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train | Dataset.set_label | Set label of Dataset.
Parameters
----------
label : list, numpy 1-D array, pandas Series / one-column DataFrame or None
The label information to be set into Dataset.
Returns
-------
self : Dataset
Dataset with set label. | python-package/lightgbm/basic.py | def set_label(self, label):
"""Set label of Dataset.
Parameters
----------
label : list, numpy 1-D array, pandas Series / one-column DataFrame or None
The label information to be set into Dataset.
Returns
-------
self : Dataset
Dataset wi... | def set_label(self, label):
"""Set label of Dataset.
Parameters
----------
label : list, numpy 1-D array, pandas Series / one-column DataFrame or None
The label information to be set into Dataset.
Returns
-------
self : Dataset
Dataset wi... | [
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train | Dataset.set_weight | Set weight of each instance.
Parameters
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weight : list, numpy 1-D array, pandas Series or None
Weight to be set for each data point.
Returns
-------
self : Dataset
Dataset with set weight. | python-package/lightgbm/basic.py | def set_weight(self, weight):
"""Set weight of each instance.
Parameters
----------
weight : list, numpy 1-D array, pandas Series or None
Weight to be set for each data point.
Returns
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Dataset with set weight.
... | def set_weight(self, weight):
"""Set weight of each instance.
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weight : list, numpy 1-D array, pandas Series or None
Weight to be set for each data point.
Returns
-------
self : Dataset
Dataset with set weight.
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train | Dataset.set_init_score | Set init score of Booster to start from.
Parameters
----------
init_score : list, numpy 1-D array, pandas Series or None
Init score for Booster.
Returns
-------
self : Dataset
Dataset with set init score. | python-package/lightgbm/basic.py | def set_init_score(self, init_score):
"""Set init score of Booster to start from.
Parameters
----------
init_score : list, numpy 1-D array, pandas Series or None
Init score for Booster.
Returns
-------
self : Dataset
Dataset with set init... | def set_init_score(self, init_score):
"""Set init score of Booster to start from.
Parameters
----------
init_score : list, numpy 1-D array, pandas Series or None
Init score for Booster.
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train | Dataset.set_group | Set group size of Dataset (used for ranking).
Parameters
----------
group : list, numpy 1-D array, pandas Series or None
Group size of each group.
Returns
-------
self : Dataset
Dataset with set group. | python-package/lightgbm/basic.py | def set_group(self, group):
"""Set group size of Dataset (used for ranking).
Parameters
----------
group : list, numpy 1-D array, pandas Series or None
Group size of each group.
Returns
-------
self : Dataset
Dataset with set group.
... | def set_group(self, group):
"""Set group size of Dataset (used for ranking).
Parameters
----------
group : list, numpy 1-D array, pandas Series or None
Group size of each group.
Returns
-------
self : Dataset
Dataset with set group.
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train | Dataset.get_label | Get the label of the Dataset.
Returns
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"""Get the label of the Dataset.
Returns
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label : numpy array or None
The label information from the Dataset.
"""
if self.label is None:
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return self.label | def get_label(self):
"""Get the label of the Dataset.
Returns
-------
label : numpy array or None
The label information from the Dataset.
"""
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train | Dataset.get_weight | Get the weight of the Dataset.
Returns
-------
weight : numpy array or None
Weight for each data point from the Dataset. | python-package/lightgbm/basic.py | def get_weight(self):
"""Get the weight of the Dataset.
Returns
-------
weight : numpy array or None
Weight for each data point from the Dataset.
"""
if self.weight is None:
self.weight = self.get_field('weight')
return self.weight | def get_weight(self):
"""Get the weight of the Dataset.
Returns
-------
weight : numpy array or None
Weight for each data point from the Dataset.
"""
if self.weight is None:
self.weight = self.get_field('weight')
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train | Dataset.get_feature_penalty | Get the feature penalty of the Dataset.
Returns
-------
feature_penalty : numpy array or None
Feature penalty for each feature in the Dataset. | python-package/lightgbm/basic.py | def get_feature_penalty(self):
"""Get the feature penalty of the Dataset.
