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b/.github/workflows/gradle.yml new file mode 100644 index 0000000..3c48f59 --- /dev/null +++ b/.github/workflows/gradle.yml @@ -0,0 +1,47 @@ +name: CI + +on: + push: + branches: [ main ] + pull_request: + branches: [ main ] + +jobs: + build: + runs-on: ubuntu-latest + strategy: + matrix: + java: [ '11', '17', '21', '23' ] + + steps: + - uses: actions/checkout@v2 + - name: Setup Java + uses: actions/setup-java@v1 + with: + java-version: ${{ matrix.java }} + - name: Build with Gradle + run: ./gradlew build + + validateGradleWrapper: + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v2 + - uses: gradle/wrapper-validation-action@v1.0.4 + + publishCoverage: + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v2 + - name: Setup Java + uses: actions/setup-java@v1 + with: + java-version: 11 + - name: Build with Gradle + run: ./gradlew jacocoTestReport + - name: Upload coverage to Codecov + uses: codecov/codecov-action@v1 + with: + files: ./build/reports/jacoco/test/jacocoTestReport.xml + name: codecov diff --git a/.gitignore b/.gitignore index 8e29992..d055963 100644 --- a/.gitignore +++ b/.gitignore @@ -1,9 +1,17 @@ .* +!.github/ *.class *.o +*.iml + !.gitignore !.travis.yml +!.editorconfig + +build/ +out/ +classes/ +data/ -/target -/data +src/test/datasets/ diff --git a/.travis.yml b/.travis.yml deleted file mode 100644 index fbb65fe..0000000 --- a/.travis.yml +++ /dev/null @@ -1,4 +0,0 @@ -language: java -jdk: - - oraclejdk7 - - openjdk6 diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..fb6f88b --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,11 @@ +## Contributing ## + +We are happy to receive pull-requests if you want to contribute new features or bugfixes to the project. +Please use the code style settings that you can find in the [code-style directory](code-style/). +If you use IntelliJ, please make sure it properly picks up the settings from the [EditorConfig file](.editorconfig) +(this should be the default behavior though). + +If you want to thank the author for this library or want to support the maintenance work, +we are happy to receive a donation. + +[![Donate](https://img.shields.io/badge/Donate-PayPal-green.svg)](https://www.paypal.me/BenediktWaldvogel) diff --git a/COPYRIGHT b/LICENSE similarity index 96% rename from COPYRIGHT rename to LICENSE index 7484947..73dd579 100644 --- a/COPYRIGHT +++ b/LICENSE @@ -1,5 +1,5 @@ -Copyright (c) 2007-2013 The LIBLINEAR Project. +Copyright (c) 2007-2022 The LIBLINEAR Project. All rights reserved. Redistribution and use in source and binary forms, with or without diff --git a/README.md b/README.md index cbfe3cb..b564d18 100644 --- a/README.md +++ b/README.md @@ -1,45 +1,83 @@ -[![Build Status](https://travis-ci.org/bwaldvogel/liblinear-java.png?branch=master)](https://travis-ci.org/bwaldvogel/liblinear-java) +[![CI](https://github.com/bwaldvogel/liblinear-java/workflows/CI/badge.svg)](https://github.com/bwaldvogel/liblinear-java/actions) +[![Maven Central](https://maven-badges.herokuapp.com/maven-central/de.bwaldvogel/liblinear/badge.svg)](http://maven-badges.herokuapp.com/maven-central/de.bwaldvogel/liblinear) +[![codecov](https://codecov.io/gh/bwaldvogel/liblinear-java/branch/main/graph/badge.svg?token=Scy4yJzBf7)](https://codecov.io/gh/bwaldvogel/liblinear-java) +[![BSD 3-Clause License](https://img.shields.io/github/license/bwaldvogel/liblinear-java.svg)](https://opensource.org/licenses/BSD-3-Clause) +[![Donate](https://img.shields.io/badge/Donate-PayPal-green.svg)](https://www.paypal.me/BenediktWaldvogel) This is the Java version of LIBLINEAR. -The project site of the original (C++) version is located at: +The project site of the original `C++` version is located at http://www.csie.ntu.edu.tw/~cjlin/liblinear/ -The upstream changelog can be found at: +The upstream changelog can be found at http://www.csie.ntu.edu.tw/~cjlin/liblinear/log -Please be aware that the code would be written differently at various places, -if it would be a pure Java project (Java coding style, less use of statics, smaller classes and methods etc.). +The upstream GitHub project can be found at + https://github.com/cjlin1/liblinear + +## Dependencies ## + +The only requirement is Java 11 or later. + +## Usage ## + +```xml + + de.bwaldvogel + liblinear + 2.44 + +``` + +Please be aware that the code would be written differently at various places, i.e. + +- Java coding style, +- less static functions and state, +- smaller classes and methods, + +if it would be a pure Java project. However, I tried to stick as close as possible to the original C++ source code for the following reasons: - **Maintainability**: - Patches for the original C++ version can be often applied easily + Patches for the original C++ version can often be applied easily - **Probability of translation errors**: Sticking to the original source code makes it less likely to introduce new bugs that are caused by porting to Java. - **Code Reviews**: - Should be more easy since it can be compared to the original version. + It should be easier to conduct code reviews since the sources can be compared to the original version. -Below follow the contents of the original README file. +Below follows a slightly modified version of the original README file. Please note that the README refers to the C++ version. -However, as already stated above, the Java version is almost identical to use. -The two most important methods that you might be interested in are: +As afore mentioned, the Java version is almost identical to use. + +The three most important methods for programmatic usage that you might be interested in are: - `Linear.train(…)` - `Linear.predict(…)` +- `Linear.predictProbability(…)` + +## Contributing ## + +Please read the [contributing guidelines](CONTRIBUTING.md) if you want to contribute code to the project. + +If you want to thank the author for this library or want to support the maintenance work, +we are happy to receive a donation. + +[![Donate](https://img.shields.io/badge/Donate-PayPal-green.svg)](https://www.paypal.me/BenediktWaldvogel) ------------------------------------------------------------------------------- LIBLINEAR is a simple package for solving large-scale regularized linear -classification and regression. It currently supports +classification, regression and outlier detection. It currently supports - L2-regularized logistic regression/L2-loss support vector classification/L1-loss support vector classification - L1-regularized L2-loss support vector classification/L1-regularized logistic regression -- L2-regularized L2-loss support vector regression/L1-loss support vector regression. +- L2-regularized L2-loss support vector regression/L1-loss support vector regression +- one-class support vector machine. This document explains the usage of LIBLINEAR. To get started, please read the **Quick Start** section first. @@ -51,15 +89,11 @@ Table of Contents - When to use LIBLINEAR but not LIBSVM - Quick Start -- Installation - `train` Usage - `predict` Usage - Examples - Library Usage -- Building Windows Binaries - Additional Information -- MATLAB/OCTAVE interface -- PYTHON interface When to use LIBLINEAR but not LIBSVM ==================================== @@ -72,8 +106,9 @@ is an example. Warning: While generally liblinear is very fast, its default solver may be slow under certain situations (e.g., data not scaled or C is -large). See Appendix B of our SVM guide about how to handle such -cases. +large). +See Appendix B of our SVM guide about how to handle such cases. + http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf Warning: If you are a beginner and your data sets are not large, you @@ -108,26 +143,6 @@ To obtain good performances, sometimes one needs to scale the data. Please check the program `svm-scale` of LIBSVM. For large and sparse data, use `-l 0` to keep the sparsity. -Installation -============ - -On Unix systems, type `make` to build the `train` and `predict` -programs. Run them without arguments to show the usages. - -On other systems, consult `Makefile` to build them (e.g., see -'Building Windows binaries' in this file) or use the pre-built -binaries (Windows binaries are in the directory `windows`). - -This software uses some level-1 BLAS subroutines. The needed functions are -included in this package. If a BLAS library is available on your -machine, you may use it by modifying the Makefile: Unmark the following line - - #LIBS ?= -lblas - -and mark - - LIBS ?= blas/blas.a - `train` Usage ============= @@ -147,31 +162,42 @@ and mark 11 -- L2-regularized L2-loss support vector regression (primal) 12 -- L2-regularized L2-loss support vector regression (dual) 13 -- L2-regularized L1-loss support vector regression (dual) + for outlier detection + 21 -- one-class support vector machine (dual) -c cost : set the parameter C (default 1) -p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1) + -n nu : set the parameter nu of one-class SVM (default 0.5) -e epsilon : set tolerance of termination criterion -s 0 and 2 |f'(w)|_2 <= eps*min(pos,neg)/l*|f'(w0)|_2, where f is the primal function and pos/neg are # of positive/negative data (default 0.01) -s 11 - |f'(w)|_2 <= eps*|f'(w0)|_2 (default 0.001) - -s 1, 3, 4 and 7 - Dual maximal violation <= eps; similar to libsvm (default 0.1) + |f'(w)|_2 <= eps*|f'(w0)|_2 (default 0.0001) + -s 1, 3, 4, 7, and 21 + Dual maximal violation <= eps; similar to libsvm (default 0.1 except 0.01 for -s 21) -s 5 and 6 - |f'(w)|_inf <= eps*min(pos,neg)/l*|f'(w0)|_inf, + |f'(w)|_1 <= eps*min(pos,neg)/l*|f'(w0)|_1, where f is the primal function (default 0.01) - -s 12 and 13\n" + -s 12 and 13 |f'(alpha)|_1 <= eps |f'(alpha0)|, where f is the dual function (default 0.1) -B bias : if bias >= 0, instance x becomes [x; bias]; if < 0, no bias term added (default -1) + -R : not regularize the bias; must with -B 1 to have the bias; DON'T use this unless you know what it is + (for -s 0, 2, 5, 6, 11) -wi weight: weights adjust the parameter C of different classes (see README for details) -v n: n-fold cross validation mode + -C : find parameters (C for -s 0, 2 and C, p for -s 11) -q : quiet mode (no outputs) Option -v randomly splits the data into n parts and calculates cross validation accuracy on them. +Option -C conducts cross validation under different parameters and finds +the best one. This option is supported only by -s 0, -s 2 (for finding +C) and -s 11 (for finding C, p). If the solver is not specified, -s 2 +is used. + Formulations: For L2-regularized logistic regression (-s 0), we solve @@ -226,13 +252,30 @@ where Q is a matrix with Q_ij = x_i^T x_j. -If `bias >= 0`, w becomes `[w; w_{n+1}]` and x becomes `[x; bias]`. +For one-class SVM dual (-s 21), we solve + + min_alpha 0.5(alpha^T Q alpha) + s.t. 0 <= alpha_i <= 1 and \sum alpha_i = nu*l, + +where + + Q is a matrix with Q_ij = x_i^T x_j. + +If `bias >= 0`, w becomes `[w; w_{n+1}]` and x becomes `[x; bias]`. For +example, L2-regularized logistic regression (-s 0) becomes + + min_w w^Tw/2 + (w_{n+1})^2/2 + C \sum log(1 + exp(-y_i [w; w_{n+1}]^T[x_i; bias])) + +Some may prefer not having `(w_{n+1})^2/2` (i.e., bias variable not +regularized). For primal solvers (-s 0, 2, 5, 6, 11), we provide an +option -R to remove `(w_{n+1})^2/2`. However, -R is generally not needed +as for most data with/without `(w_{n+1})^2/2` give similar performances. The primal-dual relationship implies that -s 1 and -s 2 give the same model, -s 0 and -s 7 give the same, and -s 11 and -s 12 give the same. We implement 1-vs-the rest multi-class strategy for classification. -In training i vs. non_i, their C parameters are `(weight from -wi)*C` +In training `i` vs. `non_i`, their C parameters are `(weight from -wi)*C` and C, respectively. If there are only two classes, we train only one model. Thus `weight1*C` vs. `weight2*C` is used. See examples below. @@ -275,12 +318,35 @@ Train linear SVM with L2-loss function. Train a logistic regression model. + > train -s 21 -n 0.1 data_file + +Train a linear one-class SVM which selects roughly 10% data as outliers. + > train -v 5 -e 0.001 data_file -Do five-fold cross-validation using L2-loss svm. +Do five-fold cross-validation using L2-loss SVM. Use a smaller stopping tolerance 0.001 than the default 0.1 if you want more accurate solutions. + > train -C data_file + +Conduct cross validation many times by L2-loss SVM +and find the parameter C which achieves the best cross +validation accuracy. + + > train -C -s 0 -v 3 -c 0.5 -e 0.0001 data_file + +For parameter selection by -C, users can specify other +solvers (currently -s 0, -s 2 and -s 11 are supported) and +different number of CV folds. Further, users can use +the -c option to specify the smallest C value of the +search range. This option is useful when users want to +rerun the parameter selection procedure from a specified +C under a different setting, such as a stricter stopping +tolerance -e 0.0001 in the above example. Similarly, for +-s 11, users can use the -p option to specify the +maximal p value of the search range. + > train -c 10 -w1 2 -w2 5 -w3 2 four_class_data_file Train four classifiers: @@ -302,6 +368,11 @@ Output probability estimates (for logistic regression only). Library Usage ============= +These functions and structures are declared in the header file `linear.h`. +You can see `train.c` and `predict.c` for examples showing how to use them. +We define LIBLINEAR_VERSION and declare `extern int liblinear_version;` +in linear.h, so you can check the version number. + - Function: `model* train(const struct problem *prob, const struct parameter *param);` @@ -329,26 +400,26 @@ Library Usage For example, if we have the following training data: - LABEL ATTR1 ATTR2 ATTR3 ATTR4 ATTR5 - ----- ----- ----- ----- ----- ----- - 1 0 0.1 0.2 0 0 - 2 0 0.1 0.3 -1.2 0 - 1 0.4 0 0 0 0 - 2 0 0.1 0 1.4 0.5 - 3 -0.1 -0.2 0.1 1.1 0.1 + LABEL ATTR1 ATTR2 ATTR3 ATTR4 ATTR5 + ----- ----- ----- ----- ----- ----- + 1 0 0.1 0.2 0 0 + 2 0 0.1 0.3 -1.2 0 + 1 0.4 0 0 0 0 + 2 0 0.1 0 1.4 0.5 + 3 -0.1 -0.2 0.1 1.1 0.1 and bias = 1, then the components of problem are: - l = 5 - n = 6 + l = 5 + n = 6 - y -> 1 2 1 2 3 + y -> 1 2 1 2 3 - x -> [ ] -> (2,0.1) (3,0.2) (6,1) (-1,?) - [ ] -> (2,0.1) (3,0.3) (4,-1.2) (6,1) (-1,?) - [ ] -> (1,0.4) (6,1) (-1,?) - [ ] -> (2,0.1) (4,1.4) (5,0.5) (6,1) (-1,?) - [ ] -> (1,-0.1) (2,-0.2) (3,0.1) (4,1.1) (5,0.1) (6,1) (-1,?) + x -> [ ] -> (2,0.1) (3,0.2) (6,1) (-1,?) + [ ] -> (2,0.1) (3,0.3) (4,-1.2) (6,1) (-1,?) + [ ] -> (1,0.4) (6,1) (-1,?) + [ ] -> (2,0.1) (4,1.4) (5,0.5) (6,1) (-1,?) + [ ] -> (1,-0.1) (2,-0.2) (3,0.1) (4,1.1) (5,0.1) (6,1) (-1,?) `struct parameter` describes the parameters of a linear classification or regression model: @@ -357,15 +428,17 @@ Library Usage int solver_type; /* these are for training only */ - double eps; /* stopping criteria */ + double eps; /* stopping tolerance */ double C; + double nu; /* one-class SVM only */ int nr_weight; int *weight_label; double* weight; double p; + double *init_sol; }; - solver_type can be one of L2R_LR, L2R_L2LOSS_SVC_DUAL, L2R_L2LOSS_SVC, L2R_L1LOSS_SVC_DUAL, MCSVM_CS, L1R_L2LOSS_SVC, L1R_LR, L2R_LR_DUAL, L2R_L2LOSS_SVR, L2R_L2LOSS_SVR_DUAL, L2R_L1LOSS_SVR_DUAL. + solver_type can be one of L2R_LR, L2R_L2LOSS_SVC_DUAL, L2R_L2LOSS_SVC, L2R_L1LOSS_SVC_DUAL, MCSVM_CS, L1R_L2LOSS_SVC, L1R_LR, L2R_LR_DUAL, L2R_L2LOSS_SVR, L2R_L2LOSS_SVR_DUAL, L2R_L1LOSS_SVR_DUAL, ONECLASS_SVM. for classification - `L2R_LR` L2-regularized logistic regression (primal) - `L2R_L2LOSS_SVC_DUAL` L2-regularized L2-loss support vector classification (dual) @@ -374,13 +447,17 @@ Library Usage - `MCSVM_CS` support vector classification by Crammer and Singer - `L1R_L2LOSS_SVC` L1-regularized L2-loss support vector classification - `L1R_LR` L1-regularized logistic regression - - `L2R_LR_DUAL` L2-regularized logistic regression (dual) for regression + - `L2R_LR_DUAL` L2-regularized logistic regression (dual) + for regression - `L2R_L2LOSS_SVR` L2-regularized L2-loss support vector regression (primal) - `L2R_L2LOSS_SVR_DUAL` L2-regularized L2-loss support vector regression (dual) - `L2R_L1LOSS_SVR_DUAL` L2-regularized L1-loss support vector regression (dual) + for outlier detection + - `ONECLASS_SVM` one-class support vector machine (dual) C is the cost of constraints violation. p is the sensitiveness of loss of support vector regression. + nu in ONECLASS_SVM approximates the fraction of data as outliers. eps is the stopping criterion. nr_weight, weight_label, and weight are used to change the penalty @@ -395,6 +472,10 @@ Library Usage If you do not want to change penalty for any of the classes, just set nr_weight to 0. + init_sol includes the initial weight vectors (supported for only some + solvers). See the explanation of the vector w in the model + structure. + *NOTE* To avoid wrong