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22,900 | Microsoft/LightGBM | python-package/lightgbm/basic.py | Dataset.get_data | def get_data(self):
"""Get the raw data of the Dataset.
Returns
-------
data : string, numpy array, pandas DataFrame, H2O DataTable's Frame, scipy.sparse, list of numpy arrays or None
Raw data used in the Dataset construction.
"""
if self.handle is None:
... | python | def get_data(self):
"""Get the raw data of the Dataset.
Returns
-------
data : string, numpy array, pandas DataFrame, H2O DataTable's Frame, scipy.sparse, list of numpy arrays or None
Raw data used in the Dataset construction.
"""
if self.handle is None:
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22,901 | Microsoft/LightGBM | python-package/lightgbm/basic.py | Dataset.get_group | def get_group(self):
"""Get the group of the Dataset.
Returns
-------
group : numpy array or None
Group size of each group.
"""
if self.group is None:
self.group = self.get_field('group')
if self.group is not None:
# gr... | python | def get_group(self):
"""Get the group of the Dataset.
Returns
-------
group : numpy array or None
Group size of each group.
"""
if self.group is None:
self.group = self.get_field('group')
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22,902 | Microsoft/LightGBM | python-package/lightgbm/basic.py | Dataset.num_data | def num_data(self):
"""Get the number of rows in the Dataset.
Returns
-------
number_of_rows : int
The number of rows in the Dataset.
"""
if self.handle is not None:
ret = ctypes.c_int()
_safe_call(_LIB.LGBM_DatasetGetNumData(self.hand... | python | def num_data(self):
"""Get the number of rows in the Dataset.
Returns
-------
number_of_rows : int
The number of rows in the Dataset.
"""
if self.handle is not None:
ret = ctypes.c_int()
_safe_call(_LIB.LGBM_DatasetGetNumData(self.hand... | [
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22,903 | Microsoft/LightGBM | python-package/lightgbm/basic.py | Dataset.get_ref_chain | def get_ref_chain(self, ref_limit=100):
"""Get a chain of Dataset objects.
Starts with r, then goes to r.reference (if exists),
then to r.reference.reference, etc.
until we hit ``ref_limit`` or a reference loop.
Parameters
----------
ref_limit : int, optional (d... | python | def get_ref_chain(self, ref_limit=100):
"""Get a chain of Dataset objects.
Starts with r, then goes to r.reference (if exists),
then to r.reference.reference, etc.
until we hit ``ref_limit`` or a reference loop.
Parameters
----------
ref_limit : int, optional (d... | [
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22,904 | Microsoft/LightGBM | python-package/lightgbm/basic.py | Dataset.add_features_from | def add_features_from(self, other):
"""Add features from other Dataset to the current Dataset.
Both Datasets must be constructed before calling this method.
Parameters
----------
other : Dataset
The Dataset to take features from.
Returns
-------
... | python | def add_features_from(self, other):
"""Add features from other Dataset to the current Dataset.
Both Datasets must be constructed before calling this method.
Parameters
----------
other : Dataset
The Dataset to take features from.
Returns
-------
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22,905 | Microsoft/LightGBM | python-package/lightgbm/basic.py | Dataset.dump_text | def dump_text(self, filename):
"""Save Dataset to a text file.
This format cannot be loaded back in by LightGBM, but is useful for debugging purposes.
Parameters
----------
filename : string
Name of the output file.
Returns
-------
self : Da... | python | def dump_text(self, filename):
"""Save Dataset to a text file.
This format cannot be loaded back in by LightGBM, but is useful for debugging purposes.
Parameters
----------
filename : string
Name of the output file.
Returns
-------
self : Da... | [
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22,906 | Microsoft/LightGBM | python-package/lightgbm/basic.py | Booster.free_dataset | def free_dataset(self):
"""Free Booster's Datasets.
Returns
-------
self : Booster
Booster without Datasets.
"""
self.__dict__.pop('train_set', None)
self.__dict__.pop('valid_sets', None)
self.__num_dataset = 0
return self | python | def free_dataset(self):
"""Free Booster's Datasets.
Returns
-------
self : Booster
Booster without Datasets.
"""
self.__dict__.pop('train_set', None)
self.__dict__.pop('valid_sets', None)
self.__num_dataset = 0
return self | [
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22,907 | Microsoft/LightGBM | python-package/lightgbm/basic.py | Booster.set_network | def set_network(self, machines, local_listen_port=12400,
listen_time_out=120, num_machines=1):
"""Set the network configuration.
Parameters
----------
machines : list, set or string
Names of machines.
local_listen_port : int, optional (default=124... | python | def set_network(self, machines, local_listen_port=12400,
listen_time_out=120, num_machines=1):
"""Set the network configuration.
Parameters
----------
machines : list, set or string
Names of machines.
local_listen_port : int, optional (default=124... | [
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22,908 | Microsoft/LightGBM | python-package/lightgbm/basic.py | Booster.add_valid | def add_valid(self, data, name):
"""Add validation data.
Parameters
----------
data : Dataset
Validation data.
name : string
Name of validation data.
Returns
-------
self : Booster
Booster with set validation data.
... | python | def add_valid(self, data, name):
"""Add validation data.
Parameters
----------
data : Dataset
Validation data.
name : string
Name of validation data.
Returns
-------
self : Booster
Booster with set validation data.
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22,909 | Microsoft/LightGBM | python-package/lightgbm/basic.py | Booster.reset_parameter | def reset_parameter(self, params):
"""Reset parameters of Booster.
Parameters
----------
params : dict
New parameters for Booster.
Returns
-------
self : Booster
Booster with new parameters.
"""
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"""Reset parameters of Booster.
Parameters
----------
params : dict
New parameters for Booster.
Returns
-------
self : Booster
Booster with new parameters.
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22,910 | Microsoft/LightGBM | python-package/lightgbm/basic.py | Booster.update | def update(self, train_set=None, fobj=None):
"""Update Booster for one iteration.
Parameters
----------
train_set : Dataset or None, optional (default=None)
Training data.
If None, last training data is used.
fobj : callable or None, optional (default=Non... | python | def update(self, train_set=None, fobj=None):
"""Update Booster for one iteration.
Parameters
----------
train_set : Dataset or None, optional (default=None)
Training data.
If None, last training data is used.
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22,911 | Microsoft/LightGBM | python-package/lightgbm/basic.py | Booster.__boost | def __boost(self, grad, hess):
"""Boost Booster for one iteration with customized gradient statistics.
Note
----
For multi-class task, the score is group by class_id first, then group by row_id.
If you want to get i-th row score in j-th class, the access way is score[j * num_dat... | python | def __boost(self, grad, hess):
"""Boost Booster for one iteration with customized gradient statistics.
Note
----
For multi-class task, the score is group by class_id first, then group by row_id.
If you want to get i-th row score in j-th class, the access way is score[j * num_dat... | [
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22,912 | Microsoft/LightGBM | python-package/lightgbm/basic.py | Booster.rollback_one_iter | def rollback_one_iter(self):
"""Rollback one iteration.
Returns
-------
self : Booster
Booster with rolled back one iteration.
