INSTRUCTION stringlengths 1 8.43k | RESPONSE stringlengths 75 104k |
|---|---|
Set label of Dataset. | 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... |
Set weight of each instance. | 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
-------
self : Dataset
Dataset with set weight.
... |
Set init score of Booster to start from. | 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... |
Set group size of Dataset ( used for ranking ). | 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.
... |
Get the label of the Dataset. | def get_label(self):
"""Get the label of the Dataset.
Returns
-------
label : numpy array or None
The label information from the Dataset.
"""
if self.label is None:
self.label = self.get_field('label')
return self.label |
Get the weight of the Dataset. | 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 |
Get the feature penalty of the Dataset. | 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... |
Get the monotone constraints of the Dataset. | 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:
... |
Get the initial score of the Dataset. | 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... |
Get the raw data of the Dataset. | 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:
... |
Get the group of the Dataset. | 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... |
Get the number of rows in the Dataset. | 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... |
Get the number of columns ( features ) in the Dataset. | 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()
_safe_call(_LI... |
Get a chain of Dataset objects. | 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... |
Add features from other Dataset to the current Dataset. | 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
-------
... |
Save Dataset to a text file. | 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... |
Free Booster s Datasets. | 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 |
Set the network configuration. | 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... |
Add validation data. | 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.
... |
Reset parameters of Booster. | def reset_parameter(self, params):
"""Reset parameters of Booster.
Parameters
----------
params : dict
New parameters for Booster.
Returns
-------
self : Booster
Booster with new parameters.
"""
if any(metric_alias in para... |
Update Booster for one iteration. | 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... |
Boost Booster for one iteration with customized gradient statistics. | 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... |
Rollback one iteration. | 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... |
Get the index of the 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,
... |
Get number of models 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)
_safe_call(_LIB.LGBM_BoosterNumModelPerIteration(
self.... |
Get number of weak sub - models. | 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,
ctypes.byref(num... |
Evaluate for data. | 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... |
Evaluate for validation data. | 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... |
Save Booster to file. | 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... |
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... |
Load Booster from a 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.
... |
Save Booster 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.
If None, if the best iteration exists, it is saved... |
Dump Booster to JSON format. | 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... |
Make a prediction. | 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 ... |
Refit the existing Booster by new data. | 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... |
Get the output of a leaf. | 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... |
Convert 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 |
Get number of features. | 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... |
Get names of features. | 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... |
Get feature importances. | 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... |
Get split value histogram for the specified feature. | 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... |
Evaluate training or validation data. | 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:
... |
Predict for training and validation dataset. | 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:
... |
Get inner evaluation count and names. | 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
_safe_call(_LIB.LGBM_BoosterGetEvalCounts(
... |
Set attributes to the Booster. | 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.
... |
Find the path to LightGBM library files. | 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... |
Convert numpy classes to JSON serializable objects. | 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 |
Format metric string. | 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:
... |
Create a callback that prints the evaluation results. | 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)
Whether to show stdv (if provided).... |
Create a callback that records the evaluation history into eval_result. | 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... |
Create a callback that resets the parameter after the first iteration. | 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... |
Create a callback that activates 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_... |
Perform the training with given parameters. | def train(params, train_set, num_boost_round=100,
valid_sets=None, valid_names=None,
fobj=None, feval=None, init_model=None,
feature_name='auto', categorical_feature='auto',
early_stopping_rounds=None, evals_result=None,
verbose_eval=True, learning_rates=None,
... |
Make a n - fold list of Booster from random indices. | def _make_n_folds(full_data, folds, nfold, params, seed, fpreproc=None, stratified=True,
shuffle=True, eval_train_metric=False):
"""Make a n-fold list of Booster from random indices."""
full_data = full_data.construct()
num_data = full_data.num_data()
if folds is not None:
if n... |
Aggregate cross - validation results. | def _agg_cv_result(raw_results, eval_train_metric=False):
"""Aggregate cross-validation results."""
cvmap = collections.defaultdict(list)
metric_type = {}
for one_result in raw_results:
for one_line in one_result:
if eval_train_metric:
key = "{} {}".format(one_line[0]... |
Perform the cross - validation with given paramaters. | def cv(params, train_set, num_boost_round=100,
folds=None, nfold=5, stratified=True, shuffle=True,
metrics=None, fobj=None, feval=None, init_model=None,
feature_name='auto', categorical_feature='auto',
early_stopping_rounds=None, fpreproc=None,
verbose_eval=None, show_stdv=True, seed=... |
Logarithmic loss with non - necessarily - binary labels. | 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 |
Measure performance of an objective. | 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... |
Check object is not tuple or does not have 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) |
Plot model s feature importances. | 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,
... |
Plot one metric during training. | 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
----------
... |
Convert specified tree to graphviz instance. | 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... |
Create a digraph representation of specified tree. | 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... |
Plot specified tree. | def plot_tree(booster, ax=None, tree_index=0, figsize=None,
old_graph_attr=None, old_node_attr=None, old_edge_attr=None,
show_info=None, precision=None, **kwargs):
"""Plot specified tree.
Note
----
It is preferable to use ``create_tree_digraph()`` because of its lossless qua... |
Return the - std = c ++ [ 0x/ 11/ 14 ] compiler flag. The c ++ 14 is preferred over c ++ 0x/ 11 ( when it is available ). | 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:
if has_flag(compiler, [standard]):
return standard
raise ... |
query is a 1d numpy array corresponding to the vector to which you want to find the closest vector vectors is a 2d numpy array corresponding to the vectors you want to consider 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 ( vectors ) which can ... | def find_nearest_neighbor(query, vectors, ban_set, cossims=None):
"""
query is a 1d numpy array corresponding to the vector to which you want to
find the closest vector
vectors is a 2d numpy array corresponding to the vectors you want to consider
ban_set is a set of indicies within vectors you want ... |
Train a supervised model and return a model object. | 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... |
Get the vector representation of word. | 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) |
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. | 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 ... |
Given a word get the subwords and their indicies. | 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]) |
Given an index get the corresponding vector of the Input Matrix. | 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) |
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 labels by a threshold on probability. A choice... | 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
most probable labels. By default this returns onl... |
Get a copy of the full input matrix of a Model. This only works if the model is not quantized. | 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()) |
Get a copy of the full output matrix of a Model. This only works if the model is not quantized. | 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()) |
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. | 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.