parameters, check_parameter() should be called before train(). @@ -408,6 +489,7 @@ Library Usage double *w; int *label; /* label of each class */ double bias; + double rho; /* one-class SVM only */ }; param describes the parameters used to obtain the model. @@ -415,21 +497,26 @@ Library Usage nr_class and nr_feature are the number of classes and features, respectively. nr_class = 2 for regression. - The nr_feature*nr_class array w gives feature weights. We use one + The array w gives feature weights; its size is + nr_feature*nr_class but is nr_feature if nr_class = 2. We use one against the rest for multi-class classification, so each feature index corresponds to nr_class weight values. Weights are organized in the following way + ``` +------------------+------------------+------------+ | nr_class weights | nr_class weights | ... | for 1st feature | for 2nd feature | +------------------+------------------+------------+ + ``` + + The array label stores class labels. If bias >= 0, x becomes [x; bias]. The number of features is increased by one, so w is a (nr_feature+1)*nr_class array. The value of bias is stored in the variable bias. - The array label stores class labels. + rho is the bias term used in one-class SVM only. - Function: `void cross_validation(const problem *prob, const parameter *param, int nr_fold, double *target);` @@ -441,6 +528,34 @@ Library Usage The format of prob is same as that for train(). +- Function: `void find_parameters(const struct problem *prob, + const struct parameter *param, int nr_fold, double start_C, + double start_p, double *best_C, double *best_p, double *best_score);` + + This function is similar to cross_validation. However, instead of + conducting cross validation under specified parameters. For -s 0, 2, it + conducts cross validation many times under parameters C = start_C, + 2*start_C, 4*start_C, 8*start_C, ..., and finds the best one with + the highest cross validation accuracy. For -s 11, it conducts cross + validation many times with a two-fold loop. The outer loop considers a + default sequence of p = 19/20*max_p, ..., 1/20*max_p, 0 and + under each p value the inner loop considers a sequence of parameters + C = start_C, 2*start_C, 4*start_C, ..., and finds the best one with the + lowest mean squared error. + + If start_C <= 0, then this procedure calculates a small enough C + for prob as the start_C. The procedure stops when the models of + all folds become stable or C reaches max_C. + + If start_p <= 0, then this procedure calculates a maximal p for prob as + the start_p. Otherwise, the procedure starts with the first + i/20*max_p <= start_p so the outer sequence is i/20*max_p, + (i-1)/20*max_p, ..., 0. + + The best C, the best p, and the corresponding accuracy (or MSE) are + assigned to *best_C, *best_p and *best_score, respectively. For + classification, *best_p is not used, and the returned value is -1. + - Function: `double predict(const model *model_, const feature_node *x);` For a classification model, the predicted class for x is returned. @@ -452,11 +567,11 @@ Library Usage This function gives nr_w decision values in the array dec_values. nr_w=1 if regression is applied or the number of classes is two. An exception is - multi-class svm by Crammer and Singer (-s 4), where nr_w = 2 if there are two classes. For all other situations, nr_w is the + multi-class SVM by Crammer and Singer (-s 4), where nr_w = 2 if there are two classes. For all other situations, nr_w is the number of classes. We implement one-vs-the rest multi-class strategy (-s 0,1,2,3,5,6,7) - and multi-class svm by Crammer and Singer (-s 4) for multi-class SVM. + and multi-class SVM by Crammer and Singer (-s 4) for multi-class SVM. The class with the highest decision value is returned. - Function: `double predict_probability(const struct model *model_, @@ -482,6 +597,30 @@ Library Usage This function outputs the name of labels into an array called label. For a regression model, label is unchanged. +- Function: `double get_decfun_coef(const struct model *model_, int feat_idx, + int label_idx);` + + This function gives the coefficient for the feature with feature index = + feat_idx and the class with label index = label_idx. Note that feat_idx + starts from 1, while label_idx starts from 0. If feat_idx is not in the + valid range (1 to nr_feature), then a zero value will be returned. For + classification models, if label_idx is not in the valid range (0 to + nr_class-1), then a zero value will be returned; for regression models + and one-class SVM models, label_idx is ignored. + +- Function: `double get_decfun_bias(const struct model *model_, int label_idx);` + + This function gives the bias term corresponding to the class with the + label_idx. For classification models, if label_idx is not in a valid range + (0 to nr_class-1), then a zero value will be returned; for regression + models, label_idx is ignored. This function cannot be called for a one-class + SVM model. + +- Function: `double get_decfun_rho(const struct model *model_);` + + This function gives rho, the bias term used in one-class SVM only. This + function can only be called for a one-class SVM model. + - Function: `const char *check_parameter(const struct problem *prob, const struct parameter *param);` @@ -490,6 +629,21 @@ Library Usage train() and cross_validation(). It returns NULL if the parameters are feasible, otherwise an error message is returned. +- Function: `int check_probability_model(const struct model *model);` + + This function returns 1 if the model supports probability output; + otherwise, it returns 0. + +- Function: `int check_regression_model(const struct model *model);` + + This function returns 1 if the model is a regression model; otherwise + it returns 0. + +- Function: `int check_oneclass_model(const struct model *model);` + + This function returns 1 if the model is a one-class SVM model; otherwise + it returns 0. + - Function: `int save_model(const char *model_file_name, const struct model *model_);` @@ -520,34 +674,6 @@ Library Usage set_print_string_function(NULL); for default printing to stdout. -Building Windows Binaries -========================= - -Windows binaries are in the directory `windows`. To build them via -Visual C++, use the following steps: - -1. Open a dos command box and change to liblinear directory. If -environment variables of VC++ have not been set, type - - `C:\Program Files\Microsoft Visual Studio 10.0\VC\bin\vcvars32.bat` - - You may have to modify the above command according which version of VC++ or where it is installed. - -2. Type - - `nmake -f Makefile.win clean all` - - -MATLAB/OCTAVE Interface -======================= - -Please check the file README in the directory `matlab`. - -PYTHON Interface -================ - -Please check the file README in the directory `python`. - Additional Information ====================== diff --git a/build.gradle b/build.gradle new file mode 100644 index 0000000..6af5d77 --- /dev/null +++ b/build.gradle @@ -0,0 +1,172 @@ +buildscript { + repositories { + mavenCentral() + } + dependencyLocking { + lockAllConfigurations() + } +} + +plugins { + id 'java-library' + id 'maven-publish' + id 'signing' + id 'jacoco' + id 'de.undercouch.download' version 'latest.release' + id 'me.champeau.gradle.jmh' version 'latest.release' +} + +group = "de.bwaldvogel" +version = '2.44' + +java { + sourceCompatibility = '11' + targetCompatibility = '11' +} + +tasks.withType(JavaCompile) { + options.encoding = 'UTF-8' + options.compilerArgs.addAll(['-Xlint:all', '-Werror']) +} + +jar { + manifest { + attributes 'Implementation-Title': project.name, 'Implementation-Version': archiveVersion + } +} + +task sourceJar(type: Jar) { + archiveClassifier = "sources" + from sourceSets.main.allJava +} + +task javadocJar(type: Jar, dependsOn: javadoc) { + archiveClassifier = "javadoc" + from javadoc.destinationDir +} + +publishing { + publications { + mavenJava(MavenPublication) { + groupId = project.group + artifactId = project.name + version = project.version + pom { + name = project.name + description = 'Java port of Liblinear' + url = 'https://github.com/bwaldvogel/liblinear' + inceptionYear = '2008' + + licenses { + license { + name = 'The BSD License' + url = 'http://www.opensource.org/licenses/bsd-license.php' + distribution = 'repo' + } + } + developers { + developer { + id = 'bwaldvogel' + name = 'Benedikt Waldvogel' + email = 'mail@bwaldvogel.de' + } + } + scm { + url = 'git@github.com:bwaldvogel/liblinear.git' + connection = 'scm:git:git@github.com:bwaldvogel/liblinear.git' + developerConnection = 'scm:git:git@github.com:bwaldvogel/liblinear.git' + } + } + + from components.java + + artifact sourceJar + artifact javadocJar + + versionMapping { + usage('java-api') { + fromResolutionOf('runtimeClasspath') + } + usage('java-runtime') { + fromResolutionResult() + } + } + } + } + repositories { + maven { + url = "https://oss.sonatype.org/service/local/staging/deploy/maven2" + credentials { + username = project.hasProperty('nexusUsername') ? project.property('nexusUsername') : System.getenv('NEXUS_USERNAME') + password = project.hasProperty('nexusPassword') ? project.property('nexusPassword') : System.getenv('NEXUS_PASSWORD') + } + } + } +} + +signing { + useGpgCmd() + sign publishing.publications.mavenJava +} + +repositories { + mavenCentral() +} + +jacocoTestReport { + reports { + html.required = true + xml.required = true + csv.required = false + } + dependsOn test +} + +apply from: 'datasets.gradle' + +test.dependsOn downloadAndVerifyDatasets + +test { + useJUnitPlatform() + + maxHeapSize = "256m" +} + +wrapper { + gradleVersion = "8.12" + distributionType = Wrapper.DistributionType.ALL +} + +jmh { + include = ['de\\.bwaldvogel\\.liblinear\\.LinearBenchmark.*'] + jvmArgs = ["-Ddataset.directory=" + projectDir + "/src/test/datasets"] +} + +tasks.named('jmh').configure { + dependsOn downloadAndVerifyRcv1 +} + +dependencies { + testImplementation group: 'org.junit.jupiter', name: 'junit-jupiter-api', version: 'latest.release' + testImplementation group: 'org.junit.jupiter', name: 'junit-jupiter-params', version: 'latest.release' + testRuntimeOnly group: 'org.junit.jupiter', name: 'junit-jupiter-engine', version: 'latest.release' + testImplementation group: 'org.slf4j', name: 'slf4j-api', version: 'latest.release' + testImplementation group: 'org.assertj', name: 'assertj-core', version: 'latest.release' + testImplementation group: 'org.mockito', name: 'mockito-core', version: 'latest.release' + testImplementation group: 'nl.jqno.equalsverifier', name: 'equalsverifier', version: 'latest.release' + + testRuntimeOnly group: 'ch.qos.logback', name: 'logback-classic', version: 'latest.release' + + jmh group: 'org.apache.commons', name: 'commons-compress', version: 'latest.release' + jmh 'org.openjdk.jmh:jmh-generator-annprocess:latest.release' // for IntelliJ + + components.all { ComponentMetadataDetails details -> + if (details.id.version =~ /(?i).+(-|\.)(CANDIDATE|RC|BETA|ALPHA|PR|M\d+).*/) { + details.status = 'milestone' + } + } +} + +dependencyLocking { + lockAllConfigurations() +} diff --git a/buildscript-gradle.lockfile b/buildscript-gradle.lockfile new file mode 100644 index 0000000..b463142 --- /dev/null +++ b/buildscript-gradle.lockfile @@ -0,0 +1,11 @@ +# This is a Gradle generated file for dependency locking. +# Manual edits can break the build and are not advised. +# This file is expected to be part of source control. +de.undercouch.download:de.undercouch.download.gradle.plugin:5.6.0=classpath +de.undercouch:gradle-download-task:5.6.0=classpath +me.champeau.gradle.jmh:me.champeau.gradle.jmh.gradle.plugin:0.5.3=classpath +me.champeau.gradle:jmh-gradle-plugin:0.5.3=classpath +net.sf.jopt-simple:jopt-simple:4.6=classpath +org.apache.commons:commons-math3:3.2=classpath +org.openjdk.jmh:jmh-core:1.27=classpath +empty= diff --git a/eclipse/cleanup.xml b/code-style/eclipse/cleanup.xml similarity index 100% rename from eclipse/cleanup.xml rename to code-style/eclipse/cleanup.xml diff --git a/eclipse/formatter.xml b/code-style/eclipse/formatter.xml similarity index 100% rename from eclipse/formatter.xml rename to code-style/eclipse/formatter.xml diff --git a/datasets.gradle b/datasets.gradle new file mode 100644 index 0000000..131ed88 --- /dev/null +++ b/datasets.gradle @@ -0,0 +1,77 @@ +task downloadDatasetSplice(type: Download) { + def baseUrl = 'https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary' + src([ + "${baseUrl}/splice", + "${baseUrl}/splice.t" + ]) + dest 'src/test/datasets/splice/' + overwrite false +} + +task verifyDatasetSpliceTrain(type: Verify, dependsOn: downloadDatasetSplice) { + src 'src/test/datasets/splice/splice' + algorithm 'SHA-256' + checksum 'eee33566cfe8988ec26122460cc712d66c5c5b79b966b259408e803addaa584d' +} + +task verifyDatasetSpliceTest(type: Verify, dependsOn: downloadDatasetSplice) { + src 'src/test/datasets/splice/splice.t' + algorithm 'SHA-256' + checksum 'f40da7cfd17860f3f1a1bb7d118f8e86c306d232ca6ac6ddefbaaef266abaa03' +} + +task downloadDatasetDna(type: Download) { + def baseUrl = 'https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass' + src([ + "${baseUrl}/dna.scale", + "${baseUrl}/dna.scale.t" + ]) + dest 'src/test/datasets/dna.scale/' + overwrite false +} + +task verifyDatasetDnaTrain(type: Verify, dependsOn: downloadDatasetDna) { + src 'src/test/datasets/dna.scale/dna.scale' + algorithm 'SHA-256' + checksum '03d39477c5e48415e9cd18e006efc8f7a3b5c3deaee8289a72511c35070e3cfe' +} + +task verifyDatasetDnaTest(type: Verify, dependsOn: downloadDatasetDna) { + src 'src/test/datasets/dna.scale/dna.scale.t' + algorithm 'SHA-256' + checksum 'd2eeed170e4e38ae2aba6aa4cd27a8ca4ecceb01a7751e8e509bb4e1a6bcef39' +} + +task downloadDatasetRcv1(type: Download) { + def baseUrl = 'https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary' + src([ + "${baseUrl}/rcv1_train.binary.bz2", + "${baseUrl}/rcv1_test.binary.bz2" + ]) + dest 'src/test/datasets/rcv1/' + overwrite false +} + +task verifyDatasetRcv1Train(type: Verify, dependsOn: downloadDatasetRcv1) { + src 'src/test/datasets/rcv1/rcv1_train.binary.bz2' + algorithm 'SHA-256' + checksum 'dda0c6381811484e284e70dd0641335917debce50948346416fde7fb32fad2e2' +} + +task verifyDatasetRcv1Test(type: Verify, dependsOn: downloadDatasetRcv1) { + src 'src/test/datasets/rcv1/rcv1_test.binary.bz2' + algorithm 'SHA-256' + checksum '975ab6c4fd8ea2a88b82e7ba2659ef21bf980ac61b3d8f13d6991739ec0f7aa7' +} + +task downloadAndVerifyRcv1 { + dependsOn 'verifyDatasetRcv1Train' + dependsOn 'verifyDatasetRcv1Test' +} + +task downloadAndVerifyDatasets { + dependsOn 'verifyDatasetSpliceTrain' + dependsOn 'verifyDatasetSpliceTest' + dependsOn 'verifyDatasetDnaTrain' + dependsOn 'verifyDatasetDnaTest' +} diff --git a/gradle.lockfile b/gradle.lockfile new file mode 100644 index 0000000..faf5ce3 --- /dev/null +++ b/gradle.lockfile @@ -0,0 +1,40 @@ +# This is a Gradle generated file for dependency locking. +# Manual edits can break the build and are not advised. +# This file is expected to be part of source control. +ch.qos.logback:logback-classic:1.5.15=jmhRuntimeClasspath,testRuntimeClasspath +ch.qos.logback:logback-core:1.5.15=jmhRuntimeClasspath,testRuntimeClasspath +commons-codec:commons-codec:1.17.1=jmh,jmhCompileClasspath,jmhRuntimeClasspath +commons-io:commons-io:2.16.1=jmh,jmhCompileClasspath,jmhRuntimeClasspath +net.bytebuddy:byte-buddy-agent:1.15.4=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath +net.bytebuddy:byte-buddy:1.15.11=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath +net.sf.jopt-simple:jopt-simple:5.0.4=jmh,jmhCompileClasspath,jmhRuntimeClasspath +nl.jqno.equalsverifier:equalsverifier:3.18=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath +org.apache.commons:commons-compress:1.27.1=jmh,jmhCompileClasspath,jmhRuntimeClasspath +org.apache.commons:commons-lang3:3.16.0=jmh,jmhCompileClasspath,jmhRuntimeClasspath +org.apache.commons:commons-math3:3.6.1=jmh,jmhCompileClasspath,jmhRuntimeClasspath +org.apiguardian:apiguardian-api:1.1.2=testCompileClasspath +org.assertj:assertj-core:3.27.0=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath +org.jacoco:org.jacoco.agent:0.8.12=jacocoAgent,jacocoAnt +org.jacoco:org.jacoco.ant:0.8.12=jacocoAnt +org.jacoco:org.jacoco.core:0.8.12=jacocoAnt +org.jacoco:org.jacoco.report:0.8.12=jacocoAnt +org.junit.jupiter:junit-jupiter-api:5.11.4=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath +org.junit.jupiter:junit-jupiter-engine:5.11.4=jmhRuntimeClasspath,testRuntimeClasspath +org.junit.jupiter:junit-jupiter-params:5.11.4=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath +org.junit.platform:junit-platform-commons:1.11.4=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath +org.junit.platform:junit-platform-engine:1.11.4=jmhRuntimeClasspath,testRuntimeClasspath +org.junit:junit-bom:5.11.4=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath +org.mockito:mockito-core:5.14.2=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath +org.objenesis:objenesis:3.4=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath +org.openjdk.jmh:jmh-core:1.37=jmh,jmhCompileClasspath,jmhRuntimeClasspath +org.openjdk.jmh:jmh-generator-annprocess:1.37=jmh,jmhCompileClasspath,jmhRuntimeClasspath +org.openjdk.jmh:jmh-generator-asm:1.25=jmh,jmhCompileClasspath,jmhRuntimeClasspath +org.openjdk.jmh:jmh-generator-bytecode:1.25=jmh,jmhCompileClasspath,jmhRuntimeClasspath +org.openjdk.jmh:jmh-generator-reflection:1.25=jmh,jmhCompileClasspath,jmhRuntimeClasspath +org.opentest4j:opentest4j:1.3.0=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath +org.ow2.asm:asm-commons:9.7=jacocoAnt +org.ow2.asm:asm-tree:9.7=jacocoAnt +org.ow2.asm:asm:5.0.3=jmh,jmhCompileClasspath,jmhRuntimeClasspath +org.ow2.asm:asm:9.7=jacocoAnt +org.slf4j:slf4j-api:2.0.16=jmhRuntimeClasspath,testCompileClasspath,testRuntimeClasspath +empty=annotationProcessor,compileClasspath,jmhAnnotationProcessor,runtimeClasspath,signatures,testAnnotationProcessor diff --git a/gradle/wrapper/gradle-wrapper.jar b/gradle/wrapper/gradle-wrapper.jar new file mode 100644 index 0000000..a4b76b9 Binary files /dev/null and b/gradle/wrapper/gradle-wrapper.jar differ diff --git a/gradle/wrapper/gradle-wrapper.properties b/gradle/wrapper/gradle-wrapper.properties new file mode 100644 index 0000000..e0fd020 --- /dev/null +++ b/gradle/wrapper/gradle-wrapper.properties @@ -0,0 +1,7 @@ +distributionBase=GRADLE_USER_HOME +distributionPath=wrapper/dists +distributionUrl=https\://services.gradle.org/distributions/gradle-8.12-all.zip +networkTimeout=10000 +validateDistributionUrl=true +zipStoreBase=GRADLE_USER_HOME +zipStorePath=wrapper/dists diff --git a/gradlew b/gradlew new file mode 100755 index 0000000..f3b75f3 --- /dev/null +++ b/gradlew @@ -0,0 +1,251 @@ +#!