Returns
-------
feature_penalty : numpy array or None
Feature penalty for each feature in the Dataset.
"""
if self.feature_penalty is None:
self.feature_penalty = self.get_f... | def get_feature_penalty(self):
"""Get the feature penalty of the Dataset.
Returns
-------
feature_penalty : numpy array or None
Feature penalty for each feature in the Dataset.
"""
if self.feature_penalty is None:
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train | Dataset.get_monotone_constraints | Get the monotone constraints of the Dataset.
Returns
-------
monotone_constraints : numpy array or None
Monotone constraints: -1, 0 or 1, for each feature in the Dataset. | python-package/lightgbm/basic.py | def get_monotone_constraints(self):
"""Get the monotone constraints of the Dataset.
Returns
-------
monotone_constraints : numpy array or None
Monotone constraints: -1, 0 or 1, for each feature in the Dataset.
"""
if self.monotone_constraints is None:
... | def get_monotone_constraints(self):
"""Get the monotone constraints of the Dataset.
Returns
-------
monotone_constraints : numpy array or None
Monotone constraints: -1, 0 or 1, for each feature in the Dataset.
"""
if self.monotone_constraints is None:
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train | Dataset.get_init_score | Get the initial score of the Dataset.
Returns
-------
init_score : numpy array or None
Init score of Booster. | python-package/lightgbm/basic.py | def get_init_score(self):
"""Get the initial score of the Dataset.
Returns
-------
init_score : numpy array or None
Init score of Booster.
"""
if self.init_score is None:
self.init_score = self.get_field('init_score')
return self.init_scor... | def get_init_score(self):
"""Get the initial score of the Dataset.
Returns
-------
init_score : numpy array or None
Init score of Booster.
"""
if self.init_score is None:
self.init_score = self.get_field('init_score')
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train | Dataset.get_data | 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. | python-package/lightgbm/basic.py | 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:
... | 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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train | Dataset.get_group | Get the group of the Dataset.
Returns
-------
group : numpy array or None
Group size of each group. | python-package/lightgbm/basic.py | 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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# gr... | def get_group(self):
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Group size of each group.
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train | Dataset.num_data | Get the number of rows in the Dataset.
Returns
-------
number_of_rows : int
The number of rows in the Dataset. | python-package/lightgbm/basic.py | 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... | def num_data(self):
"""Get the number of rows in the Dataset.
Returns
-------
number_of_rows : int
The number of rows in the Dataset.
"""
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train | Dataset.num_feature | Get the number of columns (features) in the Dataset.
Returns
-------
number_of_columns : int
The number of columns (features) in the Dataset. | python-package/lightgbm/basic.py | def num_feature(self):
"""Get the number of columns (features) in the Dataset.
Returns
-------
number_of_columns : int
The number of columns (features) in the Dataset.
"""
if self.handle is not None:
ret = ctypes.c_int()
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"""Get the number of columns (features) in the Dataset.
Returns
-------
number_of_columns : int
The number of columns (features) in the Dataset.
"""
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train | Dataset.get_ref_chain | 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 (default=100)
The limit number of referen... | python-package/lightgbm/basic.py | 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... | 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.
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----------
ref_limit : int, optional (d... | [
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train | Dataset.add_features_from | 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
-------
self : Dataset
Dataset with th... | python-package/lightgbm/basic.py | 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
-------
... | 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.
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-------
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train | Dataset.dump_text | 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 : Dataset
Returns self. | python-package/lightgbm/basic.py | 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... | 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
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train | Booster.free_dataset | Free Booster's Datasets.
Returns
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"""Free Booster's Datasets.
Returns
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Booster without Datasets.
"""
self.__dict__.pop('train_set', None)
self.__dict__.pop('valid_sets', None)
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"""Free Booster's Datasets.
Returns
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self : Booster
Booster without Datasets.
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train | Booster.set_network | Set the network configuration.
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machines : list, set or string
Names of machines.
local_listen_port : int, optional (default=12400)
TCP listen port for local machines.
listen_time_out : int, optional (default=120)
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"""Set the network configuration.
Parameters
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machines : list, set or string
Names of machines.
