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Convert a ctypes int pointer array to a numpy array.
def cint8_array_to_numpy(cptr, length):
"""Convert a ctypes int pointer array to a numpy array."""
if isinstance(cptr, ctypes.POINTER(ctypes.c_int8)):
return np.fromiter(cptr, dtype=np.int8, count=length)
else:
raise RuntimeError('Expecte... |
Convert Python dictionary to string, which is passed to C API.
def param_dict_to_str(data):
"""Convert Python dictionary to string, which is passed to C API."""
if data is None or not data:
return ""
pairs = []
for key, val in data.items():
if isinstance(val, (list, tuple, set)) or is_n... |
Fix the memory of multi-dimensional sliced object.
def convert_from_sliced_object(data):
"""Fix the memory of multi-dimensional sliced object."""
if data.base is not None and isinstance(data, np.ndarray) and isinstance(data.base, np.ndarray):
if not data.flags.c_contiguous:
warnings.warn("U... |
Get pointer of float numpy array / list.
def c_float_array(data):
"""Get pointer of float numpy array / list."""
if is_1d_list(data):
data = np.array(data, copy=False)
if is_numpy_1d_array(data):
data = convert_from_sliced_object(data)
assert data.flags.c_contiguous
if data.... |
Get pointer of int numpy array / list.
def c_int_array(data):
"""Get pointer of int numpy array / list."""
if is_1d_list(data):
data = np.array(data, copy=False)
if is_numpy_1d_array(data):
data = convert_from_sliced_object(data)
assert data.flags.c_contiguous
if data.dtype ... |
Predict logic.
Parameters
----------
data : string, numpy array, pandas DataFrame, H2O DataTable's Frame or scipy.sparse
Data source for prediction.
When data type is string, it represents the path of txt file.
num_iteration : int, optional (default=-1)
... |
Get size of prediction result.
def __get_num_preds(self, num_iteration, nrow, predict_type):
"""Get size of prediction result."""
if nrow > MAX_INT32:
raise LightGBMError('LightGBM cannot perform prediction for data'
'with number of rows greater than MAX_INT3... |
Predict for a 2-D numpy matrix.
def __pred_for_np2d(self, mat, num_iteration, predict_type):
"""Predict for a 2-D numpy matrix."""
if len(mat.shape) != 2:
raise ValueError('Input numpy.ndarray or list must be 2 dimensional')
def inner_predict(mat, num_iteration, predict_type, preds... |
Predict for a CSR data.
def __pred_for_csr(self, csr, num_iteration, predict_type):
"""Predict for a CSR data."""
def inner_predict(csr, num_iteration, predict_type, preds=None):
nrow = len(csr.indptr) - 1
n_preds = self.__get_num_preds(num_iteration, nrow, predict_type)
... |
Predict for a CSC data.
def __pred_for_csc(self, csc, num_iteration, predict_type):
"""Predict for a CSC data."""
nrow = csc.shape[0]
if nrow > MAX_INT32:
return self.__pred_for_csr(csc.tocsr(), num_iteration, predict_type)
n_preds = self.__get_num_preds(num_iteration, nrow,... |
Initialize data from a 2-D numpy matrix.
def __init_from_np2d(self, mat, params_str, ref_dataset):
"""Initialize data from a 2-D numpy matrix."""
if len(mat.shape) != 2:
raise ValueError('Input numpy.ndarray must be 2 dimensional')
self.handle = ctypes.c_void_p()
if mat.dty... |
Initialize data from a list of 2-D numpy matrices.
def __init_from_list_np2d(self, mats, params_str, ref_dataset):
"""Initialize data from a list of 2-D numpy matrices."""
ncol = mats[0].shape[1]
nrow = np.zeros((len(mats),), np.int32)
if mats[0].dtype == np.float64:
ptr_dat... |
Initialize data from a CSR matrix.
def __init_from_csr(self, csr, params_str, ref_dataset):
"""Initialize data from a CSR matrix."""
if len(csr.indices) != len(csr.data):
raise ValueError('Length mismatch: {} vs {}'.format(len(csr.indices), len(csr.data)))
self.handle = ctypes.c_voi... |
Initialize data from a CSC matrix.
def __init_from_csc(self, csc, params_str, ref_dataset):
"""Initialize data from a CSC matrix."""
if len(csc.indices) != len(csc.data):
raise ValueError('Length mismatch: {} vs {}'.format(len(csc.indices), len(csc.data)))
self.handle = ctypes.c_voi... |
Lazy init.