/bin/sh + +# +# Copyright © 2015-2021 the original authors. +# +# 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 +# +# https://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. +# +# SPDX-License-Identifier: Apache-2.0 +# + +############################################################################## +# +# Gradle start up script for POSIX generated by Gradle. +# +# Important for running: +# +# (1) You need a POSIX-compliant shell to run this script. If your /bin/sh is +# noncompliant, but you have some other compliant shell such as ksh or +# bash, then to run this script, type that shell name before the whole +# command line, like: +# +# ksh Gradle +# +# Busybox and similar reduced shells will NOT work, because this script +# requires all of these POSIX shell features: +# * functions; +# * expansions «$var», «${var}», «${var:-default}», «${var+SET}», +# «${var#prefix}», «${var%suffix}», and «$( cmd )»; +# * compound commands having a testable exit status, especially «case»; +# * various built-in commands including «command», «set», and «ulimit». +# +# Important for patching: +# +# (2) This script targets any POSIX shell, so it avoids extensions provided +# by Bash, Ksh, etc; in particular arrays are avoided. +# +# The "traditional" practice of packing multiple parameters into a +# space-separated string is a well documented source of bugs and security +# problems, so this is (mostly) avoided, by progressively accumulating +# options in "$@", and eventually passing that to Java. +# +# Where the inherited environment variables (DEFAULT_JVM_OPTS, JAVA_OPTS, +# and GRADLE_OPTS) rely on word-splitting, this is performed explicitly; +# see the in-line comments for details. +# +# There are tweaks for specific operating systems such as AIX, CygWin, +# Darwin, MinGW, and NonStop. +# +# (3) This script is generated from the Groovy template +# https://github.com/gradle/gradle/blob/HEAD/platforms/jvm/plugins-application/src/main/resources/org/gradle/api/internal/plugins/unixStartScript.txt +# within the Gradle project. +# +# You can find Gradle at https://github.com/gradle/gradle/. +# +############################################################################## + +# Attempt to set APP_HOME + +# Resolve links: $0 may be a link +app_path=$0 + +# Need this for daisy-chained symlinks. +while + APP_HOME=${app_path%"${app_path##*/}"} # leaves a trailing /; empty if no leading path + [ -h "$app_path" ] +do + ls=$( ls -ld "$app_path" ) + link=${ls#*' -> '} + case $link in #( + /*) app_path=$link ;; #( + *) app_path=$APP_HOME$link ;; + esac +done + +# This is normally unused +# shellcheck disable=SC2034 +APP_BASE_NAME=${0##*/} +# Discard cd standard output in case $CDPATH is set (https://github.com/gradle/gradle/issues/25036) +APP_HOME=$( cd -P "${APP_HOME:-./}" > /dev/null && printf '%s\n' "$PWD" ) || exit + +# Use the maximum available, or set MAX_FD != -1 to use that value. +MAX_FD=maximum + +warn () { + echo "$*" +} >&2 + +die () { + echo + echo "$*" + echo + exit 1 +} >&2 + +# OS specific support (must be 'true' or 'false'). +cygwin=false +msys=false +darwin=false +nonstop=false +case "$( uname )" in #( + CYGWIN* ) cygwin=true ;; #( + Darwin* ) darwin=true ;; #( + MSYS* | MINGW* ) msys=true ;; #( + NONSTOP* ) nonstop=true ;; +esac + +CLASSPATH=$APP_HOME/gradle/wrapper/gradle-wrapper.jar + + +# Determine the Java command to use to start the JVM. +if [ -n "$JAVA_HOME" ] ; then + if [ -x "$JAVA_HOME/jre/sh/java" ] ; then + # IBM's JDK on AIX uses strange locations for the executables + JAVACMD=$JAVA_HOME/jre/sh/java + else + JAVACMD=$JAVA_HOME/bin/java + fi + if [ ! -x "$JAVACMD" ] ; then + die "ERROR: JAVA_HOME is set to an invalid directory: $JAVA_HOME + +Please set the JAVA_HOME variable in your environment to match the +location of your Java installation." + fi +else + JAVACMD=java + if ! command -v java >/dev/null 2>&1 + then + die "ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH. + +Please set the JAVA_HOME variable in your environment to match the +location of your Java installation." + fi +fi + +# Increase the maximum file descriptors if we can. +if ! "$cygwin" && ! "$darwin" && ! "$nonstop" ; then + case $MAX_FD in #( + max*) + # In POSIX sh, ulimit -H is undefined. That's why the result is checked to see if it worked. + # shellcheck disable=SC2039,SC3045 + MAX_FD=$( ulimit -H -n ) || + warn "Could not query maximum file descriptor limit" + esac + case $MAX_FD in #( + '' | soft) :;; #( + *) + # In POSIX sh, ulimit -n is undefined. That's why the result is checked to see if it worked. + # shellcheck disable=SC2039,SC3045 + ulimit -n "$MAX_FD" || + warn "Could not set maximum file descriptor limit to $MAX_FD" + esac +fi + +# Collect all arguments for the java command, stacking in reverse order: +# * args from the command line +# * the main class name +# * -classpath +# * -D...appname settings +# * --module-path (only if needed) +# * DEFAULT_JVM_OPTS, JAVA_OPTS, and GRADLE_OPTS environment variables. + +# For Cygwin or MSYS, switch paths to Windows format before running java +if "$cygwin" || "$msys" ; then + APP_HOME=$( cygpath --path --mixed "$APP_HOME" ) + CLASSPATH=$( cygpath --path --mixed "$CLASSPATH" ) + + JAVACMD=$( cygpath --unix "$JAVACMD" ) + + # Now convert the arguments - kludge to limit ourselves to /bin/sh + for arg do + if + case $arg in #( + -*) false ;; # don't mess with options #( + /?*) t=${arg#/} t=/${t%%/*} # looks like a POSIX filepath + [ -e "$t" ] ;; #( + *) false ;; + esac + then + arg=$( cygpath --path --ignore --mixed "$arg" ) + fi + # Roll the args list around exactly as many times as the number of + # args, so each arg winds up back in the position where it started, but + # possibly modified. + # + # NB: a `for` loop captures its iteration list before it begins, so + # changing the positional parameters here affects neither the number of + # iterations, nor the values presented in `arg`. + shift # remove old arg + set -- "$@" "$arg" # push replacement arg + done +fi + + +# Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script. +DEFAULT_JVM_OPTS='"-Xmx64m" "-Xms64m"' + +# Collect all arguments for the java command: +# * DEFAULT_JVM_OPTS, JAVA_OPTS, JAVA_OPTS, and optsEnvironmentVar are not allowed to contain shell fragments, +# and any embedded shellness will be escaped. +# * For example: A user cannot expect ${Hostname} to be expanded, as it is an environment variable and will be +# treated as '${Hostname}' itself on the command line. + +set -- \ + "-Dorg.gradle.appname=$APP_BASE_NAME" \ + -classpath "$CLASSPATH" \ + org.gradle.wrapper.GradleWrapperMain \ + "$@" + +# Stop when "xargs" is not available. +if ! command -v xargs >/dev/null 2>&1 +then + die "xargs is not available" +fi + +# Use "xargs" to parse quoted args. +# +# With -n1 it outputs one arg per line, with the quotes and backslashes removed. +# +# In Bash we could simply go: +# +# readarray ARGS < <( xargs -n1 <<<"$var" ) && +# set -- "${ARGS[@]}" "$@" +# +# but POSIX shell has neither arrays nor command substitution, so instead we +# post-process each arg (as a line of input to sed) to backslash-escape any +# character that might be a shell metacharacter, then use eval to reverse +# that process (while maintaining the separation between arguments), and wrap +# the whole thing up as a single "set" statement. +# +# This will of course break if any of these variables contains a newline or +# an unmatched quote. +# + +eval "set -- $( + printf '%s\n' "$DEFAULT_JVM_OPTS $JAVA_OPTS $GRADLE_OPTS" | + xargs -n1 | + sed ' s~[^-[:alnum:]+,./:=@_]~\\&~g; ' | + tr '\n' ' ' + )" '"$@"' + +exec "$JAVACMD" "$@" diff --git a/gradlew.bat b/gradlew.bat new file mode 100644 index 0000000..9b42019 --- /dev/null +++ b/gradlew.bat @@ -0,0 +1,94 @@ +@rem +@rem Copyright 2015 the original author or authors. +@rem +@rem Licensed under the Apache License, Version 2.0 (the "License"); +@rem you may not use this file except in compliance with the License. +@rem You may obtain a copy of the License at +@rem +@rem https://www.apache.org/licenses/LICENSE-2.0 +@rem +@rem Unless required by applicable law or agreed to in writing, software +@rem distributed under the License is distributed on an "AS IS" BASIS, +@rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +@rem See the License for the specific language governing permissions and +@rem limitations under the License. +@rem +@rem SPDX-License-Identifier: Apache-2.0 +@rem + +@if "%DEBUG%"=="" @echo off +@rem ########################################################################## +@rem +@rem Gradle startup script for Windows +@rem +@rem ########################################################################## + +@rem Set local scope for the variables with windows NT shell +if "%OS%"=="Windows_NT" setlocal + +set DIRNAME=%~dp0 +if "%DIRNAME%"=="" set DIRNAME=. +@rem This is normally unused +set APP_BASE_NAME=%~n0 +set APP_HOME=%DIRNAME% + +@rem Resolve any "." and ".." in APP_HOME to make it shorter. +for %%i in ("%APP_HOME%") do set APP_HOME=%%~fi + +@rem Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script. +set DEFAULT_JVM_OPTS="-Xmx64m" "-Xms64m" + +@rem Find java.exe +if defined JAVA_HOME goto findJavaFromJavaHome + +set JAVA_EXE=java.exe +%JAVA_EXE% -version >NUL 2>&1 +if %ERRORLEVEL% equ 0 goto execute + +echo. 1>&2 +echo ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH. 1>&2 +echo. 1>&2 +echo Please set the JAVA_HOME variable in your environment to match the 1>&2 +echo location of your Java installation. 1>&2 + +goto fail + +:findJavaFromJavaHome +set JAVA_HOME=%JAVA_HOME:"=% +set JAVA_EXE=%JAVA_HOME%/bin/java.exe + +if exist "%JAVA_EXE%" goto execute + +echo. 1>&2 +echo ERROR: JAVA_HOME is set to an invalid directory: %JAVA_HOME% 1>&2 +echo. 1>&2 +echo Please set the JAVA_HOME variable in your environment to match the 1>&2 +echo location of your Java installation. 1>&2 + +goto fail + +:execute +@rem Setup the command line + +set CLASSPATH=%APP_HOME%\gradle\wrapper\gradle-wrapper.jar + + +@rem Execute Gradle +"%JAVA_EXE%" %DEFAULT_JVM_OPTS% %JAVA_OPTS% %GRADLE_OPTS% "-Dorg.gradle.appname=%APP_BASE_NAME%" -classpath "%CLASSPATH%" org.gradle.wrapper.GradleWrapperMain %* + +:end +@rem End local scope for the variables with windows NT shell +if %ERRORLEVEL% equ 0 goto mainEnd + +:fail +rem Set variable GRADLE_EXIT_CONSOLE if you need the _script_ return code instead of +rem the _cmd.exe /c_ return code! +set EXIT_CODE=%ERRORLEVEL% +if %EXIT_CODE% equ 0 set EXIT_CODE=1 +if not ""=="%GRADLE_EXIT_CONSOLE%" exit %EXIT_CODE% +exit /b %EXIT_CODE% + +:mainEnd +if "%OS%"=="Windows_NT" endlocal + +:omega diff --git a/pom.xml b/pom.xml deleted file mode 100644 index d3b28e3..0000000 --- a/pom.xml +++ /dev/null @@ -1,128 +0,0 @@ - - 4.0.0 - de.bwaldvogel - liblinear - jar - liblinear - 1.92 - Java port of Liblinear - http://www.bwaldvogel.de/liblinear-java/ - - - - The BSD License - http://www.opensource.org/licenses/bsd-license.php - repo - - - - - org.sonatype.oss - oss-parent - 6 - - - - scm:git:git@github.com:bwaldvogel/liblinear-java.git - scm:git:git@github.com:bwaldvogel/liblinear-java.git - git@github.com:bwaldvogel/liblinear-java.git - - - - - bwaldvogel - Benedikt Waldvogel - mail at bwaldvogel.de - - - - - liblinear-${project.version} - - - maven-compiler-plugin - - 1.6 - 1.6 - - - - org.apache.maven.plugins - maven-source-plugin - 2.1 - - - attach-sources - - jar-no-fork - - - - - - org.apache.maven.plugins - maven-javadoc-plugin - - - attach-javadocs - - jar - - - - - - org.apache.maven.plugins - maven-gpg-plugin - - D2250F35 - - - - - - - UTF-8 - 1.4.8 - - - - - - org.powermock - powermock-module-junit4 - ${powermock.version} - test - - - org.powermock - powermock-api-mockito - ${powermock.version} - test - - - junit - junit - 4.8.1 - test - - - org.easytesting - fest-assert - 1.4 - test - - - - - - org.apache.maven.plugins - maven-javadoc-plugin - - - org.apache.maven.plugins - maven-jxr-plugin - - - - diff --git a/release.sh b/release.sh new file mode 100755 index 0000000..7930f38 --- /dev/null +++ b/release.sh @@ -0,0 +1,2 @@ +#!/bin/bash +./gradlew --no-daemon clean build publish diff --git a/settings.gradle b/settings.gradle new file mode 100644 index 0000000..a6ba6e7 --- /dev/null +++ b/settings.gradle @@ -0,0 +1 @@ +rootProject.name = "liblinear" diff --git a/src/jmh/java/de/bwaldvogel/liblinear/LinearBenchmark.java b/src/jmh/java/de/bwaldvogel/liblinear/LinearBenchmark.java new file mode 100644 index 0000000..8374cfe --- /dev/null +++ b/src/jmh/java/de/bwaldvogel/liblinear/LinearBenchmark.java @@ -0,0 +1,112 @@ +package de.bwaldvogel.liblinear; + +import java.io.IOException; +import java.io.InputStream; +import java.nio.file.Files; +import java.nio.file.Path; +import java.nio.file.Paths; +import java.util.concurrent.TimeUnit; + +import org.apache.commons.compress.compressors.bzip2.BZip2CompressorInputStream; +import org.openjdk.jmh.annotations.Benchmark; +import org.openjdk.jmh.annotations.BenchmarkMode; +import org.openjdk.jmh.annotations.Fork; +import org.openjdk.jmh.annotations.Measurement; +import org.openjdk.jmh.annotations.Mode; +import org.openjdk.jmh.annotations.OutputTimeUnit; +import org.openjdk.jmh.annotations.Param; +import org.openjdk.jmh.annotations.Scope; +import org.openjdk.jmh.annotations.Setup; +import org.openjdk.jmh.annotations.State; +import org.openjdk.jmh.annotations.Warmup; +import org.openjdk.jmh.runner.Runner; +import org.openjdk.jmh.runner.RunnerException; +import org.openjdk.jmh.runner.options.Options; +import org.openjdk.jmh.runner.options.OptionsBuilder; + +@Fork(1) +@BenchmarkMode(Mode.AverageTime) +@OutputTimeUnit(TimeUnit.MILLISECONDS) +@Warmup(iterations = 2, time = 5, timeUnit = TimeUnit.SECONDS) +@Measurement(iterations = 5, time = 3, timeUnit = TimeUnit.SECONDS) +public class LinearBenchmark { + + @Benchmark + public void readProblem(DatasetParameters datasetParameters) throws Exception { + Path trainingFile = getTrainingFile(datasetParameters.dataset); + try (InputStream inputStream = getInputStream(trainingFile)) { + Train.readProblem(inputStream, -1); + } + } + + @Benchmark + public void train(BenchmarkParameters benchmarkParameters) { + Linear.disableDebugOutput(); + Linear.train(benchmarkParameters.problem, new Parameter(benchmarkParameters.solverType, 1, 1e-3)); + } + + public enum Dataset { + RCV1, Splice, DnaScale + } + + + @State(Scope.Benchmark) + public static class BenchmarkParameters { + + @Param + private Dataset dataset; + + @Param({"L2R_LR", "L2R_L2LOSS_SVC_DUAL"}) + private SolverType solverType; + + private Problem problem; + + @Setup + public void loadDataset() throws Exception { + Path trainingFile = getTrainingFile(dataset); + try (InputStream inputStream = getInputStream(trainingFile)) { + problem = Train.readProblem(inputStream, -1); + } + } + + } + + @State(Scope.Benchmark) + public static class DatasetParameters { + + @Param + private Dataset dataset; + + } + + public static void main(String[] args) throws RunnerException { + Options opt = new OptionsBuilder() + .include(".