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Names of machines.
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train | Booster.add_valid | Add validation data.
Parameters
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data : Dataset
Validation data.
name : string
Name of validation data.
Returns
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self : Booster
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Parameters
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Validation data.
name : string
Name of validation data.
Returns
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Booster with set validation data.
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"""Add validation data.
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Validation data.
name : string
Name of validation data.
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Booster with set validation data.
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train | Booster.reset_parameter | Reset parameters of Booster.
Parameters
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params : dict
New parameters for Booster.
Returns
-------
self : Booster
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"""Reset parameters of Booster.
Parameters
----------
params : dict
New parameters for Booster.
Returns
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"""Reset parameters of Booster.
Parameters
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params : dict
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self : Booster
Booster with new parameters.
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train | Booster.update | Update Booster for one iteration.
Parameters
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train_set : Dataset or None, optional (default=None)
Training data.
If None, last training data is used.
fobj : callable or None, optional (default=None)
Customized objective function.
... | python-package/lightgbm/basic.py | def update(self, train_set=None, fobj=None):
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Parameters
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Training data.
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Training data.
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train | Booster.__boost | Boost Booster for one iteration with customized gradient statistics.
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If you want to get i-th row score in j-th class, the access way is score[j * num_data + i]
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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... | def __boost(self, grad, hess):
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train | Booster.rollback_one_iter | Rollback one iteration.
Returns
-------
self : Booster
Booster with rolled back one iteration. | python-package/lightgbm/basic.py | def rollback_one_iter(self):
"""Rollback one iteration.
Returns
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self : Booster
Booster with rolled back one iteration.
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_safe_call(_LIB.LGBM_BoosterRollbackOneIter(
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"""Rollback one iteration.
Returns
-------
self : Booster
Booster with rolled back one iteration.
"""
_safe_call(_LIB.LGBM_BoosterRollbackOneIter(
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train | Booster.current_iteration | Get the index of the current iteration.
Returns
-------
cur_iter : int
The index of the current iteration. | python-package/lightgbm/basic.py | def current_iteration(self):
"""Get the index of the current iteration.
Returns
-------
cur_iter : int
The index of the current iteration.
"""
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Returns
-------
cur_iter : int
The index of the current iteration.
"""
out_cur_iter = ctypes.c_int(0)
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train | Booster.num_model_per_iteration | Get number of models per iteration.
Returns
-------
model_per_iter : int
The number of models per iteration. | python-package/lightgbm/basic.py | def num_model_per_iteration(self):
"""Get number of models per iteration.
Returns
-------
model_per_iter : int
The number of models per iteration.
"""
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"""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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train | Booster.num_trees | Get number of weak sub-models.
Returns
-------
num_trees : int
The number of weak sub-models. | python-package/lightgbm/basic.py | def num_trees(self):
"""Get number of weak sub-models.
Returns
-------
num_trees : int
The number of weak sub-models.
"""
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"""Get number of weak sub-models.
Returns
-------
num_trees : int
The number of weak sub-models.
"""
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train | Booster.eval | 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 function.
Should accept two parameters: preds,... | python-package/lightgbm/basic.py | 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... | 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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train | Booster.eval_valid | 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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Customized evaluation function.
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Customized evaluation function.
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train | Booster.save_model | 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 saved.
If None, if the best iteration exists, it is saved; otherwise, al... | python-package/lightgbm/basic.py | 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... | def save_model(self, filename, num_iteration=None, start_iteration=0):
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----------
filename : string
Filename to save Booster.
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train | Booster.shuffle_models | 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 that will be shuffled.
If <= 0, means the last available iterati... | python-package/lightgbm/basic.py | 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... | 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)
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train | Booster.model_from_string | 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.
Returns
-------
self : Booster
... | python-package/lightgbm/basic.py | 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.
... | 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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train | Booster.model_to_string | Save Booster to string.
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num_iteration : int or None, optional (default=None)
Index of the iteration that should be saved.
If None, if the best iteration exists, it is saved; otherwise, all iterations are saved.
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Parameters
----------
num_iteration : int or None, optional (default=None)
Index of the iteration that should be saved.