"""
_safe_call(_LIB.LGBM_BoosterRollbackOneIter(
self.handle))
self.__is_predicted_cur_iter = [False for _ in ra... | python | def rollback_one_iter(self):
"""Rollback one iteration.
Returns
-------
self : Booster
Booster with rolled back one iteration.
"""
_safe_call(_LIB.LGBM_BoosterRollbackOneIter(
self.handle))
self.__is_predicted_cur_iter = [False for _ in ra... | [
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22,913 | Microsoft/LightGBM | python-package/lightgbm/basic.py | Booster.current_iteration | def current_iteration(self):
"""Get the index of the current iteration.
Returns
-------
cur_iter : int
The index of the current iteration.
"""
out_cur_iter = ctypes.c_int(0)
_safe_call(_LIB.LGBM_BoosterGetCurrentIteration(
self.handle,
... | python | def current_iteration(self):
"""Get the index of the current iteration.
Returns
-------
cur_iter : int
The index of the current iteration.
"""
out_cur_iter = ctypes.c_int(0)
_safe_call(_LIB.LGBM_BoosterGetCurrentIteration(
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22,914 | Microsoft/LightGBM | python-package/lightgbm/basic.py | Booster.num_model_per_iteration | def num_model_per_iteration(self):
"""Get number of models per iteration.
Returns
-------
model_per_iter : int
The number of models per iteration.
"""
model_per_iter = ctypes.c_int(0)
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self.... | python | def num_model_per_iteration(self):
"""Get number of models per iteration.
Returns
-------
model_per_iter : int
The number of models per iteration.
"""
model_per_iter = ctypes.c_int(0)
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22,915 | Microsoft/LightGBM | python-package/lightgbm/basic.py | Booster.num_trees | def num_trees(self):
"""Get number of weak sub-models.
Returns
-------
num_trees : int
The number of weak sub-models.
"""
num_trees = ctypes.c_int(0)
_safe_call(_LIB.LGBM_BoosterNumberOfTotalModel(
self.handle,
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"""Get number of weak sub-models.
Returns
-------
num_trees : int
The number of weak sub-models.
"""
num_trees = ctypes.c_int(0)
_safe_call(_LIB.LGBM_BoosterNumberOfTotalModel(
self.handle,
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22,916 | Microsoft/LightGBM | python-package/lightgbm/basic.py | Booster.eval | def eval(self, data, name, feval=None):
"""Evaluate for data.
Parameters
----------
data : Dataset
Data for the evaluating.
name : string
Name of the data.
feval : callable or None, optional (default=None)
Customized evaluation functio... | python | def eval(self, data, name, feval=None):
"""Evaluate for data.
Parameters
----------
data : Dataset
Data for the evaluating.
name : string
Name of the data.
feval : callable or None, optional (default=None)
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22,917 | Microsoft/LightGBM | python-package/lightgbm/basic.py | Booster.eval_valid | def eval_valid(self, feval=None):
"""Evaluate for validation data.
Parameters
----------
feval : callable or None, optional (default=None)
Customized evaluation function.
Should accept two parameters: preds, train_data,
and return (eval_name, eval_res... | python | def eval_valid(self, feval=None):
"""Evaluate for validation data.
Parameters
----------
feval : callable or None, optional (default=None)
Customized evaluation function.
Should accept two parameters: preds, train_data,
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22,918 | Microsoft/LightGBM | python-package/lightgbm/basic.py | Booster.save_model | def save_model(self, filename, num_iteration=None, start_iteration=0):
"""Save Booster to file.
Parameters
----------
filename : string
Filename to save Booster.
num_iteration : int or None, optional (default=None)
Index of the iteration that should be sa... | python | def save_model(self, filename, num_iteration=None, start_iteration=0):
"""Save Booster to file.
Parameters
----------
filename : string
Filename to save Booster.
num_iteration : int or None, optional (default=None)
Index of the iteration that should be sa... | [
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22,919 | Microsoft/LightGBM | python-package/lightgbm/basic.py | Booster.shuffle_models | def shuffle_models(self, start_iteration=0, end_iteration=-1):
"""Shuffle models.
Parameters
----------
start_iteration : int, optional (default=0)
The first iteration that will be shuffled.
end_iteration : int, optional (default=-1)
The last iteration th... | python | def shuffle_models(self, start_iteration=0, end_iteration=-1):
"""Shuffle models.
Parameters
----------
start_iteration : int, optional (default=0)
The first iteration that will be shuffled.
end_iteration : int, optional (default=-1)
The last iteration th... | [
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The first iteration that will be shuffled.
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22,920 | Microsoft/LightGBM | python-package/lightgbm/basic.py | Booster.model_from_string | def model_from_string(self, model_str, verbose=True):
"""Load Booster from a string.
Parameters
----------
model_str : string
Model will be loaded from this string.
verbose : bool, optional (default=True)
Whether to print messages while loading model.
... | python | def model_from_string(self, model_str, verbose=True):
"""Load Booster from a string.
Parameters
----------
model_str : string
Model will be loaded from this string.
verbose : bool, optional (default=True)
Whether to print messages while loading model.
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22,921 | Microsoft/LightGBM | python-package/lightgbm/basic.py | Booster.model_to_string | def model_to_string(self, num_iteration=None, start_iteration=0):
"""Save Booster to string.
Parameters
----------
num_iteration : int or None, optional (default=None)
Index of the iteration that should be saved.
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"""Save Booster to string.
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----------
num_iteration : int or None, optional (default=None)
Index of the iteration that should be saved.
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22,922 | Microsoft/LightGBM | python-package/lightgbm/basic.py | Booster.dump_model | def dump_model(self, num_iteration=None, start_iteration=0):
"""Dump Booster to JSON format.
Parameters
----------
num_iteration : int or None, optional (default=None)
Index of the iteration that should be dumped.
If None, if the best iteration exists, it is dump... | python | def dump_model(self, num_iteration=None, start_iteration=0):
"""Dump Booster to JSON format.
Parameters
----------
num_iteration : int or None, optional (default=None)
Index of the iteration that should be dumped.
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22,923 | Microsoft/LightGBM | python-package/lightgbm/basic.py | Booster.predict | def predict(self, data, num_iteration=None,
raw_score=False, pred_leaf=False, pred_contrib=False,
data_has_header=False, is_reshape=True, **kwargs):
"""Make a prediction.
Parameters
----------
data : string, numpy array, pandas DataFrame, H2O DataTable's ... | python | def predict(self, data, num_iteration=None,
raw_score=False, pred_leaf=False, pred_contrib=False,
data_has_header=False, is_reshape=True, **kwargs):
"""Make a prediction.
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22,924 | Microsoft/LightGBM | python-package/lightgbm/basic.py | Booster.refit | def refit(self, data, label, decay_rate=0.9, **kwargs):
"""Refit the existing Booster by new data.
Parameters
----------
data : string, numpy array, pandas DataFrame, H2O DataTable's Frame or scipy.sparse
Data source for refit.
If string, it represents the path t... | python | def refit(self, data, label, decay_rate=0.9, **kwargs):
"""Refit the existing Booster by new data.
Parameters
----------
data : string, numpy array, pandas DataFrame, H2O DataTable's Frame or scipy.sparse
Data source for refit.
