Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance. Project price only 1 $
You can buy this project and download/modify it how often you want.
/*******************************************************************************
* Copyright 2018
* Ubiquitous Knowledge Processing (UKP) Lab
* Technische Universität Darmstadt
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
******************************************************************************/
package org.dkpro.tc.ml.crfsuite;
import java.util.Collection;
import org.dkpro.lab.reporting.ReportBase;
import org.dkpro.lab.task.Dimension;
import org.dkpro.lab.task.impl.DimensionBundle;
import org.dkpro.lab.task.impl.ExecutableTaskBase;
import org.dkpro.lab.task.impl.FoldDimensionBundle;
import org.dkpro.tc.core.ml.ModelSerialization_ImplBase;
import org.dkpro.tc.core.ml.TcShallowLearningAdapter;
import org.dkpro.tc.core.task.ModelSerializationTask;
import org.dkpro.tc.ml.crfsuite.reports.CrfSuiteBaselineMajorityClassIdReport;
import org.dkpro.tc.ml.crfsuite.reports.CrfSuiteBaselineRandomIdReport;
import org.dkpro.tc.ml.crfsuite.reports.CrfSuiteOutcomeIDReport;
import org.dkpro.tc.ml.crfsuite.task.serialization.CrfSuiteLoadModelConnector;
import org.dkpro.tc.ml.crfsuite.task.serialization.CrfSuiteSerializeModelConnector;
import org.dkpro.tc.ml.crfsuite.writer.CrfSuiteDataWriter;
/**
* CRFSuite machine learning adapter. Details about available algorithm and their configuration is
* provided in this class see constants prefixed with ALGORITHM.
*/
public class CrfSuiteAdapter
implements TcShallowLearningAdapter
{
public static TcShallowLearningAdapter getInstance()
{
return new CrfSuiteAdapter();
}
@Override
public ExecutableTaskBase getTestTask()
{
return new CrfSuiteTestTask();
}
@Override
public Class getOutcomeIdReportClass()
{
return CrfSuiteOutcomeIDReport.class;
}
@Override
public Class getMajorityClassBaselineIdReportClass()
{
return CrfSuiteBaselineMajorityClassIdReport.class;
}
@Override
public Class getRandomBaselineIdReportClass()
{
return CrfSuiteBaselineRandomIdReport.class;
}
@SuppressWarnings("unchecked")
@Override
public DimensionBundle> getFoldDimensionBundle(String[] files, int folds)
{
return new FoldDimensionBundle("files", Dimension.create("", files), folds);
}
@Override
public String getDataWriterClass()
{
return CrfSuiteDataWriter.class.getName();
}
@Override
public Class getLoadModelConnectorClass()
{
return CrfSuiteLoadModelConnector.class;
}
@Override
public ModelSerializationTask getSaveModelTask()
{
return new CrfSuiteSerializeModelConnector();
}
/**
* Maximize the logarithm of the likelihood of the training data with L1 and/or L2
* regularization term(s) using the Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS)
* method. When a non-zero coefficient for L1 regularization term is specified, the algorithm
* switches to the Orthant-Wise Limited-memory Quasi-Newton (OWL-QN) method. In practice, this
* algorithm improves feature weights very slowly at the beginning of a training process, but
* converges to the optimal feature weights quickly in the end.
*
*
float feature.minfreq = 0.000000;
The minimum frequency of features.
int feature.possible_states = 0;
Force to generate possible state features.
int feature.possible_transitions = 0;
Force to generate possible transition features.
float c1 = 0.000000;
Coefficient for L1 regularization.
float c2 = 1.000000;
Coefficient for L2 regularization.
int max_iterations = 2147483647;
The maximum number of iterations for L-BFGS optimization.
int num_memories = 6;
The number of limited memories for approximating the inverse hessian matrix.
float epsilon = 0.000010;
Epsilon for testing the convergence of the objective.
int period = 10;
The duration of iterations to test the stopping criterion.
float delta = 0.000010;
The threshold for the stopping criterion; an L-BFGS iteration stops when the
improvement of the log likelihood over the last ${period} iterations is no
greater than this threshold.
string linesearch = MoreThuente;
The line search algorithm used in L-BFGS updates:
{ 'MoreThuente': More and Thuente's method,
'Backtracking': Backtracking method with regular Wolfe condition,
'StrongBacktracking': Backtracking method with strong Wolfe condition
}
int max_linesearch = 20;
The maximum number of trials for the line search algorithm.