*" + LinearBenchmark.class.getSimpleName() + ".*") + .build(); + + new Runner(opt).run(); + } + + private static InputStream getInputStream(Path path) throws IOException { + InputStream inputStream = Files.newInputStream(path); + if (path.toString().endsWith(".bz2")) { + return new BZip2CompressorInputStream(inputStream); + } + return inputStream; + } + + private static Path getTrainingFile(Dataset dataset) { + Path datasetDirectory = Paths.get(System.getProperty("dataset.directory", "src/test/datasets")); + switch (dataset) { + case RCV1: + return datasetDirectory.resolve("rcv1").resolve("rcv1_train.binary.bz2"); + case Splice: + return datasetDirectory.resolve("splice").resolve("splice"); + case DnaScale: + return datasetDirectory.resolve("dna.scale").resolve("dna.scale"); + default: + throw new IllegalArgumentException("Unknown dataset: " + dataset); + } + } + +} diff --git a/src/main/java/de/bwaldvogel/liblinear/ArraySorter.java b/src/main/java/de/bwaldvogel/liblinear/ArraySorter.java index bf7c45c..5b0e3b0 100644 --- a/src/main/java/de/bwaldvogel/liblinear/ArraySorter.java +++ b/src/main/java/de/bwaldvogel/liblinear/ArraySorter.java @@ -8,11 +8,11 @@ final class ArraySorter { * * This code is borrowed from Sun's JDK 1.6.0.07 */ - public static void reversedMergesort(double[] a) { + static void reversedMergesort(double[] a) { reversedMergesort(a, 0, a.length); } - private static void reversedMergesort(double x[], int off, int len) { + private static void reversedMergesort(double[] x, int off, int len) { // Insertion sort on smallest arrays if (len < 7) { for (int i = off; i < len + off; i++) @@ -40,14 +40,17 @@ private static void reversedMergesort(double x[], int off, int len) { int a = off, b = a, c = off + len - 1, d = c; while (true) { while (b <= c && x[b] >= v) { - if (x[b] == v) swap(x, a++, b); + if (x[b] == v) + swap(x, a++, b); b++; } while (c >= b && x[c] <= v) { - if (x[c] == v) swap(x, c, d--); + if (x[c] == v) + swap(x, c, d--); c--; } - if (b > c) break; + if (b > c) + break; swap(x, b++, c--); } @@ -59,14 +62,16 @@ private static void reversedMergesort(double x[], int off, int len) { vecswap(x, b, n - s, s); // Recursively sort non-partition-elements - if ((s = b - a) > 1) reversedMergesort(x, off, s); - if ((s = d - c) > 1) reversedMergesort(x, n - s, s); + if ((s = b - a) > 1) + reversedMergesort(x, off, s); + if ((s = d - c) > 1) + reversedMergesort(x, n - s, s); } /** * Swaps x[a] with x[b]. */ - private static void swap(double x[], int a, int b) { + private static void swap(double[] x, int a, int b) { double t = x[a]; x[a] = x[b]; x[b] = t; @@ -75,7 +80,7 @@ private static void swap(double x[], int a, int b) { /** * Swaps x[a .. (a+n-1)] with x[b .. (b+n-1)]. */ - private static void vecswap(double x[], int a, int b, int n) { + private static void vecswap(double[] x, int a, int b, int n) { for (int i = 0; i < n; i++, a++, b++) swap(x, a, b); } @@ -83,7 +88,7 @@ private static void vecswap(double x[], int a, int b, int n) { /** * Returns the index of the median of the three indexed doubles. */ - private static int med3(double x[], int a, int b, int c) { + private static int med3(double[] x, int a, int b, int c) { return (x[a] < x[b] ? (x[b] < x[c] ? b : x[a] < x[c] ? c : a) : (x[b] > x[c] ? b : x[a] > x[c] ? c : a)); } diff --git a/src/main/java/de/bwaldvogel/liblinear/Blas.java b/src/main/java/de/bwaldvogel/liblinear/Blas.java new file mode 100644 index 0000000..7d90184 --- /dev/null +++ b/src/main/java/de/bwaldvogel/liblinear/Blas.java @@ -0,0 +1,189 @@ +package de.bwaldvogel.liblinear; + +class Blas { + + static double dnrm2_(int n, double[] x, MutableInt incx) { + return dnrm2_(new MutableInt(n), x, incx); + } + + private static double dnrm2_(MutableInt n, double[] x, MutableInt incx) { + final double norm; + double scale, absxi, ssq, temp; + + /* DNRM2 returns the euclidean norm of a vector via the function + name, so that + + DNRM2 := sqrt( x'*x ) + + -- This version written on 25-October-1982. + Modified on 14-October-1993 to inline the call to SLASSQ. + Sven Hammarling, Nag Ltd. */ + + /* Dereference inputs */ + int nn = n.get(); + int iincx = incx.get(); + + if (nn > 0 && iincx > 0) { + if (nn == 1) { + norm = Math.abs(x[0]); + } else { + scale = 0.0; + ssq = 1.0; + + /* The following loop is equivalent to this call to the LAPACK + auxiliary routine: CALL SLASSQ( N, X, INCX, SCALE, SSQ ) */ + + for (int ix = (nn - 1) * iincx; ix >= 0; ix -= iincx) { + if (x[ix] != 0.0) { + absxi = Math.abs(x[ix]); + if (scale < absxi) { + temp = scale / absxi; + ssq = ssq * (temp * temp) + 1.0; + scale = absxi; + } else { + temp = absxi / scale; + ssq += temp * temp; + } + } + } + norm = scale * Math.sqrt(ssq); + } + } else + norm = 0.0; + + return norm; + } + + static double ddot_(int n, double[] sx, int incx, double[] sy, int incy) { + return ddot_(new MutableInt(n), sx, new MutableInt(incx), sy, new MutableInt(incy)); + } + + private static double ddot_(MutableInt n, double[] sx, MutableInt incx, double[] sy, MutableInt incy) { + int i, m, nn, iincx, iincy; + double stemp; + int ix, iy; + + /* forms the dot product of two vectors. + uses unrolled loops for increments equal to one. + jack dongarra, linpack, 3/11/78. + modified 12/3/93, array(1) declarations changed to array(*) */ + + /* Dereference inputs */ + nn = n.get(); + iincx = incx.get(); + iincy = incy.get(); + + stemp = 0.0; + if (nn > 0) { + if (iincx == 1 && iincy == 1) /* code for both increments equal to 1 */ { + m = nn - 4; + for (i = 0; i < m; i += 5) + stemp += sx[i] * sy[i] + sx[i + 1] * sy[i + 1] + sx[i + 2] * sy[i + 2] + + sx[i + 3] * sy[i + 3] + sx[i + 4] * sy[i + 4]; + + for (; i < nn; i++) /* clean-up loop */ + stemp += sx[i] * sy[i]; + } else /* code for unequal increments or equal increments not equal to 1 */ { + ix = 0; + iy = 0; + if (iincx < 0) + ix = (1 - nn) * iincx; + if (iincy < 0) + iy = (1 - nn) * iincy; + for (i = 0; i < nn; i++) { + stemp += sx[ix] * sy[iy]; + ix += iincx; + iy += iincy; + } + } + } + + return stemp; + } + + static int daxpy_(int n, double sa, double[] sx, int incx, double[] sy, int incy) { + return daxpy_(new MutableInt(n), new MutableDouble(sa), sx, new MutableInt(incx), sy, new MutableInt(incy)); + } + + static int daxpy_(MutableInt n, MutableDouble sa, double[] sx, MutableInt incx, double[] sy, MutableInt incy) { + int i, m, ix, iy, nn, iincx, iincy; + double ssa; + + /* constant times a vector plus a vector. + uses unrolled loop for increments equal to one. + jack dongarra, linpack, 3/11/78. + modified 12/3/93, array(1) declarations changed to array(*) */ + + /* Dereference inputs */ + nn = n.get(); + ssa = sa.get(); + iincx = incx.get(); + iincy = incy.get(); + + if (nn > 0 && ssa != 0.0) { + if (iincx == 1 && iincy == 1) /* code for both increments equal to 1 */ { + m = nn - 3; + for (i = 0; i < m; i += 4) { + sy[i] += ssa * sx[i]; + sy[i + 1] += ssa * sx[i + 1]; + sy[i + 2] += ssa * sx[i + 2]; + sy[i + 3] += ssa * sx[i + 3]; + } + for (; i < nn; ++i) /* clean-up loop */ + sy[i] += ssa * sx[i]; + } else /* code for unequal increments or equal increments not equal to 1 */ { + ix = iincx >= 0 ? 0 : (1 - nn) * iincx; + iy = iincy >= 0 ? 0 : (1 - nn) * iincy; + for (i = 0; i < nn; i++) { + sy[iy] += ssa * sx[ix]; + ix += iincx; + iy += iincy; + } + } + } + + return 0; + } + + static int dscal_(int n, double sa, double[] sx, int incx) { + return dscal_(new MutableInt(n), new MutableDouble(sa), sx, new MutableInt(incx)); + } + + static int dscal_(MutableInt n, MutableDouble sa, double[] sx, MutableInt incx) { + int i, m, nincx, nn, iincx; + double ssa; + + /* scales a vector by a constant. + uses unrolled loops for increment equal to 1. + jack dongarra, linpack, 3/11/78. + modified 3/93 to return if incx .le. 0. + modified 12/3/93, array(1) declarations changed to array(*) */ + + /* Dereference inputs */ + nn = n.get(); + iincx = incx.get(); + ssa = sa.get(); + + if (nn > 0 && iincx > 0) { + if (iincx == 1) /* code for increment equal to 1 */ { + m = nn - 4; + for (i = 0; i < m; i += 5) { + sx[i] = ssa * sx[i]; + sx[i + 1] = ssa * sx[i + 1]; + sx[i + 2] = ssa * sx[i + 2]; + sx[i + 3] = ssa * sx[i + 3]; + sx[i + 4] = ssa * sx[i + 4]; + } + for (; i < nn; ++i) /* clean-up loop */ + sx[i] = ssa * sx[i]; + } else /* code for increment not equal to 1 */ { + nincx = nn * iincx; + for (i = 0; i < nincx; i += iincx) + sx[i] = ssa * sx[i]; + } + } + + return 0; + } + +} diff --git a/src/main/java/de/bwaldvogel/liblinear/DoubleArrayPointer.java b/src/main/java/de/bwaldvogel/liblinear/DoubleArrayPointer.java index df9f9a2..0a20f2d 100644 --- a/src/main/java/de/bwaldvogel/liblinear/DoubleArrayPointer.java +++ b/src/main/java/de/bwaldvogel/liblinear/DoubleArrayPointer.java @@ -4,15 +4,15 @@ final class DoubleArrayPointer { private final double[] _array; - private int _offset; + private int _offset; - - public void setOffset(int offset) { - if (offset < 0 || offset >= _array.length) throw new IllegalArgumentException("offset must be between 0 and the length of the array"); + void setOffset(int offset) { + if (offset < 0 || offset >= _array.length) + throw new IllegalArgumentException("offset must be between 0 and the length of the array"); _offset = offset; } - public DoubleArrayPointer( final double[] array, final int offset ) { + DoubleArrayPointer(final double[] array, final int offset) { _array = array; setOffset(offset); } diff --git a/src/main/java/de/bwaldvogel/liblinear/FeatureNode.java b/src/main/java/de/bwaldvogel/liblinear/FeatureNode.java index 68cb3e7..b5caf3f 100644 --- a/src/main/java/de/bwaldvogel/liblinear/FeatureNode.java +++ b/src/main/java/de/bwaldvogel/liblinear/FeatureNode.java @@ -2,11 +2,17 @@ public class FeatureNode implements Feature { - public final int index; - public double value; + public final int index; + public double value; - public FeatureNode( final int index, final double value ) { - if (index < 0) throw new IllegalArgumentException("index must be >= 0"); + public FeatureNode(int index, double value) { + this(index, value, true); + } + + FeatureNode(int index, double value, boolean validateIndex) { + if (validateIndex && index <= 0) { + throw new IllegalArgumentException("index must be > 0"); + } this.index = index; this.value = value; } @@ -14,6 +20,7 @@ public FeatureNode( final int index, final double value ) { /** * @since 1.9 */ + @Override public int getIndex() { return index; } @@ -21,6 +28,7 @@ public int getIndex() { /** * @since 1.9 */ + @Override public double getValue() { return value; } @@ -28,6 +36,7 @@ public double getValue() { /** * @since 1.9 */ + @Override public void setValue(double value) { this.value = value; } @@ -45,12 +54,17 @@ public int hashCode() { @Override public boolean equals(Object obj) { - if (this == obj) return true; - if (obj == null) return false; - if (getClass() != obj.getClass()) return false; + if (this == obj) + return true; + if (obj == null) + return false; + if (getClass() != obj.getClass()) + return false; FeatureNode other = (FeatureNode)obj; - if (index != other.index) return false; - if (Double.doubleToLongBits(value) != Double.doubleToLongBits(other.value)) return false; + if (index != other.index) + return false; + if (Double.doubleToLongBits(value) != Double.doubleToLongBits(other.value)) + return false; return true; } diff --git a/src/main/java/de/bwaldvogel/liblinear/Function.java b/src/main/java/de/bwaldvogel/liblinear/Function.java index 2ad02f9..9645f2d 100644 --- a/src/main/java/de/bwaldvogel/liblinear/Function.java +++ b/src/main/java/de/bwaldvogel/liblinear/Function.java @@ -1,6 +1,5 @@ package de.bwaldvogel.liblinear; -// origin: tron.h interface Function { double fun(double[] w); @@ -10,4 +9,40 @@ interface Function { void Hv(double[] s, double[] Hs); int get_nr_variable(); + + void get_diag_preconditioner(double[] M); + + // Note: This implementation is unused but function::linesearch_and_update + // from upstream newton.cpp seems to be unused as well + default double linesearch_and_update(double[] w, double[] s, MutableDouble f, double[] g, double alpha) { + double gTs = 0; + double eta = 0.01; + int n = get_nr_variable(); + int max_num_linesearch = 20; + double[] w_new = new double[n]; + double fold = f.get(); + + for (int i = 0; i < n; i++) + gTs += s[i] * g[i]; + + int num_linesearch = 0; + for (num_linesearch = 0; num_linesearch < max_num_linesearch; num_linesearch++) { + for (int i = 0; i < n; i++) + w_new[i] = w[i] + alpha * s[i]; + f.set(fun(w_new)); + if (f.get() - fold <= eta * alpha * gTs) + break; + else + alpha *= 0.5; + } + + if (num_linesearch >= max_num_linesearch) { + f.set(fold); + return 0; + } else + System.arraycopy(w_new, 0, w, 0, n); + + return alpha; + } + } diff --git a/src/main/java/de/bwaldvogel/liblinear/Heap.java b/src/main/java/de/bwaldvogel/liblinear/Heap.java new file mode 100644 index 0000000..9e1d3d9 --- /dev/null +++ b/src/main/java/de/bwaldvogel/liblinear/Heap.java @@ -0,0 +1,70 @@ +package de.bwaldvogel.liblinear; + +class Heap { + + enum HeapType { + MIN, MAX, + } + + + private int size; + private final HeapType type; + private final Feature[] a; + + Heap(int max_size, HeapType type) { + this.size = 0; + this.a = new Feature[max_size]; + this.type = type; + } + + private boolean cmp(Feature left, Feature right) { + if (this.type == HeapType.MIN) { + return left.getValue() > right.getValue(); + } else { + return left.getValue() < right.getValue(); + } + } + + int size() { + return size; + } + + void push(Feature node) { + a[size] = node; + size++; + int i = size - 1; + while (i > 0) { + int p = (i - 1) / 2; + if (cmp(a[p], a[i])) { + Linear.swap(a, i, p); + i = p; + } else { + break; + } + } + } + + void pop() { + size--; + a[0] = a[size]; + int i = 0; + while (i * 2 + 1 < size) { + int l = i * 2 + 1; + int r = i * 2 + 2; + if (r < size && cmp(a[l], a[r])) { + l = r; + } + if (cmp(a[i], a[l])) { + Linear.swap(a, i, l); + i = l; + } else { + break; + } + } + } + + Feature top() { + return this.a[0]; + } + +} diff --git a/src/main/java/de/bwaldvogel/liblinear/IntArrayPointer.java b/src/main/java/de/bwaldvogel/liblinear/IntArrayPointer.java index e708b80..b9b0862 100644 --- a/src/main/java/de/bwaldvogel/liblinear/IntArrayPointer.java +++ b/src/main/java/de/bwaldvogel/liblinear/IntArrayPointer.java @@ -4,15 +4,15 @@ final class IntArrayPointer { private final int[] _array; - private int _offset; + private int _offset; - - public void setOffset(int offset) { - if (offset < 0 || offset >= _array.length) throw new IllegalArgumentException("offset must be between 0 and the length of the array"); + void setOffset(int offset) { + if (offset < 0 || offset >= _array.length) + throw new IllegalArgumentException("offset must be between 0 and the length of the array"); _offset = offset; } - public IntArrayPointer( final int[] array, final int offset ) { + IntArrayPointer(final int[] array, final int offset) { _array = array; setOffset(offset); } diff --git a/src/main/java/de/bwaldvogel/liblinear/InvalidInputDataException.java b/src/main/java/de/bwaldvogel/liblinear/InvalidInputDataException.java index 7a9a66c..b1676f4 100644 --- a/src/main/java/de/bwaldvogel/liblinear/InvalidInputDataException.java +++ b/src/main/java/de/bwaldvogel/liblinear/InvalidInputDataException.java @@ -1,57 +1,29 @@ package de.bwaldvogel.liblinear; -import java.io.File; - public class InvalidInputDataException extends Exception { private static final long serialVersionUID = 2945131732407207308L; - private final int _line; - - private File _file; + private final int _line; - public InvalidInputDataException( String message, File file, int line ) { + public InvalidInputDataException(String message, int line) { super(message); - _file = file; _line = line; } - public InvalidInputDataException( String message, String filename, int line ) { - this(message, new File(filename), line); - } - - public InvalidInputDataException( String message, File file, int lineNr, Exception cause ) { + public InvalidInputDataException(String message, int lineNr, Exception cause) { super(message, cause); - _file = file; _line = lineNr; } - public InvalidInputDataException( String message, String filename, int lineNr, Exception cause ) { - this(message, new File(filename), lineNr, cause); - } - - public File getFile() { - return _file; - } - - /** - * This methods returns the path of the file. - * The method name might be misleading. - * - * @deprecated use {@link #getFile()} instead - */ - public String getFilename() { - return _file.getPath(); - } - public int getLine() { return _line; } @Override public String toString() { - return super.toString() + " (" + _file + ":" + _line + ")"; + return super.toString() + " (line " + getLine() + ")"; } } diff --git