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22,925 | Microsoft/LightGBM | python-package/lightgbm/basic.py | Booster.get_leaf_output | def get_leaf_output(self, tree_id, leaf_id):
"""Get the output of a leaf.
Parameters
----------
tree_id : int
The index of the tree.
leaf_id : int
The index of the leaf in the tree.
Returns
-------
result : float
The o... | python | def get_leaf_output(self, tree_id, leaf_id):
"""Get the output of a leaf.
Parameters
----------
tree_id : int
The index of the tree.
leaf_id : int
The index of the leaf in the tree.
Returns
-------
result : float
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22,926 | Microsoft/LightGBM | python-package/lightgbm/basic.py | Booster._to_predictor | def _to_predictor(self, pred_parameter=None):
"""Convert to predictor."""
predictor = _InnerPredictor(booster_handle=self.handle, pred_parameter=pred_parameter)
predictor.pandas_categorical = self.pandas_categorical
return predictor | python | def _to_predictor(self, pred_parameter=None):
"""Convert to predictor."""
predictor = _InnerPredictor(booster_handle=self.handle, pred_parameter=pred_parameter)
predictor.pandas_categorical = self.pandas_categorical
return predictor | [
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22,927 | Microsoft/LightGBM | python-package/lightgbm/basic.py | Booster.num_feature | def num_feature(self):
"""Get number of features.
Returns
-------
num_feature : int
The number of features.
"""
out_num_feature = ctypes.c_int(0)
_safe_call(_LIB.LGBM_BoosterGetNumFeature(
self.handle,
ctypes.byref(out_num_feat... | python | def num_feature(self):
"""Get number of features.
Returns
-------
num_feature : int
The number of features.
"""
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22,928 | Microsoft/LightGBM | python-package/lightgbm/basic.py | Booster.feature_name | def feature_name(self):
"""Get names of features.
Returns
-------
result : list
List with names of features.
"""
num_feature = self.num_feature()
# Get name of features
tmp_out_len = ctypes.c_int(0)
string_buffers = [ctypes.create_stri... | python | def feature_name(self):
"""Get names of features.
Returns
-------
result : list
List with names of features.
"""
num_feature = self.num_feature()
# Get name of features
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22,929 | Microsoft/LightGBM | python-package/lightgbm/basic.py | Booster.get_split_value_histogram | def get_split_value_histogram(self, feature, bins=None, xgboost_style=False):
"""Get split value histogram for the specified feature.
Parameters
----------
feature : int or string
The feature name or index the histogram is calculated for.
If int, interpreted as i... | python | def get_split_value_histogram(self, feature, bins=None, xgboost_style=False):
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----------
feature : int or string
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22,930 | Microsoft/LightGBM | python-package/lightgbm/basic.py | Booster.__inner_eval | def __inner_eval(self, data_name, data_idx, feval=None):
"""Evaluate training or validation data."""
if data_idx >= self.__num_dataset:
raise ValueError("Data_idx should be smaller than number of dataset")
self.__get_eval_info()
ret = []
if self.__num_inner_eval > 0:
... | python | def __inner_eval(self, data_name, data_idx, feval=None):
"""Evaluate training or validation data."""
if data_idx >= self.__num_dataset:
raise ValueError("Data_idx should be smaller than number of dataset")
self.__get_eval_info()
ret = []
if self.__num_inner_eval > 0:
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22,931 | Microsoft/LightGBM | python-package/lightgbm/basic.py | Booster.__inner_predict | def __inner_predict(self, data_idx):
"""Predict for training and validation dataset."""
if data_idx >= self.__num_dataset:
raise ValueError("Data_idx should be smaller than number of dataset")
if self.__inner_predict_buffer[data_idx] is None:
if data_idx == 0:
... | python | def __inner_predict(self, data_idx):
"""Predict for training and validation dataset."""
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raise ValueError("Data_idx should be smaller than number of dataset")
if self.__inner_predict_buffer[data_idx] is None:
if data_idx == 0:
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22,932 | Microsoft/LightGBM | python-package/lightgbm/basic.py | Booster.__get_eval_info | def __get_eval_info(self):
"""Get inner evaluation count and names."""
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self.__need_reload_eval_info = False
out_num_eval = ctypes.c_int(0)
# Get num of inner evals
_safe_call(_LIB.LGBM_BoosterGetEvalCounts(
... | python | def __get_eval_info(self):
"""Get inner evaluation count and names."""
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self.__need_reload_eval_info = False
out_num_eval = ctypes.c_int(0)
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22,933 | Microsoft/LightGBM | python-package/lightgbm/basic.py | Booster.set_attr | def set_attr(self, **kwargs):
"""Set attributes to the Booster.
Parameters
----------
**kwargs
The attributes to set.
Setting a value to None deletes an attribute.
Returns
-------
self : Booster
Booster with set attributes.
... | python | def set_attr(self, **kwargs):
"""Set attributes to the Booster.
Parameters
----------
**kwargs
The attributes to set.
Setting a value to None deletes an attribute.
Returns
-------
self : Booster
Booster with set attributes.
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22,934 | Microsoft/LightGBM | python-package/lightgbm/libpath.py | find_lib_path | def find_lib_path():
"""Find the path to LightGBM library files.
Returns
-------
lib_path: list of strings
List of all found library paths to LightGBM.
"""
if os.environ.get('LIGHTGBM_BUILD_DOC', False):
# we don't need lib_lightgbm while building docs
return []
curr... | python | def find_lib_path():
"""Find the path to LightGBM library files.
Returns
-------
lib_path: list of strings
List of all found library paths to LightGBM.
"""
if os.environ.get('LIGHTGBM_BUILD_DOC', False):
# we don't need lib_lightgbm while building docs
return []
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22,935 | Microsoft/LightGBM | python-package/lightgbm/compat.py | json_default_with_numpy | def json_default_with_numpy(obj):
"""Convert numpy classes to JSON serializable objects."""
if isinstance(obj, (np.integer, np.floating, np.bool_)):
return obj.item()
elif isinstance(obj, np.ndarray):
return obj.tolist()
else:
return obj | python | def json_default_with_numpy(obj):
"""Convert numpy classes to JSON serializable objects."""
if isinstance(obj, (np.integer, np.floating, np.bool_)):
return obj.item()
elif isinstance(obj, np.ndarray):
return obj.tolist()
else:
return obj | [
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22,936 | Microsoft/LightGBM | python-package/lightgbm/callback.py | _format_eval_result | def _format_eval_result(value, show_stdv=True):
"""Format metric string."""
if len(value) == 4:
return '%s\'s %s: %g' % (value[0], value[1], value[2])
elif len(value) == 5:
if show_stdv:
return '%s\'s %s: %g + %g' % (value[0], value[1], value[2], value[4])
else:
... | python | def _format_eval_result(value, show_stdv=True):
"""Format metric string."""
if len(value) == 4:
return '%s\'s %s: %g' % (value[0], value[1], value[2])
elif len(value) == 5:
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return '%s\'s %s: %g + %g' % (value[0], value[1], value[2], value[4])
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22,937 | Microsoft/LightGBM | python-package/lightgbm/callback.py | print_evaluation | def print_evaluation(period=1, show_stdv=True):
"""Create a callback that prints the evaluation results.
