BoghdadyJR/codesearch_python · Datasets at Hugging Face

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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('Expected int pointer')
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_numpy_1d_array(val): pairs.append(str(key) + '=' + ',...
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("Usage of np.ndarray subset (sliced data) is not recom...
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.dtype == np.float32: ptr_data ...
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 == np.int32: ptr_data = data...
def predict(self, data, num_iteration=-1, raw_score=False, pred_leaf=False, pred_contrib=False, data_has_header=False, is_reshape=True): """Predict logic. Parameters ---------- data : string, numpy array, pandas DataFrame, H2O DataTable's Frame or scipy.s...
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_INT32 (%d).\n' ...
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=None): if mat.dtype ...
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) if preds is None: ...
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, predict_type) pr...
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.dtype == np.float32 or mat.dtype == np.float6...
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_data = (ctypes.POINTER(ctypes.c_double) * len(mats))() ...
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_void_p() ptr_indptr, type_ptr_...
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_void_p() ptr_indptr, type_ptr_...
def construct(self): """Lazy init. Returns ------- self : Dataset Constructed Dataset object. """ if self.handle is None: if self.reference is not None: if self.used_indices is None: # create valid ...
def create_valid(self, data, label=None, weight=None, group=None, init_score=None, silent=False, params=None): """Create validation data align with current Dataset. Parameters ---------- data : string, numpy array, pandas DataFrame, H2O DataTable's Frame, scipy.spar...
def subset(self, used_indices, params=None): """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 co...
def save_binary(self, filename): """Save Dataset to a binary file. Parameters ---------- filename : string Name of the output file. Returns ------- self : Dataset Returns self. """ _safe_call(_LIB.LGBM_DatasetSaveBinary( ...
def set_field(self, field_name, data): """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 ...
def get_field(self, field_name): """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 set_categorical_feature(self, categorical_feature): """Set categorical features. Parameters ---------- categorical_feature : list of int or strings Names or indices of categorical features. Returns ------- self : Dataset Dataset with ...
def _set_predictor(self, predictor): """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. """ if predictor is self._predictor: return self if se...
def set_reference(self, reference): """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_feature_name(self, feature_name): """Set feature name. Parameters ---------- feature_name : list of strings Feature names. Returns ------- self : Dataset Dataset with set feature name. """ if feature_name != 'auto'...
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_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. ...
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_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 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
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_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_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_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_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_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...
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_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...
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 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 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 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
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...
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. ...
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...
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...
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...
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...
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, ...
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....
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...
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_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...
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 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 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_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...
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...
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 ...
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 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 _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 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...
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_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 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 __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: ...
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: ...
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( ...
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 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 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
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: ...
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)....
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...
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...
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_...
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, ...
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...
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]...
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=...
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 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...
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)
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, ...
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 _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...
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 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...
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 ...
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 ...
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...
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_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_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_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 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...
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_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())
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. """ ...
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...

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('Expected int pointer')

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_numpy_1d_array(val): pairs.append(str(key) + '=' + ',...

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("Usage of np.ndarray subset (sliced data) is not recom...

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.dtype == np.float32: ptr_data ...

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 == np.int32: ptr_data = data...

def predict(self, data, num_iteration=-1, raw_score=False, pred_leaf=False, pred_contrib=False, data_has_header=False, is_reshape=True): """Predict logic. Parameters ---------- data : string, numpy array, pandas DataFrame, H2O DataTable's Frame or scipy.s...

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_INT32 (%d).\n' ...

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=None): if mat.dtype ...

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) if preds is None: ...

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, predict_type) pr...

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.dtype == np.float32 or mat.dtype == np.float6...

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_data = (ctypes.POINTER(ctypes.c_double) * len(mats))() ...

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_void_p() ptr_indptr, type_ptr_...

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_void_p() ptr_indptr, type_ptr_...

def construct(self): """Lazy init. Returns ------- self : Dataset Constructed Dataset object. """ if self.handle is None: if self.reference is not None: if self.used_indices is None: # create valid ...

def create_valid(self, data, label=None, weight=None, group=None, init_score=None, silent=False, params=None): """Create validation data align with current Dataset. Parameters ---------- data : string, numpy array, pandas DataFrame, H2O DataTable's Frame, scipy.spar...

def subset(self, used_indices, params=None): """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 co...

def save_binary(self, filename): """Save Dataset to a binary file. Parameters ---------- filename : string Name of the output file. Returns ------- self : Dataset Returns self. """ _safe_call(_LIB.LGBM_DatasetSaveBinary( ...

def set_field(self, field_name, data): """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 ...

def get_field(self, field_name): """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 set_categorical_feature(self, categorical_feature): """Set categorical features. Parameters ---------- categorical_feature : list of int or strings Names or indices of categorical features. Returns ------- self : Dataset Dataset with ...

def _set_predictor(self, predictor): """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. """ if predictor is self._predictor: return self if se...

def set_reference(self, reference): """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_feature_name(self, feature_name): """Set feature name. Parameters ---------- feature_name : list of strings Feature names. Returns ------- self : Dataset Dataset with set feature name. """ if feature_name != 'auto'...

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_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. ...

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_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 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

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_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_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_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_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_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...

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_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...

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 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 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 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

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...

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. ...

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...

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...

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...

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...

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, ...

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....

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...

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_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...

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 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 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_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...

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...

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 ...

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 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 _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 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...

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_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 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 __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: ...

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: ...

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( ...

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 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 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

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: ...

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)....

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...

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...

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_...

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, ...

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...

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]...

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=...

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 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...

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)

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, ...

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 _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...

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 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...

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 ...

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 ...

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...

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_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_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_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 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...

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_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())

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. """ ...

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...