Returns
-------
self : Dataset
Constructed Dataset object.
def construct(self):
"""Lazy init.
Returns
-------
self : Dataset
Constructed Dataset object.
"""
if self.handle is None:
if self.reference... |
Create validation data align with current Dataset.
Parameters
----------
data : string, numpy array, pandas DataFrame, H2O DataTable's Frame, scipy.sparse or list of numpy arrays
Data source of Dataset.
If string, it represents the path to txt file.
label : list,... |
Get subset of current Dataset.
Parameters
----------
used_indices : list of int
Indices used to create the subset.
params : dict or None, optional (default=None)
These parameters will be passed to Dataset constructor.
Returns
-------
subs... |
Save Dataset to a binary file.
Parameters
----------
filename : string
Name of the output file.
Returns
-------
self : Dataset
Returns self.
def save_binary(self, filename):
"""Save Dataset to a binary file.
Parameters
-... |
Set property into the Dataset.
Parameters
----------
field_name : string
The field name of the information.
data : list, numpy 1-D array, pandas Series or None
The array of data to be set.
Returns
-------
self : Dataset
Datase... |
Get property from the Dataset.
Parameters
----------
field_name : string
The field name of the information.
Returns
-------
info : numpy array
A numpy array with information from the Dataset.
def get_field(self, field_name):
"""Get prope... |
Set categorical features.
Parameters
----------
categorical_feature : list of int or strings
Names or indices of categorical features.
Returns
-------
self : Dataset
Dataset with set categorical features.
def set_categorical_feature(self, catego... |
Set predictor for continued training.
It is not recommended for user to call this function.
Please use init_model argument in engine.train() or engine.cv() instead.
def _set_predictor(self, predictor):
"""Set predictor for continued training.
It is not recommended for user to call thi... |
Set reference Dataset.
Parameters
----------
reference : Dataset
Reference that is used as a template to construct the current Dataset.
Returns
-------
self : Dataset
Dataset with set reference.
def set_reference(self, reference):
"""Set... |
Set feature name.
Parameters
----------
feature_name : list of strings
Feature names.
Returns
-------
self : Dataset
Dataset with set feature name.
def set_feature_name(self, feature_name):
"""Set feature name.
Parameters
... |
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.
def set_label(self, labe... |
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.
def set_weight(self, weight):
"""Set w... |
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.
def set_init_score(self, init_score):
... |
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 ... |
Get the label of the Dataset.
Returns
-------
label : numpy array or None
The label information from the Dataset.
def get_label(self):
"""Get the label of the Dataset.
Returns
-------
label : numpy array or None
The label information fro... |
Get the weight of the Dataset.
Returns
-------
weight : numpy array or None
Weight for each data point from the Dataset.
def get_weight(self):
"""Get the weight of the Dataset.
Returns
-------
weight : numpy array or None
Weight for each... |
Get the feature penalty of the Dataset.
Returns
-------
feature_penalty : numpy array or None
Feature penalty for each feature in the Dataset.
def get_feature_penalty(self):
"""Get the feature penalty of the Dataset.
Returns
-------
feature_penalty ... |
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.
def get_monotone_constraints(self):
"""Get the monotone constraints of the Dataset.
Returns
... |
Get the initial score of the Dataset.
Returns
-------
init_score : numpy array or None
Init score of Booster.
def get_init_score(self):
"""Get the initial score of the Dataset.
Returns
-------
init_score : numpy array or None
Init score ... |
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.
def get_data(self):
"""Get the raw data of the Dataset.
Retur... |
Get the group of the Dataset.
Returns
-------
group : numpy array or None
Group size of each group.
def get_group(self):
"""Get the group of the Dataset.
Returns
-------
group : numpy array or None
Group size of each group.
"""
... |
Get the number of rows in the Dataset.