Parameters
----------
period : int, optional (default=1)
The period to print the evaluation results.
show_stdv : bool, optional (default=True)
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"""Create a callback that prints the evaluation results.
Parameters
----------
period : int, optional (default=1)
The period to print the evaluation results.
show_stdv : bool, optional (default=True)
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22,938 | Microsoft/LightGBM | python-package/lightgbm/callback.py | record_evaluation | def record_evaluation(eval_result):
"""Create a callback that records the evaluation history into ``eval_result``.
Parameters
----------
eval_result : dict
A dictionary to store the evaluation results.
Returns
-------
callback : function
The callback that records the evaluat... | python | def record_evaluation(eval_result):
"""Create a callback that records the evaluation history into ``eval_result``.
Parameters
----------
eval_result : dict
A dictionary to store the evaluation results.
Returns
-------
callback : function
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22,939 | Microsoft/LightGBM | python-package/lightgbm/callback.py | reset_parameter | def reset_parameter(**kwargs):
"""Create a callback that resets the parameter after the first iteration.
Note
----
The initial parameter will still take in-effect on first iteration.
Parameters
----------
**kwargs : value should be list or function
List of parameters for each boost... | python | def reset_parameter(**kwargs):
"""Create a callback that resets the parameter after the first iteration.
Note
----
The initial parameter will still take in-effect on first iteration.
Parameters
----------
**kwargs : value should be list or function
List of parameters for each boost... | [
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22,940 | Microsoft/LightGBM | python-package/lightgbm/callback.py | early_stopping | def early_stopping(stopping_rounds, first_metric_only=False, verbose=True):
"""Create a callback that activates early stopping.
Note
----
Activates early stopping.
The model will train until the validation score stops improving.
Validation score needs to improve at least every ``early_stopping_... | python | def early_stopping(stopping_rounds, first_metric_only=False, verbose=True):
"""Create a callback that activates early stopping.
Note
----
Activates early stopping.
The model will train until the validation score stops improving.
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22,941 | Microsoft/LightGBM | examples/python-guide/logistic_regression.py | log_loss | def log_loss(preds, labels):
"""Logarithmic loss with non-necessarily-binary labels."""
log_likelihood = np.sum(labels * np.log(preds)) / len(preds)
return -log_likelihood | python | def log_loss(preds, labels):
"""Logarithmic loss with non-necessarily-binary labels."""
log_likelihood = np.sum(labels * np.log(preds)) / len(preds)
return -log_likelihood | [
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22,942 | Microsoft/LightGBM | examples/python-guide/logistic_regression.py | experiment | def experiment(objective, label_type, data):
"""Measure performance of an objective.
Parameters
----------
objective : string 'binary' or 'xentropy'
Objective function.
label_type : string 'binary' or 'probability'
Type of the label.
data : dict
Data for training.
R... | python | def experiment(objective, label_type, data):
"""Measure performance of an objective.
Parameters
----------
objective : string 'binary' or 'xentropy'
Objective function.
label_type : string 'binary' or 'probability'
Type of the label.
data : dict
Data for training.
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22,943 | Microsoft/LightGBM | python-package/lightgbm/plotting.py | _check_not_tuple_of_2_elements | def _check_not_tuple_of_2_elements(obj, obj_name='obj'):
"""Check object is not tuple or does not have 2 elements."""
if not isinstance(obj, tuple) or len(obj) != 2:
raise TypeError('%s must be a tuple of 2 elements.' % obj_name) | python | def _check_not_tuple_of_2_elements(obj, obj_name='obj'):
"""Check object is not tuple or does not have 2 elements."""
if not isinstance(obj, tuple) or len(obj) != 2:
raise TypeError('%s must be a tuple of 2 elements.' % obj_name) | [
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22,944 | Microsoft/LightGBM | python-package/lightgbm/plotting.py | plot_importance | def plot_importance(booster, ax=None, height=0.2,
xlim=None, ylim=None, title='Feature importance',
xlabel='Feature importance', ylabel='Features',
importance_type='split', max_num_features=None,
ignore_zero=True, figsize=None, grid=True,
... | python | def plot_importance(booster, ax=None, height=0.2,
xlim=None, ylim=None, title='Feature importance',
xlabel='Feature importance', ylabel='Features',
importance_type='split', max_num_features=None,
ignore_zero=True, figsize=None, grid=True,
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22,945 | Microsoft/LightGBM | python-package/lightgbm/plotting.py | plot_metric | def plot_metric(booster, metric=None, dataset_names=None,
ax=None, xlim=None, ylim=None,
title='Metric during training',
xlabel='Iterations', ylabel='auto',
figsize=None, grid=True):
"""Plot one metric during training.
Parameters
----------
... | python | def plot_metric(booster, metric=None, dataset_names=None,
ax=None, xlim=None, ylim=None,
title='Metric during training',
xlabel='Iterations', ylabel='auto',
figsize=None, grid=True):
"""Plot one metric during training.
Parameters
----------
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22,946 | Microsoft/LightGBM | python-package/lightgbm/plotting.py | _to_graphviz | def _to_graphviz(tree_info, show_info, feature_names, precision=None, **kwargs):
"""Convert specified tree to graphviz instance.
See:
- https://graphviz.readthedocs.io/en/stable/api.html#digraph
"""
if GRAPHVIZ_INSTALLED:
from graphviz import Digraph
else:
raise ImportError('Y... | python | def _to_graphviz(tree_info, show_info, feature_names, precision=None, **kwargs):
"""Convert specified tree to graphviz instance.
See:
- https://graphviz.readthedocs.io/en/stable/api.html#digraph
"""
if GRAPHVIZ_INSTALLED:
from graphviz import Digraph
else:
raise ImportError('Y... | [
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22,947 | Microsoft/LightGBM | python-package/lightgbm/plotting.py | create_tree_digraph | def create_tree_digraph(booster, tree_index=0, show_info=None, precision=None,
old_name=None, old_comment=None, old_filename=None, old_directory=None,
old_format=None, old_engine=None, old_encoding=None, old_graph_attr=None,
old_node_attr=None, old... | python | def create_tree_digraph(booster, tree_index=0, show_info=None, precision=None,
old_name=None, old_comment=None, old_filename=None, old_directory=None,
old_format=None, old_engine=None, old_encoding=None, old_graph_attr=None,
old_node_attr=None, old... | [
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Note
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22,948 | facebookresearch/fastText | python/fastText/FastText.py | train_supervised | def train_supervised(
input,
lr=0.1,
dim=100,
ws=5,
epoch=5,
minCount=1,
minCountLabel=0,
minn=0,
maxn=0,
neg=5,
wordNgrams=1,
loss="softmax",
bucket=2000000,
thread=multiprocessing.cpu_count() - 1,
lrUpdateRate=100,
t=1e-4,
label="__label__",
verb... | python | def train_supervised(
input,
lr=0.1,
dim=100,
ws=5,
epoch=5,
minCount=1,
minCountLabel=0,
minn=0,
maxn=0,
neg=5,
wordNgrams=1,
loss="softmax",
bucket=2000000,
thread=multiprocessing.cpu_count() - 1,
lrUpdateRate=100,
t=1e-4,
label="__label__",
verb... | [
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22,949 | facebookresearch/fastText | python/fastText/FastText.py | _FastText.get_word_vector | def get_word_vector(self, word):
"""Get the vector representation of word."""
dim = self.get_dimension()
b = fasttext.Vector(dim)
self.f.getWordVector(b, word)
return np.array(b) | python | def get_word_vector(self, word):
"""Get the vector representation of word."""
dim = self.get_dimension()
b = fasttext.Vector(dim)
self.f.getWordVector(b, word)
return np.array(b) | [
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22,950 | facebookresearch/fastText | python/fastText/FastText.py | _FastText.get_subwords | def get_subwords(self, word, on_unicode_error='strict'):
"""
Given a word, get the subwords and their indicies.