"""
... |
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 for this type of model. | 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... |
Split a line of text into words and labels. Labels must start with the prefix used to create the model ( __label__ by default ). | def get_line(self, text, on_unicode_error='strict'):
"""
Split a line of text into words and labels. Labels must start with
the prefix used to create the model (__label__ by default).
"""
def check(entry):
if entry.find('\n') != -1:
raise ValueError(
... |
Quantize the model reducing the size of the model and it s memory footprint. | 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... |
Parameters ---------- 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 memory of the LSTM. Each tensor has shape ( num_layers bat... | def forward(self, # pylint: disable=arguments-differ
inputs: PackedSequence,
initial_state: Optional[Tuple[torch.Tensor, torch.Tensor]] = None
) -> Tuple[PackedSequence, Tuple[torch.Tensor, torch.Tensor]]:
"""
Parameters
----------
inputs :... |
Converts a List of pairs ( regex params ) into an RegularizerApplicator. This list should look like | def from_params(cls, params: Iterable[Tuple[str, Params]] = ()) -> Optional['RegularizerApplicator']:
"""
Converts a List of pairs (regex, params) into an RegularizerApplicator.
This list should look like
[["regex1", {"type": "l2", "alpha": 0.01}], ["regex2", "l1"]]
where each ... |
List default first if it exists | 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 ... |
Sanitize turns PyTorch and Numpy types into basic Python types so they can be serialized into JSON. | 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... |
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. | 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... |
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. | 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)), ... |
Take a list of objects and pads it to the desired length returning the padded list. The original list is not modified. | 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... |
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. | 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... |
Matches a namespace pattern against a namespace string. For example * tags matches passage_tags and question_tags and tokens matches tokens but not stemmed_tokens. | def namespace_match(pattern: str, namespace: str):
"""
Matches a namespace pattern against a namespace string. For example, ``*tags`` matches
``passage_tags`` and ``question_tags`` and ``tokens`` matches ``tokens`` but not
``stemmed_tokens``.
"""
if pattern[0] == '*' and namespace.endswith(patt... |
Sets random seeds for reproducible experiments. This may not work as expected if you use this from within a python project in which you have already imported Pytorch. If you use the scripts/ run_model. py entry point to training models with this library your experiments should be reasonably reproducible. If you are usi... | def prepare_environment(params: Params):
"""
Sets random seeds for reproducible experiments. This may not work as expected
if you use this from within a python project in which you have already imported Pytorch.
If you use the scripts/run_model.py entry point to training models with this library,
yo... |
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 frequency for the Tqdm progress bar. | 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... |
This function closes any open file handles and logs set up by prepare_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.
The file handler returned from `prepare_global_loggi... |
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 once. | 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... |
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. | 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()
... |
Get peak memory usage for this process as measured by max - resident - set size: | def peak_memory_mb() -> float:
"""
Get peak memory usage for this process, as measured by
max-resident-set size:
https://unix.stackexchange.com/questions/30940/getrusage-system-call-what-is-maximum-resident-set-size
Only works on OSX and Linux, returns 0.0 otherwise.
"""
if resource is Non... |
Get the current GPU memory usage. Based on https:// discuss. pytorch. org/ t/ access - gpu - memory - usage - in - pytorch/ 3192/ 4 | def gpu_memory_mb() -> Dict[int, int]:
"""
Get the current GPU memory usage.
Based on https://discuss.pytorch.org/t/access-gpu-memory-usage-in-pytorch/3192/4
Returns
-------
``Dict[int, int]``
Keys are device ids as integers.
Values are memory usage as integers in MB.
Re... |
An Iterable may be a list or a generator. This ensures we get a list without making an unnecessary copy. | 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) |
Takes an action index updates checklist and returns an updated state. | 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... |
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. | 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
list, and r... |
Parameters ---------- inputs: torch. FloatTensor required. A tensor of shape ( batch_size num_timesteps input_size ) to apply the LSTM over. batch_lengths: List [ int ] required. A list of length batch_size containing the lengths of the sequences in batch. initial_state: Tuple [ torch. Tensor torch. Tensor ] optional (... | def forward(self, # pylint: disable=arguments-differ
inputs: torch.FloatTensor,
batch_lengths: List[int],
initial_state: Optional[Tuple[torch.Tensor, torch.Tensor]] = None):
"""
Parameters
----------
inputs : ``torch.FloatTensor``, require... |
Determine the URL corresponding to Python object This code is from https:// github. com/ numpy/ numpy/ blob/ master/ doc/ source/ conf. py#L290 and https:// github. com/ Lasagne/ Lasagne/ pull/ 262 | def linkcode_resolve(domain, info):
"""
Determine the URL corresponding to Python object
This code is from
https://github.com/numpy/numpy/blob/master/doc/source/conf.py#L290
and https://github.com/Lasagne/Lasagne/pull/262
"""
if domain != 'py':
return None
modname = info['module... |
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. | def _get_initial_rnn_and_grammar_state(self,
question: Dict[str, torch.LongTensor],
table: Dict[str, torch.LongTensor],
world: List[WikiTablesWorld],
... |
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