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Index of the iteration that should be saved.
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train | Booster.dump_model | Dump Booster to JSON format.
Parameters
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Index of the iteration that should be dumped.
If None, if the best iteration exists, it is dumped; otherwise, all iterations are dumped.
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Parameters
----------
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Index of the iteration that should be dumped.
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Index of the iteration that should be dumped.
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train | Booster.predict | Make a prediction.
Parameters
----------
data : string, numpy array, pandas DataFrame, H2O DataTable's Frame or scipy.sparse
Data source for prediction.
If string, it represents the path to txt file.
num_iteration : int or None, optional (default=None)
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Parameters
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train | Booster.refit | Refit the existing Booster by new data.
Parameters
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data : string, numpy array, pandas DataFrame, H2O DataTable's Frame or scipy.sparse
Data source for refit.
If string, it represents the path to txt file.
label : list, numpy 1-D array or pandas Series ... | python-package/lightgbm/basic.py | 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... | 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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train | Booster.get_leaf_output | 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 output of the leaf. | python-package/lightgbm/basic.py | 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... | 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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train | Booster._to_predictor | Convert to predictor. | python-package/lightgbm/basic.py | 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 | 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
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train | Booster.num_feature | Get number of features.
Returns
-------
num_feature : int
The number of features. | python-package/lightgbm/basic.py | def num_feature(self):
"""Get number of features.
Returns
-------
num_feature : int
The number of features.
"""
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"""Get number of features.
Returns
-------
num_feature : int
The number of features.
"""
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train | Booster.feature_name | Get names of features.
Returns
-------
result : list
List with names of features. | python-package/lightgbm/basic.py | 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... | def feature_name(self):
"""Get names of features.
Returns
-------
result : list
List with names of features.
"""
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train | Booster.feature_importance | Get feature importances.
Parameters
----------
importance_type : string, optional (default="split")
How the importance is calculated.
If "split", result contains numbers of times the feature is used in a model.
If "gain", result contains total gains of splits... | python-package/lightgbm/basic.py | def feature_importance(self, importance_type='split', iteration=None):
"""Get feature importances.
Parameters
----------
importance_type : string, optional (default="split")
How the importance is calculated.
If "split", result contains numbers of times the featur... | def feature_importance(self, importance_type='split', iteration=None):
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Parameters
----------
importance_type : string, optional (default="split")
How the importance is calculated.
If "split", result contains numbers of times the featur... | [
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train | Booster.get_split_value_histogram | 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 index.
If string, interpreted as name.
Note
----
... | python-package/lightgbm/basic.py | 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... | def get_split_value_histogram(self, feature, bins=None, xgboost_style=False):
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----------
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The feature name or index the histogram is calculated for.
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train | Booster.__inner_eval | Evaluate training or validation data. | python-package/lightgbm/basic.py | 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 = []
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train | Booster.__inner_predict | Predict for training and validation dataset. | python-package/lightgbm/basic.py | 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:
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... | def __inner_predict(self, data_idx):
"""Predict for training and validation dataset."""
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train | Booster.__get_eval_info | Get inner evaluation count and names. | python-package/lightgbm/basic.py | def __get_eval_info(self):
"""Get inner evaluation count and names."""
if self.__need_reload_eval_info:
self.__need_reload_eval_info = False
out_num_eval = ctypes.c_int(0)
# Get num of inner evals
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train | Booster.set_attr | 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-package/lightgbm/basic.py | 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.
... | 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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train | find_lib_path | Find the path to LightGBM library files.
Returns
-------
lib_path: list of strings
List of all found library paths to LightGBM. | python-package/lightgbm/libpath.py | 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... | 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
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train | json_default_with_numpy | Convert numpy classes to JSON serializable objects. | python-package/lightgbm/compat.py | def json_default_with_numpy(obj):
"""Convert numpy classes to JSON serializable objects."""
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train | _format_eval_result | Format metric string. | python-package/lightgbm/callback.py | def _format_eval_result(value, show_stdv=True):
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train | print_evaluation | Create a callback that prints the evaluation results.