a/src/main/java/de/bwaldvogel/liblinear/L2R_ErmFunction.java b/src/main/java/de/bwaldvogel/liblinear/L2R_ErmFunction.java new file mode 100644 index 0000000..844a89f --- /dev/null +++ b/src/main/java/de/bwaldvogel/liblinear/L2R_ErmFunction.java @@ -0,0 +1,128 @@ +package de.bwaldvogel.liblinear; + +// L2-regularized empirical risk minimization +// min_w w^Tw/2 + \sum C_i \xi(w^Tx_i), where \xi() is the loss +abstract class L2R_ErmFunction implements Function { + + final double[] C; + final Problem prob; + double[] wx; + double[] tmp; // a working array + private double wTw; + final boolean regularize_bias; + + L2R_ErmFunction(Problem prob, Parameter parameter, double[] C) { + int l = prob.l; + + this.prob = prob; + + wx = new double[l]; + tmp = new double[l]; + this.C = C; + this.regularize_bias = parameter.regularize_bias; + } + + void Xv(double[] v, double[] Xv) { + int i; + int l = prob.l; + Feature[][] x = prob.x; + + for (i = 0; i < l; i++) + Xv[i] = SparseOperator.dot(v, x[i]); + } + + void XTv(double[] v, double[] XTv) { + int l = prob.l; + int w_size = get_nr_variable(); + Feature[][] x = prob.x; + + for (int i = 0; i < w_size; i++) + XTv[i] = 0; + + for (int i = 0; i < l; i++) { + SparseOperator.axpy(v[i], x[i], XTv); + } + } + + protected abstract double C_times_loss(int i, double wx_i); + + @Override + public double fun(double[] w) { + int i; + double f = 0; + int l = prob.l; + int w_size = get_nr_variable(); + + wTw = 0; + Xv(w, wx); + + for (i = 0; i < w_size; i++) + wTw += w[i] * w[i]; + if (!regularize_bias) + wTw -= w[w_size - 1] * w[w_size - 1]; + for (i = 0; i < l; i++) + f += C_times_loss(i, wx[i]); + f = f + 0.5 * wTw; + + return (f); + } + + @Override + public int get_nr_variable() { + return prob.n; + } + + // On entry *f must be the function value of w + // On exit w is updated and *f is the new function value + @Override + public double linesearch_and_update(double[] w, double[] s, MutableDouble f, double[] g, double alpha) { + int i; + int l = prob.l; + double sTs = 0; + double wTs = 0; + double gTs = 0; + double eta = 0.01; + int w_size = get_nr_variable(); + int max_num_linesearch = 20; + double fold = f.get(); + Xv(s, tmp); + + for (i = 0; i < w_size; i++) { + sTs += s[i] * s[i]; + wTs += s[i] * w[i]; + gTs += s[i] * g[i]; + } + if (!regularize_bias) { + // bias not used in calculating (w + \alpha s)^T (w + \alpha s) + sTs -= s[w_size - 1] * s[w_size - 1]; + wTs -= s[w_size - 1] * w[w_size - 1]; + } + + int num_linesearch = 0; + for (num_linesearch = 0; num_linesearch < max_num_linesearch; num_linesearch++) { + double loss = 0; + for (i = 0; i < l; i++) { + double inner_product = tmp[i] * alpha + wx[i]; + loss += C_times_loss(i, inner_product); + } + f.set(loss + (alpha * alpha * sTs + wTw) / 2.0 + alpha * wTs); + if (f.get() - fold <= eta * alpha * gTs) { + for (i = 0; i < l; i++) + wx[i] += alpha * tmp[i]; + break; + } else + alpha *= 0.5; + } + + if (num_linesearch >= max_num_linesearch) { + f.set(fold); + return 0; + } else + for (i = 0; i < w_size; i++) + w[i] += alpha * s[i]; + + wTw += alpha * alpha * sTs + 2 * alpha * wTs; + return alpha; + } + +} diff --git a/src/main/java/de/bwaldvogel/liblinear/L2R_L2_SvcFunction.java b/src/main/java/de/bwaldvogel/liblinear/L2R_L2_SvcFunction.java index 0a857a0..a19303c 100644 --- a/src/main/java/de/bwaldvogel/liblinear/L2R_L2_SvcFunction.java +++ b/src/main/java/de/bwaldvogel/liblinear/L2R_L2_SvcFunction.java @@ -1,114 +1,96 @@ package de.bwaldvogel.liblinear; -class L2R_L2_SvcFunction implements Function { +class L2R_L2_SvcFunction extends L2R_ErmFunction { - protected final Problem prob; - protected final double[] C; - protected final int[] I; - protected final double[] z; + protected final int[] I; + protected int sizeI; - protected int sizeI; - - public L2R_L2_SvcFunction( Problem prob, double[] C ) { - int l = prob.l; - - this.prob = prob; + public L2R_L2_SvcFunction(Problem prob, Parameter param, double[] C) { + super(prob, param, C); + I = new int[prob.l]; + } - z = new double[l]; - I = new int[l]; - this.C = C; + @Override + protected double C_times_loss(int i, double wx_i) { + double d = 1 - prob.y[i] * wx_i; + if (d > 0) + return C[i] * d * d; + else + return 0; } - public double fun(double[] w) { + @Override + public void grad(double[] w, double[] g) { int i; - double f = 0; double[] y = prob.y; int l = prob.l; int w_size = get_nr_variable(); - Xv(w, z); - - for (i = 0; i < w_size; i++) - f += w[i] * w[i]; - f /= 2.0; + sizeI = 0; for (i = 0; i < l; i++) { - z[i] = y[i] * z[i]; - double d = 1 - z[i]; - if (d > 0) f += C[i] * d * d; + tmp[i] = wx[i] * y[i]; + if (tmp[i] < 1) { + tmp[sizeI] = C[i] * y[i] * (tmp[i] - 1); + I[sizeI] = i; + sizeI++; + } } + subXTv(tmp, g); - return (f); - } - - public int get_nr_variable() { - return prob.n; + for (i = 0; i < w_size; i++) + g[i] = w[i] + 2 * g[i]; + if (!regularize_bias) + g[w_size - 1] -= w[w_size - 1]; } - public void grad(double[] w, double[] g) { - double[] y = prob.y; - int l = prob.l; + @Override + public void get_diag_preconditioner(double[] M) { int w_size = get_nr_variable(); + Feature[][] x = prob.x; - sizeI = 0; - for (int i = 0; i < l; i++) { - if (z[i] < 1) { - z[sizeI] = C[i] * y[i] * (z[i] - 1); - I[sizeI] = i; - sizeI++; + for (int i = 0; i < w_size; i++) + M[i] = 1; + if (!regularize_bias) + M[w_size - 1] = 0; + + for (int i = 0; i < sizeI; i++) { + int idx = I[i]; + for (Feature s : x[idx]) { + M[s.getIndex() - 1] += s.getValue() * s.getValue() * C[idx] * 2; } } - subXTv(z, g); - - for (int i = 0; i < w_size; i++) - g[i] = w[i] + 2 * g[i]; } + @Override public void Hv(double[] s, double[] Hs) { int i; int w_size = get_nr_variable(); - double[] wa = new double[sizeI]; + Feature[][] x = prob.x; - subXv(s, wa); - for (i = 0; i < sizeI; i++) - wa[i] = C[I[i]] * wa[i]; + for (i = 0; i < w_size; i++) + Hs[i] = 0; + for (i = 0; i < sizeI; i++) { + Feature[] xi = x[I[i]]; + double xTs = SparseOperator.dot(s, xi); + xTs = C[I[i]] * xTs; - subXTv(wa, Hs); + SparseOperator.axpy(xTs, xi, Hs); + } for (i = 0; i < w_size; i++) Hs[i] = s[i] + 2 * Hs[i]; + if (!regularize_bias) + Hs[w_size - 1] -= s[w_size - 1]; } protected void subXTv(double[] v, double[] XTv) { int i; int w_size = get_nr_variable(); + Feature[][] x = prob.x; for (i = 0; i < w_size; i++) XTv[i] = 0; - - for (i = 0; i < sizeI; i++) { - for (Feature s : prob.x[I[i]]) { - XTv[s.getIndex() - 1] += v[i] * s.getValue(); - } - } - } - - private void subXv(double[] v, double[] Xv) { - - for (int i = 0; i < sizeI; i++) { - Xv[i] = 0; - for (Feature s : prob.x[I[i]]) { - Xv[i] += v[s.getIndex() - 1] * s.getValue(); - } - } - } - - protected void Xv(double[] v, double[] Xv) { - - for (int i = 0; i < prob.l; i++) { - Xv[i] = 0; - for (Feature s : prob.x[i]) { - Xv[i] += v[s.getIndex() - 1] * s.getValue(); - } - } + for (i = 0; i < sizeI; i++) + SparseOperator.axpy(v[i], x[I[i]], XTv); } } diff --git a/src/main/java/de/bwaldvogel/liblinear/L2R_L2_SvrFunction.java b/src/main/java/de/bwaldvogel/liblinear/L2R_L2_SvrFunction.java index 88f250a..25c9ff5 100644 --- a/src/main/java/de/bwaldvogel/liblinear/L2R_L2_SvrFunction.java +++ b/src/main/java/de/bwaldvogel/liblinear/L2R_L2_SvrFunction.java @@ -5,63 +5,53 @@ */ public class L2R_L2_SvrFunction extends L2R_L2_SvcFunction { - private double p; + private final double p; - public L2R_L2_SvrFunction( Problem prob, double[] C, double p ) { - super(prob, C); - this.p = p; + public L2R_L2_SvrFunction(Problem prob, Parameter param, double[] C) { + super(prob, param, C); + this.p = param.p; } @Override - public double fun(double[] w) { - double f = 0; - double[] y = prob.y; - int l = prob.l; - int w_size = get_nr_variable(); - double d; - - Xv(w, z); - - for (int i = 0; i < w_size; i++) - f += w[i] * w[i]; - f /= 2; - for (int i = 0; i < l; i++) { - d = z[i] - y[i]; - if (d < -p) - f += C[i] * (d + p) * (d + p); - else if (d > p) f += C[i] * (d - p) * (d - p); - } - - return f; + protected double C_times_loss(int i, double wx_i) { + double d = wx_i - prob.y[i]; + if (d < -p) + return C[i] * (d + p) * (d + p); + else if (d > p) + return C[i] * (d - p) * (d - p); + return 0; } @Override public void grad(double[] w, double[] g) { + int i; double[] y = prob.y; int l = prob.l; int w_size = get_nr_variable(); + double d; sizeI = 0; - for (int i = 0; i < l; i++) { - double d = z[i] - y[i]; + for (i = 0; i < l; i++) { + d = wx[i] - y[i]; // generate index set I if (d < -p) { - z[sizeI] = C[i] * (d + p); + tmp[sizeI] = C[i] * (d + p); I[sizeI] = i; sizeI++; } else if (d > p) { - z[sizeI] = C[i] * (d - p); + tmp[sizeI] = C[i] * (d - p); I[sizeI] = i; sizeI++; } } - subXTv(z, g); + subXTv(tmp, g); - for (int i = 0; i < w_size; i++) + for (i = 0; i < w_size; i++) g[i] = w[i] + 2 * g[i]; - + if (!regularize_bias) + g[w_size - 1] -= w[w_size - 1]; } } diff --git a/src/main/java/de/bwaldvogel/liblinear/L2R_LrFunction.java b/src/main/java/de/bwaldvogel/liblinear/L2R_LrFunction.java index 6b58ae0..69b0f16 100644 --- a/src/main/java/de/bwaldvogel/liblinear/L2R_LrFunction.java +++ b/src/main/java/de/bwaldvogel/liblinear/L2R_LrFunction.java @@ -1,107 +1,83 @@ package de.bwaldvogel.liblinear; -class L2R_LrFunction implements Function { +class L2R_LrFunction extends L2R_ErmFunction { - private final double[] C; - private final double[] z; private final double[] D; - private final Problem prob; - public L2R_LrFunction( Problem prob, double[] C ) { + L2R_LrFunction(Problem prob, Parameter param, double[] C) { + super(prob, param, C); int l = prob.l; - - this.prob = prob; - - z = new double[l]; D = new double[l]; - this.C = C; } + @Override + protected double C_times_loss(int i, double wx_i) { + double ywx_i = wx_i * prob.y[i]; + if (ywx_i >= 0) + return C[i] * Math.log(1 + Math.exp(-ywx_i)); + else + return C[i] * (-ywx_i + Math.log(1 + Math.exp(ywx_i))); + } - private void Xv(double[] v, double[] Xv) { + @Override + public void grad(double[] w, double[] g) { + int i; + double[] y = prob.y; + int l = prob.l; + int w_size = get_nr_variable(); - for (int i = 0; i < prob.l; i++) { - Xv[i] = 0; - for (Feature s : prob.x[i]) { - Xv[i] += v[s.getIndex() - 1] * s.getValue(); - } + for (i = 0; i < l; i++) { + tmp[i] = 1 / (1 + Math.exp(-y[i] * wx[i])); + D[i] = tmp[i] * (1 - tmp[i]); + tmp[i] = C[i] * (tmp[i] - 1) * y[i]; } + XTv(tmp, g); + + for (i = 0; i < w_size; i++) + g[i] = w[i] + g[i]; + if (!regularize_bias) + g[w_size - 1] -= w[w_size - 1]; } - private void XTv(double[] v, double[] XTv) { + @Override + public void get_diag_preconditioner(double[] M) { int l = prob.l; int w_size = get_nr_variable(); Feature[][] x = prob.x; for (int i = 0; i < w_size; i++) - XTv[i] = 0; + M[i] = 1; + if (!regularize_bias) + M[w_size - 1] = 0; for (int i = 0; i < l; i++) { - for (Feature s : x[i]) { - XTv[s.getIndex() - 1] += v[i] * s.getValue(); + for (Feature xi : x[i]) { + M[xi.getIndex() - 1] += xi.getValue() * xi.getValue() * C[i] * D[i]; } } } - - public double fun(double[] w) { + @Override + public void Hv(double[] s, double[] Hs) { int i; - double f = 0; - double[] y = prob.y; int l = prob.l; int w_size = get_nr_variable(); - - Xv(w, z); + Feature[][] x = prob.x; for (i = 0; i < w_size; i++) - f += w[i] * w[i]; - f /= 2.0; + Hs[i] = 0; for (i = 0; i < l; i++) { - double yz = y[i] * z[i]; - if (yz >= 0) - f += C[i] * Math.log(1 + Math.exp(-yz)); - else - f += C[i] * (-yz + Math.log(1 + Math.exp(yz))); - } - - return (f); - } + Feature[] xi = x[i]; + double xTs = SparseOperator.dot(s, xi); - public void grad(double[] w, double[] g) { - int i; - double[] y = prob.y; - int l = prob.l; - int w_size = get_nr_variable(); + xTs = C[i] * D[i] * xTs; - for (i = 0; i < l; i++) { - z[i] = 1 / (1 + Math.exp(-y[i] * z[i])); - D[i] = z[i] * (1 - z[i]); - z[i] = C[i] * (z[i] - 1) * y[i]; + SparseOperator.axpy(xTs, xi, Hs); } - XTv(z, g); - - for (i = 0; i < w_size; i++) - g[i] = w[i] + g[i]; - } - - public void Hv(double[] s, double[] Hs) { - int i; - int l = prob.l; - int w_size = get_nr_variable(); - double[] wa = new double[l]; - - Xv(s, wa); - for (i = 0; i < l; i++) - wa[i] = C[i] * D[i] * wa[i]; - - XTv(wa, Hs); for (i = 0; i < w_size; i++) Hs[i] = s[i] + Hs[i]; - // delete[] wa; - } - - public int get_nr_variable() { - return prob.n; + if (!regularize_bias) + Hs[w_size - 1] -= s[w_size - 1]; } } diff --git a/src/main/java/de/bwaldvogel/liblinear/Linear.java b/src/main/java/de/bwaldvogel/liblinear/Linear.java index c9e0818..0fb809b 100644 --- a/src/main/java/de/bwaldvogel/liblinear/Linear.java +++ b/src/main/java/de/bwaldvogel/liblinear/Linear.java @@ -1,60 +1,66 @@ package de.bwaldvogel.liblinear; +import static de.bwaldvogel.liblinear.SolverType.*; + import java.io.BufferedReader; -import java.io.BufferedWriter; -import java.io.Closeable; import java.io.EOFException; import java.io.File; -import java.io.FileInputStream; -import java.io.FileOutputStream; import java.io.IOException; -import java.io.InputStreamReader; -import java.io.OutputStreamWriter; import java.io.PrintStream; import java.io.Reader; import java.io.Writer; import java.nio.charset.Charset; +import java.nio.charset.StandardCharsets; +import java.nio.file.Files; +import java.nio.file.Path; +import java.util.Arrays; import java.util.Formatter; import java.util.Locale; import java.util.Random; import java.util.regex.Pattern; +import de.bwaldvogel.liblinear.Heap.HeapType; + /** *

Java port of liblinear

* - *

The usage should be pretty similar to the C version of liblinear.

- *

Please consider reading the README file of liblinear.

+ *

The usage should be pretty similar to the C version of liblinear.

+ *

Please consider reading the README file of liblinear.

* *

The port was done by Benedikt Waldvogel (mail at bwaldvogel.de)

* - * @version 1.92 + * @version 2.44 */ public class Linear { - static final Charset FILE_CHARSET = Charset.forName("ISO-8859-1"); + static final int VERSION = 244; - static final Locale DEFAULT_LOCALE = Locale.ENGLISH; + static final Charset FILE_CHARSET = StandardCharsets.ISO_8859_1; - private static Object OUTPUT_MUTEX = new Object(); - private static PrintStream DEBUG_OUTPUT = System.out; + private static final Locale DEFAULT_LOCALE = Locale.ENGLISH; - private static final long DEFAULT_RANDOM_SEED = 0L; - static Random random = new Random(DEFAULT_RANDOM_SEED); + private static final Object OUTPUT_MUTEX = new Object(); + private static PrintStream DEBUG_OUTPUT = System.out; /** * @param target predicted classes */ public static void crossValidation(Problem prob, Parameter param, int nr_fold, double[] target) { int i; - int[] fold_start = new int[nr_fold + 1]; int l = prob.l; int[] perm = new int[l]; + if (nr_fold > l) { + nr_fold = l; + System.err.println("WARNING: # folds > # data. Will use # folds = # data instead (i.e., leave-one-out cross validation)"); + } + int[] fold_start = new int[nr_fold + 1]; + for (i = 0; i < l; i++) perm[i] = i; for (i = 0; i < l; i++) { - int j = i + random.nextInt(l - i); + int j = i + param.random.nextInt(l - i); swap(perm, i, j); } for (i = 0; i <= nr_fold; i++) @@ -89,6 +95,98 @@ public static void crossValidation(Problem prob, Parameter param, int nr_fold, d } } + public static ParameterSearchResult findParameters(Problem prob, Parameter param, int nr_fold, double start_C, double start_p) { + double best_C = Double.NaN; + double best_score = Double.NaN; + // prepare CV folds + int i; + int l = prob.l; + int[] perm = new int[l]; + Problem[] subprob = new Problem[nr_fold]; + + if (nr_fold > l) { + nr_fold = l; + System.err.println("WARNING: # folds > # data. Will use # folds = # data instead (i.e., leave-one-out cross validation)"); + } + int[] fold_start = new int[nr_fold + 1]; + for (i = 0; i < l; i++) + perm[i] = i; + for (i = 0; i < l; i++) { + int j = i + param.random.nextInt(l - i); + swap(perm, i, j); + } + for (i = 0; i <= nr_fold; i++) + fold_start[i] = i * l / nr_fold; + + for (i = 0; i < nr_fold; i++) { + int begin = fold_start[i]; + int end = fold_start[i + 1]; + int j, k; + + assert subprob[i] == null; + subprob[i] = new Problem(); + subprob[i].bias = prob.bias; + subprob[i].n = prob.n; + subprob[i].l = l - (end - begin); + subprob[i].x = new Feature[subprob[i].l][]; + subprob[i].y = new double[subprob[i].l]; + + k = 0; + for (j = 0; j < begin; j++) { + subprob[i].x[k] = prob.x[perm[j]]; + subprob[i].y[k] = prob.y[perm[j]]; + ++k; + } + for (j = end; j < l; j++) { + subprob[i].x[k] = prob.x[perm[j]]; + subprob[i].y[k] = prob.y[perm[j]]; + ++k; + } + } + + Parameter param_tmp = param.clone(); + double best_p = -1; + if (param.getSolverType() == L2R_LR || param.getSolverType() == L2R_L2LOSS_SVC) { + if (start_C <= 0) + start_C = calc_start_C(prob, param_tmp); + double max_C = 1024; + start_C = Math.min(start_C, max_C); + ParameterCSearchResult best_tmp = find_parameter_C(prob, param_tmp, start_C, max_C, fold_start, perm, subprob, nr_fold); + best_C = best_tmp.getBestC(); + best_score = best_tmp.getBestScore(); + } else if (param.getSolverType() == L2R_L2LOSS_SVR) { + double max_p = calc_max_p(prob); + int num_p_steps = 20; + double max_C = 1048576; + best_score = Double.POSITIVE_INFINITY; + i = num_p_steps - 1; + if (start_p > 0) + i = Math.min((int)(start_p / (max_p / num_p_steps)), i); + for (; i >= 0; i--) { + param_tmp.p = i * max_p / num_p_steps; + double start_C_tmp; + if (start_C <= 0) + start_C_tmp = calc_start_C(prob, param_tmp); + else + start_C_tmp = start_C; + start_C_tmp = Math.min(start_C_tmp, max_C); + + ParameterCSearchResult best_tmp = find_parameter_C(prob, param_tmp, start_C_tmp, max_C, fold_start, perm, subprob, nr_fold); + + if (best_tmp.getBestScore() < best_score) { + best_p = param_tmp.p; + best_C = best_tmp.getBestC(); + best_score = best_tmp.getBestScore(); + } + } + } else { + // Note: The upstream version of liblinear does *NOT* throw an exception in this case + throw new IllegalArgumentException("Unsupported solver: " + param.getSolverType()); + } + + return new ParameterSearchResult(best_C, best_score, best_p); + } + /** used as complex return type */ private static class GroupClassesReturn { @@ -97,7 +195,7 @@ private static class GroupClassesReturn { final int nr_class; final int[] start; - GroupClassesReturn( int nr_class, int[] label, int[] start, int[] count ) { + GroupClassesReturn(int nr_class, int[] label, int[] start, int[] count) { this.nr_class = nr_class; this.label = label; this.start = start; @@ -128,8 +226,8 @@ private static GroupClassesReturn groupClasses(Problem prob, int[] perm) { if (j == nr_class) { if (nr_class == max_nr_class) { max_nr_class *= 2; - label = copyOf(label, max_nr_class); - count = copyOf(count, max_nr_class); + label = Arrays.copyOf(label, max_nr_class); + count = Arrays.copyOf(count, max_nr_class); } label[nr_class] = this_label; count[nr_class] = 1; @@ -137,6 +235,22 @@ private static GroupClassesReturn groupClasses(Problem prob, int[] perm) { } } + // + // Labels are ordered by their first occurrence in the training set. + // However, for two-class sets with -1/+1 labels and -1 appears first, + // we swap labels to ensure that internally the binary SVM has positive data corresponding to the +1 instances. + // + if (nr_class == 2 && label[0] == -1 && label[1] == 1) { + swap(label, 0, 1); + swap(count, 0, 1); + for (i = 0; i < l; i++) { + if (data_label[i] == 0) + data_label[i] = 1; + else + data_label[i] = 0; + } + } + int[] start = new int[nr_class]; start[0] = 0; for (i = 1; i < nr_class; i++) @@ -154,7 +268,8 @@ private static GroupClassesReturn groupClasses(Problem prob, int[] perm) { static void info(String message) { synchronized (OUTPUT_MUTEX) { - if (DEBUG_OUTPUT == null) return; + if (DEBUG_OUTPUT == null) + return; DEBUG_OUTPUT.printf(message); DEBUG_OUTPUT.flush(); } @@ -162,7 +277,8 @@ static void info(String message) { static void info(String format, Object... args) { synchronized (OUTPUT_MUTEX) { - if (DEBUG_OUTPUT == null) return; + if (DEBUG_OUTPUT == null) + return; DEBUG_OUTPUT.printf(format, args); DEBUG_OUTPUT.flush(); } @@ -174,7 +290,8 @@ static void info(String format, Object... args) { * @throws NumberFormatException see {@link Double#parseDouble(String)} */ static double atof(String s) { - if (s == null || s.length() < 1) throw new IllegalArgumentException("Can't convert empty string to integer"); + if (s == null || s.length() < 1) + throw new IllegalArgumentException("Can't convert empty string to integer"); double d = Double.parseDouble(s); if (Double.isNaN(d) || Double.isInfinite(d)) { throw new IllegalArgumentException("NaN or Infinity in input: " + s); @@ -188,30 +305,14 @@ static double atof(String s) { * @throws NumberFormatException see {@link Integer#parseInt(String)} */ static int atoi(String s) throws NumberFormatException { - if (s == null || s.length() < 1) throw new IllegalArgumentException("Can't convert empty string to integer"); + if (s == null || s.length() < 1) + throw new IllegalArgumentException("Can't convert empty string to integer"); // Integer.parseInt doesn't accept '+' prefixed strings - if (s.charAt(0) == '+') s = s.substring(1); + if (s.charAt(0) == '+') + s = s.substring(1); return Integer.parseInt(s); } - /** - * Java5 'backport' of Arrays.copyOf - */ - public static double[] copyOf(double[] original, int newLength) { - double[] copy = new double[newLength]; - System.arraycopy(original, 0, copy, 0, Math.min(original.length, newLength)); - return copy; - } - - /** - * Java5 'backport' of Arrays.copyOf - */ - public static int[] copyOf(int[] original, int newLength) { - int[] copy = new int[newLength]; - System.arraycopy(original, 0, copy, 0, Math.min(original.length, newLength)); - return copy; - } - /** * Loads the model from inputReader. * It uses {@link java.util.Locale#ENGLISH} for number formatting. @@ -232,22 +333,19 @@ public static Model loadModel(Reader inputReader) throws IOException { reader = new BufferedReader(inputReader); } - String line = null; + String line; while ((line = reader.readLine()) != null) { String[] split = whitespace.split(line); if (split[0].equals("solver_type")) { - SolverType solver = SolverType.valueOf(split[1]); - if (solver == null) { - throw new RuntimeException("unknown solver type"); - } - model.solverType = solver; + model.solverType = SolverType.valueOf(split[1]); } else if (split[0].equals("nr_class")) { model.nr_class = atoi(split[1]); - Integer.parseInt(split[1]); } else if (split[0].equals("nr_feature")) { model.nr_feature = atoi(split[1]); } else if (split[0].equals("bias")) { model.bias = atof(split[1]); + } else if (split[0].equals("rho")) { + model.rho = atof(split[1]); } else if (split[0].equals("w")) { break; } else if (split[0].equals("label")) { @@ -261,10 +359,12 @@ public static Model loadModel(Reader inputReader) throws IOException { } int w_size = model.nr_feature; - if (model.bias >= 0) w_size++; + if (model.bias >= 0) + w_size++; int nr_w = model.nr_class; - if (model.nr_class == 2 && model.solverType != SolverType.MCSVM_CS) nr_w = 1; + if (model.nr_class == 2 && model.solverType != MCSVM_CS) + nr_w = 1; model.w = new double[w_size * nr_w]; int[] buffer = new int[128]; @@ -281,6 +381,11 @@ public static Model loadModel(Reader inputReader) throws IOException { model.w[i * nr_w + j] = atof(new String(buffer, 0, b)); break; } else { + if (b >= buffer.length) { + throw new RuntimeException("illegal weight in model file at index " + (i * nr_w + j) + ", with string content '" + + new String(buffer, 0, buffer.length) + "', is not terminated " + + "with a whitespace character, or is longer than expected (" + buffer.length + " characters max)."); + } buffer[b++] = ch; } } @@ -292,23 +397,23 @@ public static Model loadModel(Reader inputReader) throws IOException { /** * Loads the model from the file with ISO-8859-1 charset. - * It uses {@link java.util.Locale#ENGLISH} for number formatting. + * It uses {@link Locale#ENGLISH} for number formatting. + * + * @deprecated use {@link Linear#loadModel(Path)} instead */ + @Deprecated public static Model loadModel(File modelFile) throws IOException { - BufferedReader inputReader = new BufferedReader(new InputStreamReader(new FileInputStream(modelFile), FILE_CHARSET)); - try { - return loadModel(inputReader); - } - finally { - inputReader.close(); - } + return loadModel(modelFile.toPath()); } - static void closeQuietly(Closeable c) { - if (c == null) return; - try { - c.close(); - } catch (Throwable t) {} + /** + * Loads the model from the file with ISO-8859-1 charset. + * It uses {@link java.util.Locale#ENGLISH} for number formatting. + */ + public static Model loadModel(Path modelPath) throws IOException { + try (Reader inputReader = Files.newBufferedReader(modelPath, FILE_CHARSET)) { + return loadModel(inputReader); + } } public static double predict(Model model, Feature[] x) { @@ -369,7 +474,7 @@ public static double predictValues(Model model, Feature[] x, double[] dec_values double[] w = model.w; int nr_w; - if (model.nr_class == 2 && model.solverType != SolverType.MCSVM_CS) + if (model.nr_class == 2 && model.solverType != MCSVM_CS) nr_w = 1; else nr_w = model.nr_class; @@ -386,16 +491,22 @@ public static double predictValues(Model model, Feature[] x, double[] dec_values } } } + if (model.solverType.isOneClass()) { + dec_values[0] -= model.rho; + } if (model.nr_class == 2) { if (model.solverType.isSupportVectorRegression()) return dec_values[0]; + else if (model.solverType.isOneClass()) + return (dec_values[0] > 0) ? 1 : -1; else return (dec_values[0] > 0) ? model.label[0] : model.label[1]; } else { int dec_max_idx = 0; for (int i = 1; i < model.nr_class; i++) { - if (dec_values[i] > dec_values[dec_max_idx]) dec_max_idx = i; + if (dec_values[i] > dec_values[dec_max_idx]) + dec_max_idx = i; } return model.label[dec_max_idx]; } @@ -404,7 +515,8 @@ public static double predictValues(Model model, Feature[] x, double[] dec_values static void printf(Formatter formatter, String format, Object... args) throws IOException { formatter.format(format, args); IOException ioException = formatter.ioException(); - if (ioException != null) throw ioException; + if (ioException != null) + throw ioException; } /** @@ -416,13 +528,14 @@ static void printf(Formatter formatter, String format, Object... args) throws IO public static void saveModel(Writer modelOutput, Model model) throws IOException { int nr_feature = model.nr_feature; int w_size = nr_feature; - if (model.bias >= 0) w_size++; + if (model.bias >= 0) + w_size++; int nr_w = model.nr_class; - if (model.nr_class == 2 && model.solverType != SolverType.MCSVM_CS) nr_w = 1; + if (model.nr_class == 2 && model.solverType != MCSVM_CS) + nr_w = 1; - Formatter formatter = new Formatter(modelOutput, DEFAULT_LOCALE); - try { + try (Formatter formatter = new Formatter(modelOutput, DEFAULT_LOCALE)) { printf(formatter, "solver_type %s\n", model.solverType.name()); printf(formatter, "nr_class %d\n", model.nr_class); @@ -435,18 +548,21 @@ public static void saveModel(Writer modelOutput, Model model) throws IOException } printf(formatter, "nr_feature %d\n", nr_feature); - printf(formatter, "bias %.16g\n", model.bias); + printf(formatter, "bias %.17g\n", model.bias); + + if (model.solverType.isOneClass()) + printf(formatter, "rho %.17g\n", model.rho); printf(formatter, "w\n"); for (int i = 0; i < w_size; i++) { for (int j = 0; j < nr_w; j++) { double value = model.w[i * nr_w + j]; - /** this optimization is the reason for {@link Model#equals(double[], double[])} */ + /* this optimization is the reason for {@link Model#equals(double[], double[])} */ if (value == 0.0) { printf(formatter, "%d ", 0); } else { - printf(formatter, "%.16g ", value); + printf(formatter, "%.17g ", value); } } printf(formatter, "\n"); @@ -454,20 +570,30 @@ public static void saveModel(Writer modelOutput, Model model) throws IOException formatter.flush(); IOException ioException = formatter.ioException(); - if (ioException != null) throw ioException; - } - finally { - formatter.close(); + if (ioException != null) + throw ioException; } } /** * Writes the model to the file with ISO-8859-1 charset. - * It uses {@link java.util.Locale#ENGLISH} for number formatting. + * It uses {@link Locale#ENGLISH} for number formatting. + * + * @deprecated use {@link Linear#saveModel(Path, Model)} instead */ + @Deprecated public static void saveModel(File modelFile, Model model) throws IOException { - BufferedWriter modelOutput = new BufferedWriter(new OutputStreamWriter(new FileOutputStream(modelFile), FILE_CHARSET)); - saveModel(modelOutput, model); + saveModel(modelFile.toPath(), model); + } + + /** + * Writes the model to the file with ISO-8859-1 charset. + * It uses {@link java.util.Locale#ENGLISH} for number formatting. + */ + public static void saveModel(Path modelPath, Model model) throws IOException { + try (Writer modelWriter = Files.newBufferedWriter(modelPath, FILE_CHARSET)) { + saveModel(modelWriter, model); + } } /* @@ -489,13 +615,13 @@ private static int GETI(byte[] y, int i) { * D is a diagonal matrix * * In L1-SVM case: - * upper_bound_i = Cp if y_i = 1 - * upper_bound_i = Cn if y_i = -1 - * D_ii = 0 + * upper_bound_i = Cp if y_i = 1 + * upper_bound_i = Cn if y_i = -1 + * D_ii = 0 * In L2-SVM case: - * upper_bound_i = INF - * D_ii = 1/(2*Cp) if y_i = 1 - * D_ii = 1/(2*Cn) if y_i = -1 + * upper_bound_i = INF + * D_ii = 1/(2*Cp) if y_i = 1 + * D_ii = 1/(2*Cn) if y_i = -1 * * Given: * x, y, Cp, Cn @@ -503,16 +629,19 @@ private static int GETI(byte[] y, int i) { * * solution will be put in w * + * this function returns the number of iterations + * * See Algorithm 3 of Hsieh et al., ICML 2008 * */ - private static void solve_l2r_l1l2_svc(Problem prob, double[] w, double eps, double Cp, double Cn, SolverType solver_type) { + private static int solve_l2r_l1l2_svc(Problem prob, Parameter param, double[] w, double Cp, double Cn, int max_iter) { int l = prob.l; int w_size = prob.n; + double eps = param.eps; + SolverType solver_type = param.solverType; int i, s, iter = 0; double C, d, G; double[] QD = new double[l]; - int max_iter = 1000; int[] index = new int[l]; double[] alpha = new double[l]; byte[] y = new byte[l]; @@ -527,7 +656,7 @@ private static void solve_l2r_l1l2_svc(Problem prob, double[] w, double eps, dou // default solver_type: L2R_L2LOSS_SVC_DUAL double diag[] = new double[] {0.5 / Cn, 0, 0.5 / Cp}; double upper_bound[] = new double[] {Double.POSITIVE_INFINITY, 0, Double.POSITIVE_INFINITY}; - if (solver_type == SolverType.L2R_L1LOSS_SVC_DUAL) { + if (solver_type == L2R_L1LOSS_SVC_DUAL) { diag[0] = 0; diag[2] = 0; upper_bound[0] = Cn; @@ -552,11 +681,10 @@ private static void solve_l2r_l1l2_svc(Problem prob, double[] w, double eps, dou for (i = 0; i < l; i++) { QD[i] = diag[GETI(y, i)]; - for (Feature xi : prob.x[i]) { - double val = xi.getValue(); - QD[i] += val * val; - w[xi.getIndex() - 1] += y[i] * alpha[i] * val; - } + Feature[] xi = prob.x[i]; + QD[i] += SparseOperator.nrm2_sq(xi); + SparseOperator.axpy(y[i] * alpha[i], xi, w); + index[i] = i; } @@ -565,19 +693,16 @@ private static void solve_l2r_l1l2_svc(Problem prob, double[] w, double eps, dou PGmin_new = Double.POSITIVE_INFINITY; for (i = 0; i < active_size; i++) { - int j = i + random.nextInt(active_size - i); + int j = i + param.random.nextInt(active_size - i); swap(index, i, j); } for (s = 0; s < active_size; s++) { i = index[s]; - G = 0; byte yi = y[i]; + Feature[] xi = prob.x[i]; - for (Feature xi : prob.x[i]) { - G += w[xi.getIndex() - 1] * xi.getValue(); - } - G = G * yi - 1; + G = yi * SparseOperator.dot(w, xi) - 1; C = upper_bound[GETI(y, i)]; G += alpha[i] * diag[GETI(y, i)]; @@ -612,17 +737,16 @@ private static void solve_l2r_l1l2_svc(Problem prob, double[] w, double eps, dou double alpha_old = alpha[i]; alpha[i] = Math.min(Math.max(alpha[i] - G / QD[i], 0.0), C); d = (alpha[i] - alpha_old) * yi; - - for (Feature xi : prob.x[i]) { - w[xi.getIndex() - 1] += d * xi.getValue(); - } + SparseOperator.axpy(d, xi, w); } } iter++; - if (iter % 10 == 0) info("."); + if (iter % 10 == 0) + info("."); - if (PGmax_new - PGmin_new <= eps) { + if (PGmax_new - PGmin_new <= eps && + Math.abs(PGmax_new) <= eps && Math.abs(PGmin_new) <= eps) { if (active_size == l) break; else { @@ -635,12 +759,13 @@ private static void solve_l2r_l1l2_svc(Problem prob, double[] w, double eps, dou } PGmax_old = PGmax_new; PGmin_old = PGmin_new; - if (PGmax_old <= 0) PGmax_old = Double.POSITIVE_INFINITY; - if (PGmin_old >= 0) PGmin_old = Double.NEGATIVE_INFINITY; + if (PGmax_old <= 0) + PGmax_old = Double.POSITIVE_INFINITY; + if (PGmin_old >= 0) + PGmin_old = Double.NEGATIVE_INFINITY; } info("%noptimization finished, #iter = %d%n", iter); - if (iter >= max_iter) info("%nWARNING: reaching max number of iterations%nUsing -s 2 may be faster (also see FAQ)%n%n"); // calculate objective value @@ -650,10 +775,13 @@ private static void solve_l2r_l1l2_svc(Problem prob, double[] w, double eps, dou v += w[i] * w[i]; for (i = 0; i < l; i++) { v += alpha[i] * (alpha[i] * diag[GETI(y, i)] - 2); - if (alpha[i] > 0) ++nSV; + if (alpha[i] > 0) + ++nSV; } info("Objective value = %g%n", v / 2); info("nSV = %d%n", nSV); + + return iter; } // To support weights for instances, use GETI(i) (i) @@ -672,11 +800,11 @@ private static int GETI_SVR(int i) { * D is a diagonal matrix * * In L1-SVM case: - * upper_bound_i = C - * lambda_i = 0 + * upper_bound_i = C + * lambda_i = 0 * In L2-SVM case: - * upper_bound_i = INF - * lambda_i = 1/(2*C) + * upper_bound_i = INF + * lambda_i = 1/(2*C) * * Given: * x, y, p, C @@ -684,23 +812,25 @@ private static int GETI_SVR(int i) { * * solution will be put in w * + * this function returns the number of iterations + * * See Algorithm 4 of Ho and Lin, 2012 */ - private static void solve_l2r_l1l2_svr(Problem prob, double[] w, Parameter param) { + private static int solve_l2r_l1l2_svr(Problem