Python库 | crf_tagger-0.3.1.tar.gz

from tqdm import tqdm import pycrfsuite from crf_tagger._fileresource import FileResource from crf_tagger.trainer import LinePerIterationTrainer from crf_tagger.compat import BaseEstimator class CRF(BaseEstimator): """ python-crfsuite wrapper with interface siimlar to scikit-learn. It allows to use a familiar fit/predict interface and scikit-learn model selection utilities (cross-validation, hyperparameter optimization). Unlike pycrfsuite.Trainer / pycrfsuite.Tagger this object is picklable; on-disk files are managed automatically. Parameters ---------- algorithm : str, optional (default='lbfgs') Training algorithm. Allowed values: * ``'lbfgs'`` - Gradient descent using the L-BFGS method * ``'l2sgd'`` - Stochastic Gradient Descent with L2 regularization term * ``'ap'`` - Averaged Perceptron * ``'pa'`` - Passive Aggressive (PA) * ``'arow'`` - Adaptive Regularization Of Weight Vector (AROW) min_freq : float, optional (default=0) Cut-off threshold for occurrence frequency of a feature. CRFsuite will ignore features whose frequencies of occurrences in the training data are no greater than `min_freq`. The default is no cut-off. all_possible_states : bool, optional (default=False) Specify whether CRFsuite generates state features that do not even occur in the training data (i.e., negative state features). When True, CRFsuite generates state features that associate all of possible combinations between attributes and labels. Suppose that the numbers of attributes and labels are A and L respectively, this function will generate (A * L) features. Enabling this function may improve the labeling accuracy because the CRF model can learn the condition where an item is not predicted to its reference label. However, this function may also increase the number of features and slow down the training process drastically. This function is disabled by default. all_possible_transitions : bool, optional (default=False) Specify whether CRFsuite generates transition features that do not even occur in the training data (i.e., negative transition features). When True, CRFsuite generates transition features that associate all of possible label pairs. Suppose that the number of labels in the training data is L, this function will generate (L * L) transition features. This function is disabled by default. c1 : float, optional (default=0) The coefficient for L1 regularization. If a non-zero value is specified, CRFsuite switches to the Orthant-Wise Limited-memory Quasi-Newton (OWL-QN) method. The default value is zero (no L1 regularization). Supported training algorithms: lbfgs c2 : float, optional (default=1.0) The coefficient for L2 regularization. Supported training algorithms: l2sgd, lbfgs max_iterations : int, optional (default=None) The maximum number of iterations for optimization algorithms. Default value depends on training algorithm: * lbfgs - unlimited; * l2sgd - 1000; * ap - 100; * pa - 100; * arow - 100. num_memories : int, optional (default=6) The number of limited memories for approximating the inverse hessian matrix. Supported training algorithms: lbfgs epsilon : float, optional (default=1e-5) The epsilon parameter that determines the condition of convergence. Supported training algorithms: ap, arow, lbfgs, pa period : int, optional (default=10) The duration of iterations to test the stopping criterion. Supported training algorithms: l2sgd, lbfgs delta : float, optional (default=1e-5) The threshold for the stopping criterion; an iteration stops when the improvement of the log likelihood over the last `period` iterations is no greater than this threshold. Supported training algorithms: l2sgd, lbfgs linesearch : str, optional (default='MoreThuente') The line search algorithm used in L-BFGS updates. Allowed values: * ``'MoreThuente'`` - More and Thuente's method; * ``'Backtracking'`` - backtracking method with regular Wolfe condition; * ``'StrongBacktracking'`` - backtracking method with strong Wolfe condition. Supported training algorithms: lbfgs max_linesearch : int, optional (default=20) The maximum number of trials for the line search algorithm. Supported training algorithms: lbfgs calibration_eta : float, optional (default=0.1) The initial value of learning rate (eta) used for calibration. Supported training algorithms: l2sgd calibration_rate : float, optional (default=2.0) The rate of increase/decrease of learning rate for calibration. Supported training algorithms: l2sgd calibration_samples : int, optional (default=1000) The number of instances used for calibration. The calibration routine randomly chooses instances no larger than `calibration_samples`. Supported training algorithms: l2sgd calibration_candidates : int, optional (default=10) The number of candidates of learning rate. The calibration routine terminates after finding `calibration_samples` candidates of learning rates that can increase log-likelihood. Supported training algorithms: l2sgd calibration_max_trials : int, optional (default=20) The maximum number of trials of learning rates for calibration. The calibration routine terminates after trying `calibration_max_trials` candidate values of learning rates. Supported training algorithms: l2sgd pa_type : int, optional (default=1) The strategy for updating feature weights. Allowed values: * 0 - PA without slack variables; * 1 - PA type I; * 2 - PA type II. Supported training algorithms: pa c : float, optional (default=1) Aggressiveness parameter (used only for PA-I and PA-II). This parameter controls the influence of the slack term on the objective function. Supported training algorithms: pa error_sensitive : bool, optional (default=True) If this parameter is True, the optimization routine includes into the objective function the square root of the number of incorrect labels predicted by the model. Supported training algorithms: pa averaging : bool, optional (default=True) If this parameter is True, the optimization routine computes the average of feature weights at all updates in the training process (similarly to Averaged Perceptron). Supported training algorithms: pa variance : float, optional (default=1) The initial variance of every feature weight. The algorithm initialize a vector of feature weights as a multivariate Gaussian distribution with mean 0 and variance `variance`. Supported training algorithms: arow gamma : float, optional (default=1) The tradeoff between loss function and changes of feature weights. Supported training algorithms: arow verbose : bool, optional (default=False) Enable trainer verbose mode. model_filename : str, optional (default=None) A path to an existing CRFSuite model. This parameter allows to load and use existing crfsuite models. By default, model files are created automatically and saved in temporary locations; the preferred way to save/load CRF models is to use pickle (or its alternatives like joblib). """ def __init__( self, algorithm=None,