Parameters
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period : int, optional (default=1)
The period to print the evaluation results.
show_stdv : bool, optional (default=True)
Whether to show stdv (if provided).
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period : int, optional (default=1)
The period to print the evaluation results.
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The period to print the evaluation results.
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train | record_evaluation | Create a callback that records the evaluation history into ``eval_result``.
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train | reset_parameter | Create a callback that resets the parameter after the first iteration.
Note
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Parameters
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**kwargs : value should be list or function
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"""Create a callback that resets the parameter after the first iteration.
Note
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Parameters
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Note
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train | early_stopping | Create a callback that activates early stopping.
Note
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Activates early stopping.
The model will train until the validation score stops improving.
Validation score needs to improve at least every ``early_stopping_rounds`` round(s)
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Note
----
Activates early stopping.
The model will train until the validation score stops improving.
Validation score needs to improve at least every ``early_stopping_... | def early_stopping(stopping_rounds, first_metric_only=False, verbose=True):
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The model will train until the validation score stops improving.
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train | train | Perform the training with given parameters.
Parameters
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params : dict
Parameters for training.
train_set : Dataset
Data to be trained on.
num_boost_round : int, optional (default=100)
Number of boosting iterations.
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train | _make_n_folds | Make a n-fold list of Booster from random indices. | python-package/lightgbm/engine.py | def _make_n_folds(full_data, folds, nfold, params, seed, fpreproc=None, stratified=True,
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train | _agg_cv_result | Aggregate cross-validation results. | python-package/lightgbm/engine.py | def _agg_cv_result(raw_results, eval_train_metric=False):
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train | cv | Perform the cross-validation with given paramaters.
Parameters
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params : dict
Parameters for Booster.
train_set : Dataset
Data to be trained on.
num_boost_round : int, optional (default=100)
Number of boosting iterations.
folds : generator or iterator of (train... | python-package/lightgbm/engine.py | def cv(params, train_set, num_boost_round=100,
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feature_name='auto', categorical_feature='auto',
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feature_name='auto', categorical_feature='auto',
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train | log_loss | Logarithmic loss with non-necessarily-binary labels. | examples/python-guide/logistic_regression.py | 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 | def log_loss(preds, labels):
"""Logarithmic loss with non-necessarily-binary labels."""
log_likelihood = np.sum(labels * np.log(preds)) / len(preds)
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train | experiment | 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.
Returns
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Parameters
----------
objective : string 'binary' or 'xentropy'
Objective function.
label_type : string 'binary' or 'probability'
Type of the label.
data : dict
Data for training.
R... | 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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train | _check_not_tuple_of_2_elements | Check object is not tuple or does not have 2 elements. | python-package/lightgbm/plotting.py | def _check_not_tuple_of_2_elements(obj, obj_name='obj'):
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train | plot_importance | Plot model's feature importances.
Parameters
----------
booster : Booster or LGBMModel
Booster or LGBMModel instance which feature importance should be plotted.
ax : matplotlib.axes.Axes or None, optional (default=None)
Target axes instance.
If None, new figure and axes will be ... | python-package/lightgbm/plotting.py | 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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xlim=None, ylim=None, title='Feature importance',
xlabel='Feature importance', ylabel='Features',
importance_type='split', max_num_features=None,
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train | plot_metric | Plot one metric during training.
Parameters
----------
booster : dict or LGBMModel
Dictionary returned from ``lightgbm.train()`` or LGBMModel instance.
metric : string or None, optional (default=None)
The metric name to plot.
Only one metric supported because different metrics h... | python-package/lightgbm/plotting.py | 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
----------
... | 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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train | _to_graphviz | Convert specified tree to graphviz instance.
See:
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"""Convert specified tree to graphviz instance.
See:
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"""
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"""Convert specified tree to graphviz instance.
See:
- https://graphviz.readthedocs.io/en/stable/api.html#digraph
"""
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train | create_tree_digraph | Create a digraph representation of specified tree.
Note
----
For more information please visit
https://graphviz.readthedocs.io/en/stable/api.html#digraph.