"""
pair = self.f.getSubwords(word, on_unicode_error)
return pair[0], np.array(pair[1]) | python | def get_subwords(self, word, on_unicode_error='strict'):
"""
Given a word, get the subwords and their indicies.
"""
pair = self.f.getSubwords(word, on_unicode_error)
return pair[0], np.array(pair[1]) | [
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22,951 | facebookresearch/fastText | python/fastText/FastText.py | _FastText.get_input_vector | def get_input_vector(self, ind):
"""
Given an index, get the corresponding vector of the Input Matrix.
"""
dim = self.get_dimension()
b = fasttext.Vector(dim)
self.f.getInputVector(b, ind)
return np.array(b) | python | def get_input_vector(self, ind):
"""
Given an index, get the corresponding vector of the Input Matrix.
"""
dim = self.get_dimension()
b = fasttext.Vector(dim)
self.f.getInputVector(b, ind)
return np.array(b) | [
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22,952 | facebookresearch/fastText | python/fastText/FastText.py | _FastText.predict | def predict(self, text, k=1, threshold=0.0, on_unicode_error='strict'):
"""
Given a string, get a list of labels and a list of
corresponding probabilities. k controls the number
of returned labels. A choice of 5, will return the 5
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"""
Given a string, get a list of labels and a list of
corresponding probabilities. k controls the number
of returned labels. A choice of 5, will return the 5
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22,953 | facebookresearch/fastText | python/fastText/FastText.py | _FastText.get_input_matrix | def get_input_matrix(self):
"""
Get a copy of the full input matrix of a Model. This only
works if the model is not quantized.
"""
if self.f.isQuant():
raise ValueError("Can't get quantized Matrix")
return np.array(self.f.getInputMatrix()) | python | def get_input_matrix(self):
"""
Get a copy of the full input matrix of a Model. This only
works if the model is not quantized.
"""
if self.f.isQuant():
raise ValueError("Can't get quantized Matrix")
return np.array(self.f.getInputMatrix()) | [
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22,954 | facebookresearch/fastText | python/fastText/FastText.py | _FastText.get_output_matrix | def get_output_matrix(self):
"""
Get a copy of the full output matrix of a Model. This only
works if the model is not quantized.
"""
if self.f.isQuant():
raise ValueError("Can't get quantized Matrix")
return np.array(self.f.getOutputMatrix()) | python | def get_output_matrix(self):
"""
Get a copy of the full output matrix of a Model. This only
works if the model is not quantized.
"""
if self.f.isQuant():
raise ValueError("Can't get quantized Matrix")
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22,955 | facebookresearch/fastText | python/fastText/FastText.py | _FastText.get_words | def get_words(self, include_freq=False, on_unicode_error='strict'):
"""
Get the entire list of words of the dictionary optionally
including the frequency of the individual words. This
does not include any subwords. For that please consult
the function get_subwords.
"""
... | python | def get_words(self, include_freq=False, on_unicode_error='strict'):
"""
Get the entire list of words of the dictionary optionally
including the frequency of the individual words. This
does not include any subwords. For that please consult
the function get_subwords.
"""
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22,956 | facebookresearch/fastText | python/fastText/FastText.py | _FastText.get_labels | def get_labels(self, include_freq=False, on_unicode_error='strict'):
"""
Get the entire list of labels of the dictionary optionally
including the frequency of the individual labels. Unsupervised
models use words as labels, which is why get_labels
will call and return get_words fo... | python | def get_labels(self, include_freq=False, on_unicode_error='strict'):
"""
Get the entire list of labels of the dictionary optionally
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22,957 | facebookresearch/fastText | python/fastText/FastText.py | _FastText.quantize | def quantize(
self,
input=None,
qout=False,
cutoff=0,
retrain=False,
epoch=None,
lr=None,
thread=None,
verbose=None,
dsub=2,
qnorm=False
):
"""
Quantize the model reducing the size of the model and
it's m... | python | def quantize(
self,
input=None,
qout=False,
cutoff=0,
retrain=False,
epoch=None,
lr=None,
thread=None,
verbose=None,
dsub=2,
qnorm=False
):
"""
Quantize the model reducing the size of the model and
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22,958 | allenai/allennlp | allennlp/common/registrable.py | Registrable.list_available | def list_available(cls) -> List[str]:
"""List default first if it exists"""
keys = list(Registrable._registry[cls].keys())
default = cls.default_implementation
if default is None:
return keys
elif default not in keys:
message = "Default implementation %s ... | python | def list_available(cls) -> List[str]:
"""List default first if it exists"""
keys = list(Registrable._registry[cls].keys())
default = cls.default_implementation
if default is None:
return keys
elif default not in keys:
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22,959 | allenai/allennlp | allennlp/common/util.py | sanitize | def sanitize(x: Any) -> Any: # pylint: disable=invalid-name,too-many-return-statements
"""
Sanitize turns PyTorch and Numpy types into basic Python types so they
can be serialized into JSON.
"""
if isinstance(x, (str, float, int, bool)):
# x is already serializable
return x
elif... | python | def sanitize(x: Any) -> Any: # pylint: disable=invalid-name,too-many-return-statements
"""
Sanitize turns PyTorch and Numpy types into basic Python types so they
can be serialized into JSON.
"""
if isinstance(x, (str, float, int, bool)):
# x is already serializable
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22,960 | allenai/allennlp | allennlp/common/util.py | group_by_count | def group_by_count(iterable: List[Any], count: int, default_value: Any) -> List[List[Any]]:
"""
Takes a list and groups it into sublists of size ``count``, using ``default_value`` to pad the
list at the end if the list is not divisable by ``count``.
For example:
>>> group_by_count([1, 2, 3, 4, 5, 6... | python | def group_by_count(iterable: List[Any], count: int, default_value: Any) -> List[List[Any]]:
"""
Takes a list and groups it into sublists of size ``count``, using ``default_value`` to pad the
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22,961 | allenai/allennlp | allennlp/common/util.py | lazy_groups_of | def lazy_groups_of(iterator: Iterator[A], group_size: int) -> Iterator[List[A]]:
"""
Takes an iterator and batches the individual instances into lists of the
specified size. The last list may be smaller if there are instances left over.