prob, Parameter param, double[] w, int max_iter) { + SolverType solver_type = param.solverType; int l = prob.l; double C = param.C; double p = param.p; int w_size = prob.n; double eps = param.eps; int i, s, iter = 0; - int max_iter = 1000; int active_size = l; int[] index = new int[l]; double d, G, H; double Gmax_old = Double.POSITIVE_INFINITY; double Gmax_new, Gnorm1_new; - double Gnorm1_init = 0; // initialize to 0 to get rid of Eclipse warning/error + double Gnorm1_init = -1.0; // Gnorm1_init is initialized at the first iteration double[] beta = new double[l]; double[] QD = new double[l]; double[] y = prob.y; @@ -709,9 +839,11 @@ private static void solve_l2r_l1l2_svr(Problem prob, double[] w, Parameter param double[] lambda = new double[] {0.5 / C}; double[] upper_bound = new double[] {Double.POSITIVE_INFINITY}; - if (param.solverType == SolverType.L2R_L1LOSS_SVR_DUAL) { + if (solver_type == L2R_L1LOSS_SVR_DUAL) { lambda[0] = 0; upper_bound[0] = C; + } else { + assert solver_type == L2R_L2LOSS_SVR_DUAL; } // Initial beta can be set here. Note that @@ -722,23 +854,19 @@ private static void solve_l2r_l1l2_svr(Problem prob, double[] w, Parameter param for (i = 0; i < w_size; i++) w[i] = 0; for (i = 0; i < l; i++) { - QD[i] = 0; - for (Feature xi : prob.x[i]) { - double val = xi.getValue(); - QD[i] += val * val; - w[xi.getIndex() - 1] += beta[i] * val; - } + Feature[] xi = prob.x[i]; + QD[i] = SparseOperator.nrm2_sq(xi); + SparseOperator.axpy(beta[i], xi, w); index[i] = i; } - while (iter < max_iter) { Gmax_new = 0; Gnorm1_new = 0; for (i = 0; i < active_size; i++) { - int j = i + random.nextInt(active_size - i); + int j = i + param.random.nextInt(active_size - i); swap(index, i, j); } @@ -747,11 +875,8 @@ private static void solve_l2r_l1l2_svr(Problem prob, double[] w, Parameter param G = -y[i] + lambda[GETI_SVR(i)] * beta[i]; H = QD[i] + lambda[GETI_SVR(i)]; - for (Feature xi : prob.x[i]) { - int ind = xi.getIndex() - 1; - double val = xi.getValue(); - G += val * w[ind]; - } + Feature[] xi = prob.x[i]; + G += SparseOperator.dot(w, xi); double Gp = G + p; double Gn = G - p; @@ -801,22 +926,22 @@ else if (Gn > H * beta[i]) else d = -beta[i]; - if (Math.abs(d) < 1.0e-12) continue; + if (Math.abs(d) < 1.0e-12) + continue; double beta_old = beta[i]; beta[i] = Math.min(Math.max(beta[i] + d, -upper_bound[GETI_SVR(i)]), upper_bound[GETI_SVR(i)]); d = beta[i] - beta_old; - if (d != 0) { - for (Feature xi : prob.x[i]) { - w[xi.getIndex() - 1] += d * xi.getValue(); - } - } + if (d != 0) + SparseOperator.axpy(d, xi, w); } - if (iter == 0) Gnorm1_init = Gnorm1_new; + if (iter == 0) + Gnorm1_init = Gnorm1_new; iter++; - if (iter % 10 == 0) info("."); + if (iter % 10 == 0) + info("."); if (Gnorm1_new <= eps * Gnorm1_init) { if (active_size == l) @@ -833,7 +958,6 @@ else if (Gn > H * beta[i]) } info("%noptimization finished, #iter = %d%n", iter); - if (iter >= max_iter) info("%nWARNING: reaching max number of iterations%nUsing -s 11 may be faster%n%n"); // calculate objective value double v = 0; @@ -843,11 +967,14 @@ else if (Gn > H * beta[i]) v = 0.5 * v; for (i = 0; i < l; i++) { v += p * Math.abs(beta[i]) - y[i] * beta[i] + 0.5 * lambda[GETI_SVR(i)] * beta[i] * beta[i]; - if (beta[i] != 0) nSV++; + if (beta[i] != 0) + nSV++; } info("Objective value = %g%n", v); info("nSV = %d%n", nSV); + + return iter; } /** @@ -867,24 +994,26 @@ else if (Gn > H * beta[i]) * * solution will be put in w * + * this function returns the number of iterations + * * See Algorithm 5 of Yu et al., MLJ 2010 * * * @since 1.7 */ - private static void solve_l2r_lr_dual(Problem prob, double w[], double eps, double Cp, double Cn) { + private static int solve_l2r_lr_dual(Problem prob, Parameter param, double[] w, double Cp, double Cn, int max_iter) { int l = prob.l; int w_size = prob.n; + double eps = param.eps; int i, s, iter = 0; - double xTx[] = new double[l]; - int max_iter = 1000; - int index[] = new int[l]; - double alpha[] = new double[2 * l]; // store alpha and C - alpha - byte y[] = new byte[l]; + double[] xTx = new double[l]; + int[] index = new int[l]; + double[] alpha = new double[2 * l]; // store alpha and C - alpha + byte[] y = new byte[l]; int max_inner_iter = 100; // for inner Newton double innereps = 1e-2; double innereps_min = Math.min(1e-8, eps); - double upper_bound[] = new double[] {Cn, 0, Cp}; + double[] upper_bound = new double[] {Cn, 0, Cp}; for (i = 0; i < l; i++) { if (prob.y[i] > 0) { @@ -905,31 +1034,26 @@ private static void solve_l2r_lr_dual(Problem prob, double w[], double eps, doub for (i = 0; i < w_size; i++) w[i] = 0; for (i = 0; i < l; i++) { - xTx[i] = 0; - for (Feature xi : prob.x[i]) { - double val = xi.getValue(); - xTx[i] += val * val; - w[xi.getIndex() - 1] += y[i] * alpha[2 * i] * val; - } + Feature[] xi = prob.x[i]; + xTx[i] = SparseOperator.nrm2_sq(xi); + SparseOperator.axpy(y[i] * alpha[2 * i], xi, w); index[i] = i; } while (iter < max_iter) { for (i = 0; i < l; i++) { - int j = i + random.nextInt(l - i); + int j = i + param.random.nextInt(l - i); swap(index, i, j); } int newton_iter = 0; double Gmax = 0; for (s = 0; s < l; s++) { i = index[s]; - byte yi = y[i]; + final byte yi = y[i]; double C = upper_bound[GETI(y, i)]; double ywTx = 0, xisq = xTx[i]; - for (Feature xi : prob.x[i]) { - ywTx += w[xi.getIndex() - 1] * xi.getValue(); - } - ywTx *= y[i]; + Feature[] xi = prob.x[i]; + ywTx = yi * SparseOperator.dot(w, xi); double a = xisq, b = ywTx; // Decide to minimize g_1(z) or g_2(z) @@ -943,7 +1067,8 @@ private static void solve_l2r_lr_dual(Problem prob, double w[], double eps, doub // g_t(z) = z*log(z) + (C-z)*log(C-z) + 0.5a(z-alpha_old)^2 + sign*b(z-alpha_old) double alpha_old = alpha[ind1]; double z = alpha_old; - if (C - z < 0.5 * C) z = 0.1 * z; + if (C - z < 0.5 * C) + z = 0.1 * z; double gp = a * (z - alpha_old) + sign * b + Math.log(z / (C - z)); Gmax = Math.max(Gmax, Math.abs(gp)); @@ -951,7 +1076,8 @@ private static void solve_l2r_lr_dual(Problem prob, double w[], double eps, doub final double eta = 0.1; // xi in the paper int inner_iter = 0; while (inner_iter <= max_inner_iter) { - if (Math.abs(gp) < innereps) break; + if (Math.abs(gp) < innereps) + break; double gpp = a + C / (C - z) / z; double tmpz = z - gp / gpp; if (tmpz <= 0) @@ -968,16 +1094,16 @@ private static void solve_l2r_lr_dual(Problem prob, double w[], double eps, doub { alpha[ind1] = z; alpha[ind2] = C - z; - for (Feature xi : prob.x[i]) { - w[xi.getIndex() - 1] += sign * (z - alpha_old) * yi * xi.getValue(); - } + SparseOperator.axpy(sign * (z - alpha_old) * yi, xi, w); } } iter++; - if (iter % 10 == 0) info("."); + if (iter % 10 == 0) + info("."); - if (Gmax < eps) break; + if (Gmax < eps) + break; if (newton_iter <= l / 10) { innereps = Math.max(innereps_min, 0.1 * innereps); @@ -986,7 +1112,6 @@ private static void solve_l2r_lr_dual(Problem prob, double w[], double eps, doub } info("%noptimization finished, #iter = %d%n", iter); - if (iter >= max_iter) info("%nWARNING: reaching max number of iterations%nUsing -s 0 may be faster (also see FAQ)%n%n"); // calculate objective value @@ -998,6 +1123,8 @@ private static void solve_l2r_lr_dual(Problem prob, double w[], double eps, doub v += alpha[2 * i] * Math.log(alpha[2 * i]) + alpha[2 * i + 1] * Math.log(alpha[2 * i + 1]) - upper_bound[GETI(y, i)] * Math.log(upper_bound[GETI(y, i)]); info("Objective value = %g%n", v); + + return iter; } /** @@ -1013,16 +1140,22 @@ private static void solve_l2r_lr_dual(Problem prob, double w[], double eps, doub * * solution will be put in w * + * this function returns the number of iterations + * * See Yuan et al. (2010) and appendix of LIBLINEAR paper, Fan et al. (2008) + * + * To not regularize the bias (i.e., regularize_bias = 0), a constant feature = 1 + * must have been added to the original data. (see -B and -R option) * * * @since 1.5 */ - private static void solve_l1r_l2_svc(Problem prob_col, double[] w, double eps, double Cp, double Cn) { + private static int solve_l1r_l2_svc(Problem prob_col, Parameter param, double[] w, + double Cp, double Cn, double eps, int max_iter) { int l = prob_col.l; int w_size = prob_col.n; + boolean regularize_bias = param.regularize_bias; int j, s, iter = 0; - int max_iter = 1000; int active_size = w_size; int max_num_linesearch = 20; @@ -1030,7 +1163,7 @@ private static void solve_l1r_l2_svc(Problem prob_col, double[] w, double eps, d double d, G_loss, G, H; double Gmax_old = Double.POSITIVE_INFINITY; double Gmax_new, Gnorm1_new; - double Gnorm1_init = 0; // eclipse moans this variable might not be initialized + double Gnorm1_init = -1.0; // Gnorm1_init is initialized at the first iteration double d_old, d_diff; double loss_old = 0; // eclipse moans this variable might not be initialized double loss_new; @@ -1072,7 +1205,7 @@ private static void solve_l1r_l2_svc(Problem prob_col, double[] w, double eps, d Gnorm1_new = 0; for (j = 0; j < active_size; j++) { - int i = j + random.nextInt(active_size - j); + int i = j + param.random.nextInt(active_size - j); swap(index, i, j); } @@ -1096,50 +1229,65 @@ private static void solve_l1r_l2_svc(Problem prob_col, double[] w, double eps, d H *= 2; H = Math.max(H, 1e-12); - double Gp = G + 1; - double Gn = G - 1; double violation = 0; - if (w[j] == 0) { - if (Gp < 0) - violation = -Gp; - else if (Gn > 0) - violation = Gn; - else if (Gp > Gmax_old / l && Gn < -Gmax_old / l) { - active_size--; - swap(index, s, active_size); - s--; - continue; - } - } else if (w[j] > 0) - violation = Math.abs(Gp); - else - violation = Math.abs(Gn); - + double Gp = 0, Gn = 0; + if (j == w_size - 1 && !regularize_bias) + violation = Math.abs(G); + else { + Gp = G + 1; + Gn = G - 1; + if (w[j] == 0) { + if (Gp < 0) + violation = -Gp; + else if (Gn > 0) + violation = Gn; + else if (Gp > Gmax_old / l && Gn < -Gmax_old / l) { + active_size--; + swap(index, s, active_size); + s--; + continue; + } + } else if (w[j] > 0) + violation = Math.abs(Gp); + else + violation = Math.abs(Gn); + } Gmax_new = Math.max(Gmax_new, violation); Gnorm1_new += violation; // obtain Newton direction d - if (Gp < H * w[j]) - d = -Gp / H; - else if (Gn > H * w[j]) - d = -Gn / H; - else - d = -w[j]; + if (j == w_size - 1 && !regularize_bias) + d = -G / H; + else { + if (Gp < H * w[j]) + d = -Gp / H; + else if (Gn > H * w[j]) + d = -Gn / H; + else + d = -w[j]; + } - if (Math.abs(d) < 1.0e-12) continue; + if (Math.abs(d) < 1.0e-12) + continue; - double delta = Math.abs(w[j] + d) - Math.abs(w[j]) + G * d; + double delta; + if (j == w_size - 1 && !regularize_bias) + delta = G * d; + else + delta = Math.abs(w[j] + d) - Math.abs(w[j]) + G * d; d_old = 0; int num_linesearch; for (num_linesearch = 0; num_linesearch < max_num_linesearch; num_linesearch++) { d_diff = d_old - d; - cond = Math.abs(w[j] + d) - Math.abs(w[j]) - sigma * delta; + if (j == w_size - 1 && !regularize_bias) + cond = -sigma * delta; + else + cond = Math.abs(w[j] + d) - Math.abs(w[j]) - sigma * delta; appxcond = xj_sq[j] * d * d + G_loss * d + cond; if (appxcond <= 0) { - for (Feature x : prob_col.x[j]) { - b[x.getIndex() - 1] += d_diff * x.getValue(); - } + Feature[] x = prob_col.x[j]; + SparseOperator.axpy(d_diff, x, b); break; } @@ -1188,10 +1336,10 @@ else if (Gn > H * w[j]) b[i] = 1; for (int i = 0; i < w_size; i++) { - if (w[i] == 0) continue; - for (Feature x : prob_col.x[i]) { - b[x.getIndex() - 1] -= w[i] * x.getValue(); - } + if (w[i] == 0) + continue; + Feature[] x = prob_col.x[i]; + SparseOperator.axpy(-w[i], x, b); } } } @@ -1200,9 +1348,10 @@ else if (Gn > H * w[j]) Gnorm1_init = Gnorm1_new; } iter++; - if (iter % 10 == 0) info("."); + if (iter % 10 == 0) + info("."); - if (Gmax_new <= eps * Gnorm1_init) { + if (Gnorm1_new <= eps * Gnorm1_init) { if (active_size == w_size) break; else { @@ -1217,7 +1366,8 @@ else if (Gn > H * w[j]) } info("%noptimization finished, #iter = %d%n", iter); - if (iter >= max_iter) info("%nWARNING: reaching max number of iterations%n"); + if (iter >= max_iter) + info("%nWARNING: reaching max number of iterations%n"); // calculate objective value @@ -1232,11 +1382,16 @@ else if (Gn > H * w[j]) nnz++; } } + if (!regularize_bias) + v -= Math.abs(w[w_size - 1]); for (j = 0; j < l; j++) - if (b[j] > 0) v += C[GETI(y, j)] * b[j] * b[j]; + if (b[j] > 0) + v += C[GETI(y, j)] * b[j] * b[j]; info("Objective value = %g%n", v); info("#nonzeros/#features = %d/%d%n", nnz, w_size); + + return iter; } /** @@ -1252,17 +1407,22 @@ else if (Gn > H * w[j]) * * solution will be put in w * + * this function returns the number of iterations + * * See Yuan et al. (2011) and appendix of LIBLINEAR paper, Fan et al. (2008) + * + * To not regularize the bias (i.e., regularize_bias = 0), a constant feature = 1 + * must have been added to the original data. (see -B and -R option) * * * @since 1.5 */ - private static void solve_l1r_lr(Problem prob_col, double[] w, double eps, double Cp, double Cn) { + private static int solve_l1r_lr(Problem prob_col, Parameter param, double[] w, double Cp, double Cn, double eps, int max_iter) { int l = prob_col.l; int w_size = prob_col.n; + boolean regularize_bias = param.regularize_bias; int j, s, newton_iter = 0, iter = 0; int max_newton_iter = 100; - int max_iter = 1000; int max_num_linesearch = 20; int active_size; int QP_active_size; @@ -1272,7 +1432,7 @@ private static void solve_l1r_lr(Problem prob_col, double[] w, double eps, doubl double sigma = 0.01; double w_norm, w_norm_new; double z, G, H; - double Gnorm1_init = 0; // eclipse moans this variable might not be initialized + double Gnorm1_init = -1.0; // Gnorm1_init is initialized at the first iteration double Gmax_old = Double.POSITIVE_INFINITY; double Gmax_new, Gnorm1_new; double QP_Gmax_old = Double.POSITIVE_INFINITY; @@ -1321,6 +1481,9 @@ private static void solve_l1r_lr(Problem prob_col, double[] w, double eps, doubl } } } + if (!regularize_bias) + w_norm -= Math.abs(w[w_size - 1]); + for (j = 0; j < l; j++) { exp_wTx[j] = Math.exp(exp_wTx[j]); double tau_tmp = 1 / (1 + exp_wTx[j]); @@ -1346,33 +1509,38 @@ private static void solve_l1r_lr(Problem prob_col, double[] w, double eps, doubl } Grad[j] = -tmp + xjneg_sum[j]; - double Gp = Grad[j] + 1; - double Gn = Grad[j] - 1; double violation = 0; - if (w[j] == 0) { - if (Gp < 0) - violation = -Gp; - else if (Gn > 0) - violation = Gn; - //outer-level shrinking - else if (Gp > Gmax_old / l && Gn < -Gmax_old / l) { - active_size--; - swap(index, s, active_size); - s--; - continue; - } - } else if (w[j] > 0) - violation = Math.abs(Gp); - else - violation = Math.abs(Gn); - + if (j == w_size - 1 && !regularize_bias) + violation = Math.abs(Grad[j]); + else { + double Gp = Grad[j] + 1; + double Gn = Grad[j] - 1; + if (w[j] == 0) { + if (Gp < 0) + violation = -Gp; + else if (Gn > 0) + violation = Gn; + //outer-level shrinking + else if (Gp > Gmax_old / l && Gn < -Gmax_old / l) { + active_size--; + swap(index, s, active_size); + s--; + continue; + } + } else if (w[j] > 0) + violation = Math.abs(Gp); + else + violation = Math.abs(Gn); + } Gmax_new = Math.max(Gmax_new, violation); Gnorm1_new += violation; } - if (newton_iter == 0) Gnorm1_init = Gnorm1_new; + if (newton_iter == 0) + Gnorm1_init = Gnorm1_new; - if (Gnorm1_new <= eps * Gnorm1_init) break; + if (Gnorm1_new <= eps * Gnorm1_init) + break; iter = 0; QP_Gmax_old = Double.POSITIVE_INFINITY; @@ -1387,7 +1555,7 @@ else if (Gp > Gmax_old / l && Gn < -Gmax_old / l) { QP_Gnorm1_new = 0; for (j = 0; j < QP_active_size; j++) { - int i = random.nextInt(QP_active_size - j); + int i = param.random.nextInt(QP_active_size - j); swap(index, i, j); } @@ -1401,46 +1569,51 @@ else if (Gp > Gmax_old / l && Gn < -Gmax_old / l) { G += x.getValue() * D[ind] * xTd[ind]; } - double Gp = G + 1; - double Gn = G - 1; double violation = 0; - if (wpd[j] == 0) { - if (Gp < 0) - violation = -Gp; - else if (Gn > 0) - violation = Gn; - //inner-level shrinking - else if (Gp > QP_Gmax_old / l && Gn < -QP_Gmax_old / l) { - QP_active_size--; - swap(index, s, QP_active_size); - s--; - continue; - } - } else if (wpd[j] > 0) - violation = Math.abs(Gp); - else - violation = Math.abs(Gn); - + if (j == w_size - 1 && !regularize_bias) { + // bias term not shrunken + violation = Math.abs(G); + z = -G / H; + } else { + double Gp = G + 1; + double Gn = G - 1; + if (wpd[j] == 0) { + if (Gp < 0) + violation = -Gp; + else if (Gn > 0) + violation = Gn; + //inner-level shrinking + else if (Gp > QP_Gmax_old / l && Gn < -QP_Gmax_old / l) { + QP_active_size--; + swap(index, s, QP_active_size); + s--; + continue; + } + } else if (wpd[j] > 0) + violation = Math.abs(Gp); + else + violation = Math.abs(Gn); + + // obtain solution of one-variable problem + if (Gp < H * wpd[j]) + z = -Gp / H; + else if (Gn > H * wpd[j]) + z = -Gn / H; + else + z = -wpd[j]; + } QP_Gmax_new = Math.max(QP_Gmax_new, violation); QP_Gnorm1_new += violation; - // obtain solution of one-variable