Returns
-------
number_of_rows : int
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 D... |
Get the number of columns (features) in the Dataset.
Returns
-------
number_of_columns : int
The number of columns (features) in the Dataset.
def num_feature(self):
"""Get the number of columns (features) in the Dataset.
Returns
-------
number_of_co... |
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... |
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... |
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.
def dump_... |
Free Booster's Datasets.
Returns
-------
self : Booster
Booster without Datasets.
def free_dataset(self):
"""Free Booster's Datasets.
Returns
-------
self : Booster
Booster without Datasets.
"""
self.__dict__.pop('train_s... |
Set the network configuration.
Parameters
----------
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)
Socket t... |
Add validation data.
Parameters
----------
data : Dataset
Validation data.
name : string
Name of validation data.
Returns
-------
self : Booster
Booster with set validation data.
def add_valid(self, data, name):
"""Ad... |
Reset parameters of Booster.
Parameters
----------
params : dict
New parameters for Booster.
Returns
-------
self : Booster
Booster with new parameters.
def reset_parameter(self, params):
"""Reset parameters of Booster.
Paramete... |
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=None)
Customized objective function.
... |
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_data + i]
and you should group grad a... |
Rollback one iteration.
Returns
-------
self : Booster
Booster with rolled back one iteration.
def rollback_one_iter(self):
"""Rollback one iteration.
Returns
-------
self : Booster
Booster with rolled back one iteration.
"""
... |
Get the index of the current iteration.
Returns
-------
cur_iter : int
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 it... |
Get number of models per iteration.
Returns
-------
model_per_iter : int
The 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 mod... |
Get number of weak sub-models.
Returns
-------
num_trees : int
The 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_t... |
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,... |
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_result, is_higher_better) or list of such tuples... |
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... |
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... |
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
... |
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; otherwise, all iterations are saved.
If <= 0, all iterations ar... |
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 dumped; otherwise, all iterations are dumped.
If <= 0, all itera... |
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)
... |
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 to txt file.
label : list, numpy 1-D array or pandas Series ... |
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.
def get_leaf_output(self, tree_id, l... |
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.
Returns
-------
num_feature : int
The number of features.
def num_feature(self):
"""Get number of features.
Returns
-------
num_feature : int
The number of features.
"""
out_num_feature = ctypes.c_... |
Get names of features.
Returns
-------
result : list
List with names of features.
def feature_name(self):
"""Get names of features.
Returns
-------
result : list
List with names of features.
"""
num_feature = self.num_fea... |
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... |
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
----
... |
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 = []... |
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 N... |
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... |
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):
"""S... |
Find the path to LightGBM library files.
Returns
-------
lib_path: list of strings
List of all found library paths to LightGBM.
def find_lib_path():
"""Find the path to LightGBM library files.
Returns
-------
lib_path: list of strings
List of all found library paths to Light... |
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:
... |
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])
... |
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).
Returns
-------
callback : function
... |
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 evaluation history into the passed dictionary.
de... |
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 boosting round
or a customized func... |
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_rounds`` round(s)
to continue training.
Requires at least one validation da... |
Perform the training with given parameters.
Parameters
----------
params : dict
Parameters for training.
train_set : Dataset
Data to be trained on.
num_boost_round : int, optional (default=100)
Number of boosting iterations.
valid_sets : list of Datasets or None, optiona... |
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_da... |
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:
... |
Perform the cross-validation with given paramaters.
Parameters
----------
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... |
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.
Parameters
----------
objective : string 'binary' or 'xentropy'
Objective function.
label_type : string 'binary' or 'probability'
Type of the label.
data : dict
Data for training.
Returns
-------
result : dict
Experim... |
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.
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 ... |
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... |
Convert specified tree to graphviz instance.
See:
- https://graphviz.readthedocs.io/en/stable/api.html#digraph
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.... |
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... |
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
----------
booster : Booster or LGBMModel
Booster or LGBMModel ins... |
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']
... |
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(vector... |
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
as tokenizer.perl mentioned here: http... |
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 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 n... |
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... |
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 ValueE... |
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 Val... |
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 entir... |
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=... |
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