Parameters
----------
booster : Booster or LGBMModel
Booster or LGBMModel instance to be converted.
tree_index : int, optio... | python-package/lightgbm/plotting.py | 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... | def create_tree_digraph(booster, tree_index=0, show_info=None, precision=None,
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old_format=None, old_engine=None, old_encoding=None, old_graph_attr=None,
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train | plot_tree | Plot specified tree.
Note
----
It is preferable to use ``create_tree_digraph()`` because of its lossless quality
and returned objects can be also rendered and displayed directly inside a Jupyter notebook.
Parameters
----------
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show_info=None, precision=None, **kwargs):
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train | cpp_flag | Return the -std=c++[0x/11/14] compiler flag.
The c++14 is preferred over c++0x/11 (when it is available). | setup.py | def cpp_flag(compiler):
"""Return the -std=c++[0x/11/14] compiler flag.
The c++14 is preferred over c++0x/11 (when it is available).
"""
standards = ['-std=c++14', '-std=c++11', '-std=c++0x']
for standard in standards:
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"""Return the -std=c++[0x/11/14] compiler flag.
The c++14 is preferred over c++0x/11 (when it is available).
"""
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train | find_nearest_neighbor | query is a 1d numpy array corresponding to the vector to which you want to
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ban_set is a set of indicies within vectors you want to ignore for nearest match
cossims is a 1d numpy array of size len(vector... | python/fastText/util/util.py | def find_nearest_neighbor(query, vectors, ban_set, cossims=None):
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train | train_supervised | Train a supervised model and return a model object.
input must be a filepath. The input text does not need to be tokenized
as per the tokenize function, but it must be preprocessed and encoded
as UTF-8. You might want to consult standard preprocessing scripts such
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minn=0,
maxn=0,
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bucket=2000000,
thread=multiprocessing.cpu_count() - 1,
lrUpdateRate=100,
t=1e-4,
label="__label__",
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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__",
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train | _FastText.get_word_vector | Get the vector representation of word. | python/fastText/FastText.py | 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) | 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)
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train | _FastText.get_sentence_vector | Given a string, get a single vector represenation. This function
assumes to be given a single line of text. We split words on
whitespace (space, newline, tab, vertical tab) and the control
characters carriage return, formfeed and the null character. | python/fastText/FastText.py | def get_sentence_vector(self, text):
"""
Given a string, get a single vector represenation. This function
assumes to be given a single line of text. We split words on
whitespace (space, newline, tab, vertical tab) and the control
characters carriage return, formfeed and the null ... | def get_sentence_vector(self, text):
"""
Given a string, get a single vector represenation. This function
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whitespace (space, newline, tab, vertical tab) and the control
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train | _FastText.get_subwords | Given a word, get the subwords and their indicies. | python/fastText/FastText.py | 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]) | def get_subwords(self, word, on_unicode_error='strict'):
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Given a word, get the subwords and their indicies.
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train | _FastText.get_input_vector | Given an index, get the corresponding vector of the Input Matrix. | python/fastText/FastText.py | 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) | def get_input_vector(self, ind):
"""
Given an index, get the corresponding vector of the Input Matrix.
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dim = self.get_dimension()
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train | _FastText.predict | 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
most probable labels. By default this returns only
the most likely label and probability. threshold filters
the returned labe... | python/fastText/FastText.py | def predict(self, text, k=1, threshold=0.0, on_unicode_error='strict'):
"""
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train | _FastText.get_input_matrix | Get a copy of the full input matrix of a Model. This only
works if the model is not quantized. | python/fastText/FastText.py | 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()) | def get_input_matrix(self):
"""
Get a copy of the full input matrix of a Model. This only
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"""
if self.f.isQuant():
raise ValueError("Can't get quantized Matrix")
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train | _FastText.get_output_matrix | Get a copy of the full output matrix of a Model. This only
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"""
Get a copy of the full output matrix of a Model. This only
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"""
if self.f.isQuant():
raise ValueError("Can't get quantized Matrix")
return np.array(self.f.getOutputMatrix()) | def get_output_matrix(self):
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Get a copy of the full output matrix of a Model. This only
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train | _FastText.get_words | Get the entire list of words of the dictionary optionally
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Split a line of text into words and labels. Labels must start with
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"""
def check(entry):
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Split a line of text into words and labels. Labels must start with
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train | _FastText.quantize | Quantize the model reducing the size of the model and
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train | StackedBidirectionalLstm.forward | Parameters
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inputs : ``PackedSequence``, required.