"""
return iter(lambda: list(islice(iterator, 0, group_size)), ... | python | def lazy_groups_of(iterator: Iterator[A], group_size: int) -> Iterator[List[A]]:
"""
Takes an iterator and batches the individual instances into lists of the
specified size. The last list may be smaller if there are instances left over.
"""
return iter(lambda: list(islice(iterator, 0, group_size)), ... | [
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22,962 | allenai/allennlp | allennlp/common/util.py | pad_sequence_to_length | def pad_sequence_to_length(sequence: List,
desired_length: int,
default_value: Callable[[], Any] = lambda: 0,
padding_on_right: bool = True) -> List:
"""
Take a list of objects and pads it to the desired length, returning the padde... | python | def pad_sequence_to_length(sequence: List,
desired_length: int,
default_value: Callable[[], Any] = lambda: 0,
padding_on_right: bool = True) -> List:
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22,963 | allenai/allennlp | allennlp/common/util.py | add_noise_to_dict_values | def add_noise_to_dict_values(dictionary: Dict[A, float], noise_param: float) -> Dict[A, float]:
"""
Returns a new dictionary with noise added to every key in ``dictionary``. The noise is
uniformly distributed within ``noise_param`` percent of the value for every value in the
dictionary.
"""
new... | python | def add_noise_to_dict_values(dictionary: Dict[A, float], noise_param: float) -> Dict[A, float]:
"""
Returns a new dictionary with noise added to every key in ``dictionary``. The noise is
uniformly distributed within ``noise_param`` percent of the value for every value in the
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22,964 | allenai/allennlp | allennlp/common/util.py | prepare_global_logging | def prepare_global_logging(serialization_dir: str, file_friendly_logging: bool) -> logging.FileHandler:
"""
This function configures 3 global logging attributes - streaming stdout and stderr
to a file as well as the terminal, setting the formatting for the python logging
library and setting the interval... | python | def prepare_global_logging(serialization_dir: str, file_friendly_logging: bool) -> logging.FileHandler:
"""
This function configures 3 global logging attributes - streaming stdout and stderr
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22,965 | allenai/allennlp | allennlp/common/util.py | cleanup_global_logging | def cleanup_global_logging(stdout_handler: logging.FileHandler) -> None:
"""
This function closes any open file handles and logs set up by `prepare_global_logging`.
Parameters
----------
stdout_handler : ``logging.FileHandler``, required.
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22,966 | allenai/allennlp | allennlp/common/util.py | get_spacy_model | def get_spacy_model(spacy_model_name: str, pos_tags: bool, parse: bool, ner: bool) -> SpacyModelType:
"""
In order to avoid loading spacy models a whole bunch of times, we'll save references to them,
keyed by the options we used to create the spacy model, so any particular configuration only
gets loaded... | python | def get_spacy_model(spacy_model_name: str, pos_tags: bool, parse: bool, ner: bool) -> SpacyModelType:
"""
In order to avoid loading spacy models a whole bunch of times, we'll save references to them,
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22,967 | allenai/allennlp | allennlp/common/util.py | import_submodules | def import_submodules(package_name: str) -> None:
"""
Import all submodules under the given package.
Primarily useful so that people using AllenNLP as a library
can specify their own custom packages and have their custom
classes get loaded and registered.
"""
importlib.invalidate_caches()
... | python | def import_submodules(package_name: str) -> None:
"""
Import all submodules under the given package.
Primarily useful so that people using AllenNLP as a library
can specify their own custom packages and have their custom
classes get loaded and registered.
"""
importlib.invalidate_caches()
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22,968 | allenai/allennlp | allennlp/common/util.py | ensure_list | def ensure_list(iterable: Iterable[A]) -> List[A]:
"""
An Iterable may be a list or a generator.
This ensures we get a list without making an unnecessary copy.
"""
if isinstance(iterable, list):
return iterable
else:
return list(iterable) | python | def ensure_list(iterable: Iterable[A]) -> List[A]:
"""
An Iterable may be a list or a generator.
This ensures we get a list without making an unnecessary copy.
"""
if isinstance(iterable, list):
return iterable
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22,969 | allenai/allennlp | allennlp/state_machines/states/checklist_statelet.py | ChecklistStatelet.update | def update(self, action: torch.Tensor) -> 'ChecklistStatelet':
"""
Takes an action index, updates checklist and returns an updated state.
"""
checklist_addition = (self.terminal_actions == action).float()
new_checklist = self.checklist + checklist_addition
new_checklist_s... | python | def update(self, action: torch.Tensor) -> 'ChecklistStatelet':
"""
Takes an action index, updates checklist and returns an updated state.
"""
checklist_addition = (self.terminal_actions == action).float()
new_checklist = self.checklist + checklist_addition
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22,970 | allenai/allennlp | allennlp/semparse/worlds/wikitables_world.py | WikiTablesWorld._remove_action_from_type | def _remove_action_from_type(valid_actions: Dict[str, List[str]],
type_: str,
filter_function: Callable[[str], bool]) -> None:
"""
Finds the production rule matching the filter function in the given type's valid action
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type_: str,
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22,971 | allenai/allennlp | allennlp/models/semantic_parsing/wikitables/wikitables_semantic_parser.py | WikiTablesSemanticParser._get_neighbor_indices | def _get_neighbor_indices(worlds: List[WikiTablesWorld],
num_entities: int,
tensor: torch.Tensor) -> torch.LongTensor:
"""
This method returns the indices of each entity's neighbors. A tensor
is accepted as a parameter for copying purpo... | python | def _get_neighbor_indices(worlds: List[WikiTablesWorld],
num_entities: int,
tensor: torch.Tensor) -> torch.LongTensor:
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] | 648a36f77db7e45784c047176074f98534c76636 | https://github.com/allenai/allennlp/blob/648a36f77db7e45784c047176074f98534c76636/allennlp/models/semantic_parsing/wikitables/wikitables_semantic_parser.py#L299-L341 |
22,972 | allenai/allennlp | allennlp/models/semantic_parsing/wikitables/wikitables_semantic_parser.py | WikiTablesSemanticParser._get_linking_probabilities | def _get_linking_probabilities(self,
worlds: List[WikiTablesWorld],
linking_scores: torch.FloatTensor,
question_mask: torch.LongTensor,
entity_type_dict: Dict[int, int]) -> torch.F... | python | def _get_linking_probabilities(self,
worlds: List[WikiTablesWorld],
linking_scores: torch.FloatTensor,
question_mask: torch.LongTensor,
entity_type_dict: Dict[int, int]) -> torch.F... | [
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22,973 | allenai/allennlp | allennlp/models/semantic_parsing/wikitables/wikitables_semantic_parser.py | WikiTablesSemanticParser._create_grammar_state | def _create_grammar_state(self,
world: WikiTablesWorld,
possible_actions: List[ProductionRule],
linking_scores: torch.Tensor,
entity_types: torch.Tensor) -> LambdaGrammarStatelet:
"""
... | python | def _create_grammar_state(self,
world: WikiTablesWorld,
possible_actions: List[ProductionRule],
linking_scores: torch.Tensor,
entity_types: torch.Tensor) -> LambdaGrammarStatelet:
"""
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22,974 | allenai/allennlp | allennlp/state_machines/transition_functions/linking_coverage_transition_function.py | LinkingCoverageTransitionFunction._get_linked_logits_addition | def _get_linked_logits_addition(checklist_state: ChecklistStatelet,
action_ids: List[int],
action_logits: torch.Tensor) -> torch.Tensor:
"""
Gets the logits of desired terminal actions yet to be produced by the decoder, and
... | python | def _get_linked_logits_addition(checklist_state: ChecklistStatelet,
action_ids: List[int],
action_logits: torch.Tensor) -> torch.Tensor:
"""
Gets the logits of desired terminal actions yet to be produced by the decoder, and
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22,975 | allenai/allennlp | allennlp/semparse/action_space_walker.py | ActionSpaceWalker._walk | def _walk(self) -> None:
"""
Walk over action space to collect completed paths of at most ``self._max_path_length`` steps.