*
*
*/
public static final String ALGORITHM_LBFGS = "lbfgs";
/**
* Given an item sequence (x, y) in the training data, the algorithm computes the loss: s(x, y')
* - s(x, y), where s(x, y') is the score of the Viterbi label sequence, and s(x, y) is the
* score of the label sequence of the training data.
*
*
float feature.minfreq = 0.000000;
The minimum frequency of features.
int feature.possible_states = 0;
Force to generate possible state features.
int feature.possible_transitions = 0;
Force to generate possible transition features.
float variance = 1.000000;
The initial variance of every feature weight.
float gamma = 1.000000;
Tradeoff parameter.
int max_iterations = 100;
The maximum number of iterations.
float epsilon = 0.000000;
The stopping criterion (the mean loss).
*
*/
public static final String ALGORITHM_ADAPTIVE_REGULARIZATION_OF_WEIGHT_VECTOR = "arow";
/**
* Given an item sequence (x, y) in the training data, the algorithm computes the loss: s(x, y')
* - s(x, y) + sqrt(d(y', y)), where s(x, y') is the score of the Viterbi label sequence, s(x,
* y) is the score of the label sequence of the training data, and d(y', y) measures the
* distance between the Viterbi label sequence (y') and the reference label sequence (y). If the
* item suffers from a non-negative loss, the algorithm updates the model based on the loss.
*
*
float feature.minfreq = 0.000000;
The minimum frequency of features.
int feature.possible_states = 0;
Force to generate possible state features.
int feature.possible_transitions = 0;
Force to generate possible transition features.
int max_iterations = 100;
The maximum number of iterations.
float epsilon = 0.000000;
The stopping criterion (the ratio of incorrect label predictions).
*
*/
public static final String ALGORITHM_AVERAGED_PERCEPTRON = "ap";
/**
* Maximize the logarithm of the likelihood of the training data with L2 regularization term(s)
* using Stochastic Gradient Descent (SGD) with batch size 1. This algorithm usually approaches
* to the optimal feature weights quite rapidly, but shows slow convergences at the end.
*
*
float feature.minfreq = 0.000000;
The minimum frequency of features.
int feature.possible_states = 0;
Force to generate possible state features.
int feature.possible_transitions = 0;
Force to generate possible transition features.
float c2 = 1.000000;
Coefficient for L2 regularization.
int max_iterations = 1000;
The maximum number of iterations (epochs) for SGD optimization.
int period = 10;
The duration of iterations to test the stopping criterion.
float delta = 0.000001;
The threshold for the stopping criterion; an optimization process stops when
the improvement of the log likelihood over the last ${period} iterations is no
greater than this threshold.
float calibration.eta = 0.100000;
The initial value of learning rate (eta) used for calibration.
float calibration.rate = 2.000000;
The rate of increase/decrease of learning rate for calibration.
int calibration.samples = 1000;
The number of instances used for calibration.
int calibration.candidates = 10;
The number of candidates of learning rate.
int calibration.max_trials = 20;
The maximum number of trials of learning rates for calibration.
*
*/
public static final String ALGORITHM_L2_STOCHASTIC_GRADIENT_DESCENT = "l2sgd";
@Override
public boolean useSparseFeatures()
{
return true;
}
@Override
public String toString()
{
return getClass().getSimpleName();
}
}