A batch first ``PackedSequence`` to run the stacked LSTM over.
initial_state : Tuple[torch.Tensor, torch.Tensor], optional, (default = None)
A tuple (state, memory) representing the initial hidden state and memo... | allennlp/modules/stacked_bidirectional_lstm.py | def forward(self, # pylint: disable=arguments-differ
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Converts a List of pairs (regex, params) into an RegularizerApplicator.
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train | Registrable.list_available | List default first if it exists | allennlp/common/registrable.py | def list_available(cls) -> List[str]:
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train | sanitize | Sanitize turns PyTorch and Numpy types into basic Python types so they
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train | group_by_count | Takes a list and groups it into sublists of size ``count``, using ``default_value`` to pad the
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For example:
>>> group_by_count([1, 2, 3, 4, 5, 6, 7], 3, 0)
[[1, 2, 3], [4, 5, 6], [7, 0, 0]]
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"""
Takes a list and groups it into sublists of size ``count``, using ``default_value`` to pad the
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For example:
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train | lazy_groups_of | Takes an iterator and batches the individual instances into lists of the
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sequence : List
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desired_length : int
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train | add_noise_to_dict_values | Returns a new dictionary with noise added to every key in ``dictionary``. The noise is
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Matches a namespace pattern against a namespace string. For example, ``*tags`` matches
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train | import_submodules | Import all submodules under the given package.
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train | peak_memory_mb | Get peak memory usage for this process, as measured by
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https://unix.stackexchange.com/questions/30940/getrusage-system-call-what-is-maximum-resident-set-size
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Get peak memory usage for this process, as measured by
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https://unix.stackexchange.com/questions/30940/getrusage-system-call-what-is-maximum-resident-set-size
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train | gpu_memory_mb | Get the current GPU memory usage.
Based on https://discuss.pytorch.org/t/access-gpu-memory-usage-in-pytorch/3192/4
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``Dict[int, int]``
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``Dict[int, int]``
Keys are device ids as integers.
Values are memory usage as integers in MB.
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train | ensure_list | An Iterable may be a list or a generator.
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"""
An Iterable may be a list or a generator.
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"""
if isinstance(iterable, list):
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train | ChecklistStatelet.update | Takes an action index, updates checklist and returns an updated state. | allennlp/state_machines/states/checklist_statelet.py | 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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train | WikiTablesWorld._remove_action_from_type | Finds the production rule matching the filter function in the given type's valid action
list, and removes it. If there is more than one matching function, we crash. | allennlp/semparse/worlds/wikitables_world.py | def _remove_action_from_type(valid_actions: Dict[str, List[str]],
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"""
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train | LstmCellWithProjection.forward | Parameters
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batch_lengths : ``List[int]``, required.
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train | linkcode_resolve | Determine the URL corresponding to Python object
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https://github.com/numpy/numpy/blob/master/doc/source/conf.py#L290
and https://github.com/Lasagne/Lasagne/pull/262 | doc/conf.py | def linkcode_resolve(domain, info):
"""
Determine the URL corresponding to Python object
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Determine the URL corresponding to Python object
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train | WikiTablesSemanticParser._get_initial_rnn_and_grammar_state | Encodes the question and table, computes a linking between the two, and constructs an
initial RnnStatelet and LambdaGrammarStatelet for each batch instance to pass to the
decoder.
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table: Dict[str, torch.LongTensor],
world: List[WikiTablesWorld],
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... | allenai/allennlp | python | https://github.com/allenai/allennlp/blob/648a36f77db7e45784c047176074f98534c76636/allennlp/models/semantic_parsing/wikitables/wikitables_semantic_parser.py#L140-L296 | [
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"WikiTa... | 648a36f77db7e45784c047176074f98534c76636 |
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