"""
# Buffer of NTs to expand, previous actions
incomplete_paths = [([str(type_)], [f"{START_SYMBOL} -> {type_}"]) for type_ in
s... | python | def _walk(self) -> None:
"""
Walk over action space to collect completed paths of at most ``self._max_path_length`` steps.
"""
# Buffer of NTs to expand, previous actions
incomplete_paths = [([str(type_)], [f"{START_SYMBOL} -> {type_}"]) for type_ in
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22,976 | allenai/allennlp | allennlp/data/dataset.py | Batch._check_types | def _check_types(self) -> None:
"""
Check that all the instances have the same types.
"""
all_instance_fields_and_types: List[Dict[str, str]] = [{k: v.__class__.__name__
for k, v in x.fields.items()}
... | python | def _check_types(self) -> None:
"""
Check that all the instances have the same types.
"""
all_instance_fields_and_types: List[Dict[str, str]] = [{k: v.__class__.__name__
for k, v in x.fields.items()}
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22,977 | allenai/allennlp | allennlp/data/dataset.py | Batch.as_tensor_dict | def as_tensor_dict(self,
padding_lengths: Dict[str, Dict[str, int]] = None,
verbose: bool = False) -> Dict[str, Union[torch.Tensor, Dict[str, torch.Tensor]]]:
# This complex return type is actually predefined elsewhere as a DataArray,
# but we can't use it b... | python | def as_tensor_dict(self,
padding_lengths: Dict[str, Dict[str, int]] = None,
verbose: bool = False) -> Dict[str, Union[torch.Tensor, Dict[str, torch.Tensor]]]:
# This complex return type is actually predefined elsewhere as a DataArray,
# but we can't use it b... | [
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22,978 | allenai/allennlp | allennlp/semparse/worlds/atis_world.py | get_strings_from_utterance | def get_strings_from_utterance(tokenized_utterance: List[Token]) -> Dict[str, List[int]]:
"""
Based on the current utterance, return a dictionary where the keys are the strings in
the database that map to lists of the token indices that they are linked to.
"""
string_linking_scores: Dict[str, List[i... | python | def get_strings_from_utterance(tokenized_utterance: List[Token]) -> Dict[str, List[int]]:
"""
Based on the current utterance, return a dictionary where the keys are the strings in
the database that map to lists of the token indices that they are linked to.
"""
string_linking_scores: Dict[str, List[i... | [
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22,979 | allenai/allennlp | allennlp/semparse/worlds/atis_world.py | AtisWorld._update_grammar | def _update_grammar(self):
"""
We create a new ``Grammar`` object from the one in ``AtisSqlTableContext``, that also
has the new entities that are extracted from the utterance. Stitching together the expressions
to form the grammar is a little tedious here, but it is worth it because we ... | python | def _update_grammar(self):
"""
We create a new ``Grammar`` object from the one in ``AtisSqlTableContext``, that also
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22,980 | allenai/allennlp | allennlp/semparse/worlds/atis_world.py | AtisWorld._update_expression_reference | def _update_expression_reference(self, # pylint: disable=no-self-use
grammar: Grammar,
parent_expression_nonterminal: str,
child_expression_nonterminal: str) -> None:
"""
When we add a new expr... | python | def _update_expression_reference(self, # pylint: disable=no-self-use
grammar: Grammar,
parent_expression_nonterminal: str,
child_expression_nonterminal: str) -> None:
"""
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22,981 | allenai/allennlp | allennlp/semparse/worlds/atis_world.py | AtisWorld._get_sequence_with_spacing | def _get_sequence_with_spacing(self, # pylint: disable=no-self-use
new_grammar,
expressions: List[Expression],
name: str = '') -> Sequence:
"""
This is a helper method for generating sequences, since... | python | def _get_sequence_with_spacing(self, # pylint: disable=no-self-use
new_grammar,
expressions: List[Expression],
name: str = '') -> Sequence:
"""
This is a helper method for generating sequences, since... | [
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22,982 | allenai/allennlp | allennlp/semparse/worlds/atis_world.py | AtisWorld._get_linked_entities | def _get_linked_entities(self) -> Dict[str, Dict[str, Tuple[str, str, List[int]]]]:
"""
This method gets entities from the current utterance finds which tokens they are linked to.
The entities are divided into two main groups, ``numbers`` and ``strings``. We rely on these
entities later ... | python | def _get_linked_entities(self) -> Dict[str, Dict[str, Tuple[str, str, List[int]]]]:
"""
This method gets entities from the current utterance finds which tokens they are linked to.
The entities are divided into two main groups, ``numbers`` and ``strings``. We rely on these
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22,983 | allenai/allennlp | allennlp/service/config_explorer.py | make_app | def make_app(include_packages: Sequence[str] = ()) -> Flask:
"""
Creates a Flask app that serves up a simple configuration wizard.
"""
# Load modules
for package_name in include_packages:
import_submodules(package_name)
app = Flask(__name__) # pylint: disable=invalid-name
@app.err... | python | def make_app(include_packages: Sequence[str] = ()) -> Flask:
"""
Creates a Flask app that serves up a simple configuration wizard.
"""
# Load modules
for package_name in include_packages:
import_submodules(package_name)
app = Flask(__name__) # pylint: disable=invalid-name
@app.err... | [
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22,984 | allenai/allennlp | allennlp/training/metrics/fbeta_measure.py | _prf_divide | def _prf_divide(numerator, denominator):
"""Performs division and handles divide-by-zero.
On zero-division, sets the corresponding result elements to zero.
"""
result = numerator / denominator
mask = denominator == 0.0
if not mask.any():
return result
# remove nan
result[mask] ... | python | def _prf_divide(numerator, denominator):
"""Performs division and handles divide-by-zero.
On zero-division, sets the corresponding result elements to zero.
"""
result = numerator / denominator
mask = denominator == 0.0
if not mask.any():
return result
# remove nan
result[mask] ... | [
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22,985 | allenai/allennlp | tutorials/tagger/basic_pytorch.py | load_data | def load_data(file_path: str) -> Tuple[List[str], List[str]]:
"""
One sentence per line, formatted like
The###DET dog###NN ate###V the###DET apple###NN
Returns a list of pairs (tokenized_sentence, tags)
"""
data = []
with open(file_path) as f:
for line in f:
pairs ... | python | def load_data(file_path: str) -> Tuple[List[str], List[str]]:
"""
One sentence per line, formatted like
The###DET dog###NN ate###V the###DET apple###NN
Returns a list of pairs (tokenized_sentence, tags)
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22,986 | allenai/allennlp | allennlp/data/vocabulary.py | Vocabulary.save_to_files | def save_to_files(self, directory: str) -> None:
"""
Persist this Vocabulary to files so it can be reloaded later.
