Sink,
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num_instances=self.env.config.parallelism)
return self.__register(op) | [
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24,294 | ray-project/ray | python/ray/log_monitor.py | LogMonitor.update_log_filenames | def update_log_filenames(self):
"""Update the list of log files to monitor."""
log_filenames = os.listdir(self.logs_dir)
for log_filename in log_filenames:
full_path = os.path.join(self.logs_dir, log_filename)
if full_path not in self.log_filenames:
self.... | python | def update_log_filenames(self):
"""Update the list of log files to monitor."""
log_filenames = os.listdir(self.logs_dir)
for log_filename in log_filenames:
full_path = os.path.join(self.logs_dir, log_filename)
if full_path not in self.log_filenames:
self.... | [
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] | 4eade036a0505e244c976f36aaa2d64386b5129b | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/log_monitor.py#L90-L104 |
24,295 | ray-project/ray | python/ray/log_monitor.py | LogMonitor.open_closed_files | def open_closed_files(self):
"""Open some closed files if they may have new lines.
Opening more files may require us to close some of the already open
files.
"""
if not self.can_open_more_files:
# If we can't open any more files. Close all of the files.
s... | python | def open_closed_files(self):
"""Open some closed files if they may have new lines.
Opening more files may require us to close some of the already open
files.
"""
if not self.can_open_more_files:
# If we can't open any more files. Close all of the files.
s... | [
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] | 4eade036a0505e244c976f36aaa2d64386b5129b | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/log_monitor.py#L106-L160 |
24,296 | ray-project/ray | python/ray/log_monitor.py | LogMonitor.run | def run(self):
"""Run the log monitor.
This will query Redis once every second to check if there are new log
files to monitor. It will also store those log files in Redis.
"""
while True:
self.update_log_filenames()
self.open_closed_files()
an... | python | def run(self):
"""Run the log monitor.
This will query Redis once every second to check if there are new log
files to monitor. It will also store those log files in Redis.
"""
while True:
self.update_log_filenames()
self.open_closed_files()
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] | 4eade036a0505e244c976f36aaa2d64386b5129b | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/log_monitor.py#L210-L223 |
24,297 | ray-project/ray | python/ray/tune/suggest/suggestion.py | SuggestionAlgorithm.add_configurations | def add_configurations(self, experiments):
"""Chains generator given experiment specifications.
Arguments:
experiments (Experiment | list | dict): Experiments to run.
"""
experiment_list = convert_to_experiment_list(experiments)
for experiment in experiment_list:
... | python | def add_configurations(self, experiments):
"""Chains generator given experiment specifications.
Arguments:
experiments (Experiment | list | dict): Experiments to run.
"""
experiment_list = convert_to_experiment_list(experiments)
for experiment in experiment_list:
... | [
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] | 4eade036a0505e244c976f36aaa2d64386b5129b | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/tune/suggest/suggestion.py#L43-L53 |
24,298 | ray-project/ray | python/ray/tune/suggest/suggestion.py | SuggestionAlgorithm.next_trials | def next_trials(self):
"""Provides a batch of Trial objects to be queued into the TrialRunner.
A batch ends when self._trial_generator returns None.
Returns:
trials (list): Returns a list of trials.
"""
trials = []
for trial in self._trial_generator:
... | python | def next_trials(self):
"""Provides a batch of Trial objects to be queued into the TrialRunner.
A batch ends when self._trial_generator returns None.
Returns:
trials (list): Returns a list of trials.
"""
trials = []
for trial in self._trial_generator:
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] | 4eade036a0505e244c976f36aaa2d64386b5129b | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/tune/suggest/suggestion.py#L55-L71 |
24,299 | ray-project/ray | python/ray/tune/suggest/suggestion.py | SuggestionAlgorithm._generate_trials | def _generate_trials(self, experiment_spec, output_path=""):
"""Generates trials with configurations from `_suggest`.
Creates a trial_id that is passed into `_suggest`.
Yields:
Trial objects constructed according to `spec`
"""
if "run" not in experiment_spec:
... | python | def _generate_trials(self, experiment_spec, output_path=""):
"""Generates trials with configurations from `_suggest`.
Creates a trial_id that is passed into `_suggest`.
Yields:
Trial objects constructed according to `spec`
"""
if "run" not in experiment_spec:
... | [
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] | 4eade036a0505e244c976f36aaa2d64386b5129b | https://github.com/ray-project/ray/blob/4eade036a0505e244c976f36aaa2d64386b5129b/python/ray/tune/suggest/suggestion.py#L73-L102 |
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