Each namespace corresponds to one file.
Parameters
----------
directory : ``str``
The directory where we save the serialized vocabulary.
... | python | def save_to_files(self, directory: str) -> None:
"""
Persist this Vocabulary to files so it can be reloaded later.
Each namespace corresponds to one file.
Parameters
----------
directory : ``str``
The directory where we save the serialized vocabulary.
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22,987 | allenai/allennlp | allennlp/data/vocabulary.py | Vocabulary.from_files | def from_files(cls, directory: str) -> 'Vocabulary':
"""
Loads a ``Vocabulary`` that was serialized using ``save_to_files``.
Parameters
----------
directory : ``str``
The directory containing the serialized vocabulary.
"""
logger.info("Loading token d... | python | def from_files(cls, directory: str) -> 'Vocabulary':
"""
Loads a ``Vocabulary`` that was serialized using ``save_to_files``.
Parameters
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directory : ``str``
The directory containing the serialized vocabulary.
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22,988 | allenai/allennlp | allennlp/data/vocabulary.py | Vocabulary.set_from_file | def set_from_file(self,
filename: str,
is_padded: bool = True,
oov_token: str = DEFAULT_OOV_TOKEN,
namespace: str = "tokens"):
"""
If you already have a vocabulary file for a trained model somewhere, and you really w... | python | def set_from_file(self,
filename: str,
is_padded: bool = True,
oov_token: str = DEFAULT_OOV_TOKEN,
namespace: str = "tokens"):
"""
If you already have a vocabulary file for a trained model somewhere, and you really w... | [
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22,989 | allenai/allennlp | allennlp/data/vocabulary.py | Vocabulary.extend_from_instances | def extend_from_instances(self,
params: Params,
instances: Iterable['adi.Instance'] = ()) -> None:
"""
Extends an already generated vocabulary using a collection of instances.
"""
min_count = params.pop("min_count", None)
... | python | def extend_from_instances(self,
params: Params,
instances: Iterable['adi.Instance'] = ()) -> None:
"""
Extends an already generated vocabulary using a collection of instances.
"""
min_count = params.pop("min_count", None)
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22,990 | allenai/allennlp | allennlp/data/vocabulary.py | Vocabulary.is_padded | def is_padded(self, namespace: str) -> bool:
"""
Returns whether or not there are padding and OOV tokens added to the given namespace.
"""
return self._index_to_token[namespace][0] == self._padding_token | python | def is_padded(self, namespace: str) -> bool:
"""
Returns whether or not there are padding and OOV tokens added to the given namespace.
"""
return self._index_to_token[namespace][0] == self._padding_token | [
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22,991 | allenai/allennlp | allennlp/data/vocabulary.py | Vocabulary.add_token_to_namespace | def add_token_to_namespace(self, token: str, namespace: str = 'tokens') -> int:
"""
Adds ``token`` to the index, if it is not already present. Either way, we return the index of
the token.
"""
if not isinstance(token, str):
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"""
Adds ``token`` to the index, if it is not already present. Either way, we return the index of
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"""
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22,992 | allenai/allennlp | allennlp/models/model.py | Model.get_regularization_penalty | def get_regularization_penalty(self) -> Union[float, torch.Tensor]:
"""
Computes the regularization penalty for the model.
Returns 0 if the model was not configured to use regularization.
"""
if self._regularizer is None:
return 0.0
else:
return se... | python | def get_regularization_penalty(self) -> Union[float, torch.Tensor]:
"""
Computes the regularization penalty for the model.
Returns 0 if the model was not configured to use regularization.
"""
if self._regularizer is None:
return 0.0
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22,993 | allenai/allennlp | allennlp/models/model.py | Model._get_prediction_device | def _get_prediction_device(self) -> int:
"""
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Returns
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"""
This method checks the device of the model parameters to determine the cuda_device
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22,994 | allenai/allennlp | allennlp/models/model.py | Model._maybe_warn_for_unseparable_batches | def _maybe_warn_for_unseparable_batches(self, output_key: str):
"""
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22,995 | allenai/allennlp | allennlp/semparse/domain_languages/wikitables_language.py | WikiTablesLanguage.evaluate_logical_form | def evaluate_logical_form(self, logical_form: str, target_list: List[str]) -> bool:
"""
Takes a logical form, and the list of target values as strings from the original lisp
string, and returns True iff the logical form executes to the target list, using the
official WikiTableQuestions e... | python | def evaluate_logical_form(self, logical_form: str, target_list: List[str]) -> bool:
"""
Takes a logical form, and the list of target values as strings from the original lisp
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22,996 | allenai/allennlp | allennlp/semparse/domain_languages/wikitables_language.py | WikiTablesLanguage.select_string | def select_string(self, rows: List[Row], column: StringColumn) -> List[str]:
"""
Select function takes a list of rows and a column name and returns a list of strings as
in cells.
"""
return [str(row.values[column.name]) for row in rows if row.values[column.name] is not None] | python | def select_string(self, rows: List[Row], column: StringColumn) -> List[str]:
"""
Select function takes a list of rows and a column name and returns a list of strings as
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"""
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22,997 | allenai/allennlp | allennlp/semparse/domain_languages/wikitables_language.py | WikiTablesLanguage.select_date | def select_date(self, rows: List[Row], column: DateColumn) -> Date:
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Select function takes a row as a list and a column name and returns the date in that column.
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for row in rows:
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if isinsta... | python | def select_date(self, rows: List[Row], column: DateColumn) -> Date:
"""
Select function takes a row as a list and a column name and returns the date in that column.
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dates: List[Date] = []
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22,998 | allenai/allennlp | allennlp/semparse/domain_languages/wikitables_language.py | WikiTablesLanguage.same_as | def same_as(self, rows: List[Row], column: Column) -> List[Row]:
"""
Takes a row and a column and returns a list of rows from the full set of rows that contain
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"""
cell_value = rows[0].values[column.name]
return_list... | python | def same_as(self, rows: List[Row], column: Column) -> List[Row]:
"""
Takes a row and a column and returns a list of rows from the full set of rows that contain
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] | 648a36f77db7e45784c047176074f98534c76636 | https://github.com/allenai/allennlp/blob/648a36f77db7e45784c047176074f98534c76636/allennlp/semparse/domain_languages/wikitables_language.py#L389-L399 |
22,999 | allenai/allennlp | allennlp/semparse/domain_languages/wikitables_language.py | WikiTablesLanguage.date | def date(self, year: Number, month: Number, day: Number) -> Date:
"""
Takes three numbers and returns a ``Date`` object whose year, month, and day are the three
numbers in that order.
"""
return Date(year, month, day) | python | def date(self, year: Number, month: Number, day: Number) -> Date:
"""
Takes three numbers and returns a ``Date`` object whose year, month, and day are the three
numbers in that order.
"""
return Date(year, month, day) | [
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