problem - if (Gp < H * wpd[j]) - z = -Gp / H; - else if (Gn > H * wpd[j]) - z = -Gn / H; - else - z = -wpd[j]; - - if (Math.abs(z) < 1.0e-12) continue; + if (Math.abs(z) < 1.0e-12) { + continue; + } z = Math.min(Math.max(z, -10.0), 10.0); wpd[j] += z; - for (Feature x : prob_col.x[j]) { - int ind = x.getIndex() - 1; - xTd[ind] += x.getValue() * z; - } + Feature[] x = prob_col.x[j]; + SparseOperator.axpy(z, x, xTd); } iter++; @@ -1449,7 +1622,7 @@ else if (Gn > H * wpd[j]) //inner stopping if (QP_active_size == active_size) break; - //active set reactivation + //active set reactivation else { QP_active_size = active_size; QP_Gmax_old = Double.POSITIVE_INFINITY; @@ -1460,19 +1633,25 @@ else if (Gn > H * wpd[j]) QP_Gmax_old = QP_Gmax_new; } - if (iter >= max_iter) info("WARNING: reaching max number of inner iterations%n"); + if (iter >= max_iter) { + info("WARNING: reaching max number of inner iterations%n"); + } delta = 0; w_norm_new = 0; for (j = 0; j < w_size; j++) { delta += Grad[j] * (wpd[j] - w[j]); - if (wpd[j] != 0) w_norm_new += Math.abs(wpd[j]); + if (wpd[j] != 0) + w_norm_new += Math.abs(wpd[j]); } + if (!regularize_bias) + w_norm_new -= Math.abs(wpd[w_size - 1]); delta += (w_norm_new - w_norm); negsum_xTd = 0; for (int i = 0; i < l; i++) - if (y[i] == -1) negsum_xTd += C[GETI(y, i)] * xTd[i]; + if (y[i] == -1) + negsum_xTd += C[GETI(y, i)] * xTd[i]; int num_linesearch; for (num_linesearch = 0; num_linesearch < max_num_linesearch; num_linesearch++) { @@ -1499,8 +1678,11 @@ else if (Gn > H * wpd[j]) w_norm_new = 0; for (j = 0; j < w_size; j++) { wpd[j] = (w[j] + wpd[j]) * 0.5; - if (wpd[j] != 0) w_norm_new += Math.abs(wpd[j]); + if (wpd[j] != 0) + w_norm_new += Math.abs(wpd[j]); } + if (!regularize_bias) + w_norm_new -= Math.abs(wpd[w_size - 1]); delta *= 0.5; negsum_xTd *= 0.5; for (int i = 0; i < l; i++) @@ -1514,17 +1696,18 @@ else if (Gn > H * wpd[j]) exp_wTx[i] = 0; for (int i = 0; i < w_size; i++) { - if (w[i] == 0) continue; - for (Feature x : prob_col.x[i]) { - exp_wTx[x.getIndex() - 1] += w[i] * x.getValue(); - } + if (w[i] == 0) + continue; + Feature[] x = prob_col.x[i]; + SparseOperator.axpy(w[i], x, exp_wTx); } for (int i = 0; i < l; i++) exp_wTx[i] = Math.exp(exp_wTx[i]); } - if (iter == 1) inner_eps *= 0.25; + if (iter == 1) + inner_eps *= 0.25; newton_iter++; Gmax_old = Gmax_new; @@ -1534,7 +1717,9 @@ else if (Gn > H * wpd[j]) info("=========================%n"); info("optimization finished, #iter = %d%n", newton_iter); - if (newton_iter >= max_newton_iter) info("WARNING: reaching max number of iterations%n"); + if (newton_iter >= max_newton_iter) { + info("WARNING: reaching max number of iterations%n"); + } // calculate objective value @@ -1545,6 +1730,8 @@ else if (Gn > H * wpd[j]) v += Math.abs(w[j]); nnz++; } + if (!regularize_bias) + v -= Math.abs(w[w_size - 1]); for (j = 0; j < l; j++) if (y[j] == 1) v += C[GETI(y, j)] * Math.log(1 + 1 / exp_wTx[j]); @@ -1553,6 +1740,245 @@ else if (Gn > H * wpd[j]) info("Objective value = %g%n", v); info("#nonzeros/#features = %d/%d%n", nnz, w_size); + + return newton_iter; + } + + /* + * A two-level coordinate descent algorithm for + * a scaled one-class SVM dual problem + * + * min_\alpha 0.5(\alpha^T Q \alpha), + * s.t. 0 <= \alpha_i <= 1 and + * e^T \alpha = \nu l + * + * where Qij = xi^T xj + * + * Given: + * x, nu + * eps is the stopping tolerance + * + * solution will be put in w and rho + * + * this function returns the number of iterations + * + * See Algorithm 7 in supplementary materials of Chou et al., SDM 2020. + * + * @since 2.40 + */ + static int solve_oneclass_svm(Problem prob, Parameter param, double[] w, MutableDouble rho, int max_iter) { + int l = prob.l; + int w_size = prob.n; + double eps = param.eps; + double nu = param.nu; + int i, j, s, iter = 0; + double Gi, Gj; + double Qij, quad_coef, delta, sum; + double old_alpha_i; + double[] QD = new double[l]; + double[] G = new double[l]; + int[] index = new int[l]; + double[] alpha = new double[l]; + int max_inner_iter; + int active_size = l; + + double negGmax; // max { -grad(f)_i | alpha_i < 1 } + double negGmin; // min { -grad(f)_i | alpha_i > 0 } + + int[] most_violating_i = new int[l]; + int[] most_violating_j = new int[l]; + + int n = (int)(nu * l); // # of alpha's at upper bound + for (i = 0; i < n; i++) + alpha[i] = 1; + if (n < l) + alpha[i] = nu * l - n; + for (i = n + 1; i < l; i++) + alpha[i] = 0; + + for (i = 0; i < w_size; i++) + w[i] = 0; + for (i = 0; i < l; i++) { + Feature[] xi = prob.x[i]; + QD[i] = SparseOperator.nrm2_sq(xi); + SparseOperator.axpy(alpha[i], xi, w); + + index[i] = i; + } + + while (iter < max_iter) { + negGmax = Double.NEGATIVE_INFINITY; + negGmin = Double.POSITIVE_INFINITY; + + for (s = 0; s < active_size; s++) { + i = index[s]; + Feature[] xi = prob.x[i]; + G[i] = SparseOperator.dot(w, xi); + if (alpha[i] < 1) + negGmax = Math.max(negGmax, -G[i]); + if (alpha[i] > 0) + negGmin = Math.min(negGmin, -G[i]); + } + + if (negGmax - negGmin < eps) { + if (active_size == l) + break; + else { + active_size = l; + info("*"); + continue; + } + } + + for (s = 0; s < active_size; s++) { + i = index[s]; + if ((alpha[i] == 1 && -G[i] > negGmax) || + (alpha[i] == 0 && -G[i] < negGmin)) { + active_size--; + Linear.swap(index, s, active_size); + s--; + } + } + + max_inner_iter = Math.max(active_size / 10, 1); + Heap min_heap = new Heap(max_inner_iter, HeapType.MIN); + Heap max_heap = new Heap(max_inner_iter, HeapType.MAX); + + for (s = 0; s < active_size; s++) { + i = index[s]; + FeatureNode node = new FeatureNode(i, -G[i], false); + + if (alpha[i] < 1) { + if (min_heap.size() < max_inner_iter) + min_heap.push(node); + else if (min_heap.top().getValue() < node.getValue()) { + min_heap.pop(); + min_heap.push(node); + } + } + + if (alpha[i] > 0) { + if (max_heap.size() < max_inner_iter) + max_heap.push(node); + else if (max_heap.top().getValue() > node.getValue()) { + max_heap.pop(); + max_heap.push(node); + } + } + } + max_inner_iter = Math.min(min_heap.size(), max_heap.size()); + while (max_heap.size() > max_inner_iter) + max_heap.pop(); + while (min_heap.size() > max_inner_iter) + min_heap.pop(); + + for (s = max_inner_iter - 1; s >= 0; s--) { + most_violating_i[s] = min_heap.top().getIndex(); + most_violating_j[s] = max_heap.top().getIndex(); + min_heap.pop(); + max_heap.pop(); + } + + for (s = 0; s < max_inner_iter; s++) { + i = most_violating_i[s]; + j = most_violating_j[s]; + + if ((alpha[i] == 0 && alpha[j] == 0) || + (alpha[i] == 1 && alpha[j] == 1)) + continue; + + Feature[] xi = prob.x[i]; + Feature[] xj = prob.x[j]; + + Gi = SparseOperator.dot(w, xi); + Gj = SparseOperator.dot(w, xj); + + int violating_pair = 0; + if (alpha[i] < 1 && alpha[j] > 0 && -Gj + 1e-12 < -Gi) + violating_pair = 1; + else if (alpha[i] > 0 && alpha[j] < 1 && -Gi + 1e-12 < -Gj) + violating_pair = 1; + if (violating_pair == 0) + continue; + + Qij = SparseOperator.sparse_dot(xi, xj); + quad_coef = QD[i] + QD[j] - 2 * Qij; + if (quad_coef <= 0) + quad_coef = 1e-12; + delta = (Gi - Gj) / quad_coef; + old_alpha_i = alpha[i]; + sum = alpha[i] + alpha[j]; + alpha[i] = alpha[i] - delta; + alpha[j] = alpha[j] + delta; + if (sum > 1) { + if (alpha[i] > 1) { + alpha[i] = 1; + alpha[j] = sum - 1; + } + } else { + if (alpha[j] < 0) { + alpha[j] = 0; + alpha[i] = sum; + } + } + if (sum > 1) { + if (alpha[j] > 1) { + alpha[j] = 1; + alpha[i] = sum - 1; + } + } else { + if (alpha[i] < 0) { + alpha[i] = 0; + alpha[j] = sum; + } + } + delta = alpha[i] - old_alpha_i; + SparseOperator.axpy(delta, xi, w); + SparseOperator.axpy(-delta, xj, w); + } + iter++; + if (iter % 10 == 0) + info("."); + } + info("%noptimization finished, #iter = %d%n", iter); + if (iter >= max_iter) + info("%nWARNING: reaching max number of iterations%n%n"); + + // calculate object value + double v = 0; + for (i = 0; i < w_size; i++) + v += w[i] * w[i]; + int nSV = 0; + for (i = 0; i < l; i++) { + if (alpha[i] > 0) + ++nSV; + } + info("Objective value = %f%n", v / 2); + info("nSV = %d%n", nSV); + + // calculate rho + double nr_free = 0; + double ub = Double.POSITIVE_INFINITY, lb = Double.NEGATIVE_INFINITY, sum_free = 0; + for (i = 0; i < l; i++) { + double G_ = SparseOperator.dot(w, prob.x[i]); + if (alpha[i] == 1) + lb = Math.max(lb, G_); + else if (alpha[i] == 0) + ub = Math.min(ub, G_); + else { + ++nr_free; + sum_free += G_; + } + } + + if (nr_free > 0) + rho.set(sum_free / nr_free); + else + rho.set((ub + lb) / 2); + + info("rho = %f%n", rho.get()); + + return iter; } // transpose matrix X from row format to column format @@ -1610,17 +2036,36 @@ static void swap(IntArrayPointer array, int idxA, int idxB) { array.set(idxB, temp); } + static void swap(Feature[] array, int idxA, int idxB) { + Feature temp = array[idxA]; + array[idxA] = array[idxB]; + array[idxB] = temp; + } /** * @throws IllegalArgumentException if the feature nodes of prob are not sorted in ascending order */ public static Model train(Problem prob, Parameter param) { + if (prob == null) { + throw new IllegalArgumentException("problem must not be null"); + } + if (param == null) { + throw new IllegalArgumentException("parameter must not be null"); + } + if (param.eps <= 0) { + throw new IllegalArgumentException("eps <= 0"); + } + if (param.C <= 0) { + throw new IllegalArgumentException("C <= 0"); + } + if (param.p < 0 && param.solverType == L2R_L2LOSS_SVR) { + throw new IllegalArgumentException("p < 0"); + } - if (prob == null) throw new IllegalArgumentException("problem must not be null"); - if (param == null) throw new IllegalArgumentException("parameter must not be null"); - - if (prob.n == 0) throw new IllegalArgumentException("problem has zero features"); - if (prob.l == 0) throw new IllegalArgumentException("problem has zero instances"); + if (prob.n == 0) + throw new IllegalArgumentException("problem has zero features"); + if (prob.l == 0) + throw new IllegalArgumentException("problem has zero instances"); for (Feature[] nodes : prob.x) { int indexBefore = 0; @@ -1632,6 +2077,29 @@ public static Model train(Problem prob, Parameter param) { } } + if (prob.bias >= 0 && param.solverType.isOneClass()) { + throw new IllegalArgumentException("prob->bias >=0, but this is ignored in ONECLASS_SVM"); + } + + if (!param.regularize_bias) { + if (prob.bias != 1.0) + throw new IllegalArgumentException("To not regularize bias, must specify -B 1 along with -R"); + + if (param.solverType != L2R_LR + && param.solverType != L2R_L2LOSS_SVC + && param.solverType != L1R_L2LOSS_SVC + && param.solverType != L1R_LR + && param.solverType != L2R_L2LOSS_SVR) + throw new IllegalArgumentException("-R option supported only for solver L2R_LR, L2R_L2LOSS_SVC, L1R_L2LOSS_SVC, L1R_LR, and L2R_L2LOSS_SVR"); + } + + if (param.init_sol != null + && param.getSolverType() != L2R_LR + && param.getSolverType() != L2R_L2LOSS_SVC + && param.getSolverType() != L2R_L2LOSS_SVR) { + throw new IllegalArgumentException("Initial-solution specification supported only for solvers L2R_LR, L2R_L2LOSS_SVC, and L2R_L2LOSS_SVR"); + } + int l = prob.l; int n = prob.n; int w_size = prob.n; @@ -1645,16 +2113,27 @@ public static Model train(Problem prob, Parameter param) { model.solverType = param.solverType; model.bias = prob.bias; - if (param.solverType == SolverType.L2R_L2LOSS_SVR || // - param.solverType == SolverType.L2R_L1LOSS_SVR_DUAL || // - param.solverType == SolverType.L2R_L2LOSS_SVR_DUAL) { + if (param.solverType.isSupportVectorRegression()) { model.w = new double[w_size]; + if (param.init_sol != null) + for (int i = 0; i < w_size; i++) + model.w[i] = param.init_sol[i]; + else + for (int i = 0; i < w_size; i++) + model.w[i] = 0; model.nr_class = 2; model.label = null; checkProblemSize(n, model.nr_class); train_one(prob, param, model.w, 0, 0); + } else if (param.solverType.isOneClass()) { + model.w = new double[w_size]; + model.nr_class = 2; + model.label = null; + MutableDouble rho = new MutableDouble(); + solve_oneclass_svm(prob, param, model.w, rho, param.max_iters); + model.rho = rho.get(); } else { int[] perm = new int[l]; @@ -1679,9 +2158,11 @@ public static Model train(Problem prob, Parameter param) { for (int i = 0; i < param.getNumWeights(); i++) { int j; for (j = 0; j < nr_class; j++) - if (param.weightLabel[i] == label[j]) break; + if (param.weightLabel[i] == label[j]) + break; - if (j == nr_class) throw new IllegalArgumentException("class label " + param.weightLabel[i] + " specified in weight is not found"); + if (j == nr_class) + throw new IllegalArgumentException("class label " + param.weightLabel[i] + " specified in weight is not found"); weighted_C[j] *= param.weight[i]; } @@ -1700,7 +2181,7 @@ public static Model train(Problem prob, Parameter param) { sub_prob.x[k] = x[k]; // multi-class svm by Crammer and Singer - if (param.solverType == SolverType.MCSVM_CS) { + if (param.solverType == MCSVM_CS) { model.w = new double[n * nr_class]; for (int i = 0; i < nr_class; i++) { for (int j = start[i]; j < start[i] + count[i]; j++) { @@ -1708,7 +2189,7 @@ public static Model train(Problem prob, Parameter param) { } } - SolverMCSVM_CS solver = new SolverMCSVM_CS(sub_prob, nr_class, weighted_C, param.eps); + SolverMCSVM_CS solver = new SolverMCSVM_CS(sub_prob, nr_class, weighted_C, param.eps, param.random); solver.solve(model.w); } else { if (nr_class == 2) { @@ -1721,6 +2202,13 @@ public static Model train(Problem prob, Parameter param) { for (; k < sub_prob.l; k++) sub_prob.y[k] = -1; + if (param.init_sol != null) + for (int i = 0; i < w_size; i++) + model.w[i] = param.init_sol[i]; + else + for (int i = 0; i < w_size; i++) + model.w[i] = 0; + train_one(sub_prob, param, model.w, weighted_C[0], weighted_C[1]); } else { model.w = new double[w_size * nr_class]; @@ -1737,6 +2225,13 @@ public static Model train(Problem prob, Parameter param) { for (; k < sub_prob.l; k++) sub_prob.y[k] = -1; + if (param.init_sol != null) + for (int j = 0; j < w_size; j++) + w[j] = param.init_sol[j * nr_class + i]; + else + for (int j = 0; j < w_size; j++) + w[j] = 0; + train_one(sub_prob, param, w, weighted_C[i], param.C); for (int j = 0; j < n; j++) @@ -1758,83 +2253,268 @@ private static void checkProblemSize(int n, int nr_class) { } private static void train_one(Problem prob, Parameter param, double[] w, double Cp, double Cn) { - double eps = param.eps; - int pos = 0; - for (int i = 0; i < prob.l; i++) - if (prob.y[i] > 0) { - pos++; + SolverType solver_type = param.solverType; + int dual_solver_max_iter = 300; + int iter; + + // upstream: (solver_type==L2R_L2LOSS_SVR || solver_type==L2R_L1LOSS_SVR_DUAL || solver_type==L2R_L2LOSS_SVR_DUAL) + boolean is_regression = solver_type.isSupportVectorRegression(); + + // Some solvers use Cp,Cn but not C array; extensions possible but no plan for now + double[] C = new double[prob.l]; + double primal_solver_tol = param.eps; + if (is_regression) { + for (int i = 0; i < prob.l; i++) + C[i] = param.C; + } else { + int pos = 0; + for (int i = 0; i < prob.l; i++) { + if (prob.y[i] > 0) { + pos++; + C[i] = Cp; + } else + C[i] = Cn; } - int neg = prob.l - pos; - - double primal_solver_tol = eps * Math.max(Math.min(pos, neg), 1) / prob.l; + int neg = prob.l - pos; + primal_solver_tol = param.eps * Math.max(Math.min(pos, neg), 1) / prob.l; + } - Function fun_obj = null; - switch (param.solverType) { + switch (solver_type) { case L2R_LR: { - double[] C = new double[prob.l]; - for (int i = 0; i < prob.l; i++) { - if (prob.y[i] > 0) - C[i] = Cp; - else - C[i] = Cn; - } - fun_obj = new L2R_LrFunction(prob, C); - Tron tron_obj = new Tron(fun_obj, primal_solver_tol); - tron_obj.tron(w); + L2R_LrFunction fun_obj = new L2R_LrFunction(prob, param, C); + Newton newton_obj = new Newton(fun_obj, primal_solver_tol, param.max_iters); + newton_obj.newton(w); break; } case L2R_L2LOSS_SVC: { - double[] C = new double[prob.l]; - for (int i = 0; i < prob.l; i++) { - if (prob.y[i] > 0) - C[i] = Cp; - else - C[i] = Cn; - } - fun_obj = new L2R_L2_SvcFunction(prob, C); - Tron tron_obj = new Tron(fun_obj, primal_solver_tol); - tron_obj.tron(w); + L2R_L2_SvcFunction fun_obj = new L2R_L2_SvcFunction(prob, param, C); + Newton newton_obj = new Newton(fun_obj, primal_solver_tol, param.max_iters); + newton_obj.newton(w); break; } - case L2R_L2LOSS_SVC_DUAL: - solve_l2r_l1l2_svc(prob, w, eps, Cp, Cn, SolverType.L2R_L2LOSS_SVC_DUAL); + case L2R_L2LOSS_SVC_DUAL: { + iter = solve_l2r_l1l2_svc(prob, param, w, Cp, Cn, dual_solver_max_iter); + if (iter >= dual_solver_max_iter) { + info("%nWARNING: reaching max number of iterations%nSwitching to use -s 2%n%n"); + // primal_solver_tol obtained from eps for dual may be too loose + primal_solver_tol *= 0.1; + L2R_L2_SvcFunction fun_obj = new L2R_L2_SvcFunction(prob, param, C); + Newton newton_obj = new Newton(fun_obj, primal_solver_tol, param.max_iters); + newton_obj.newton(w); + } break; - case L2R_L1LOSS_SVC_DUAL: - solve_l2r_l1l2_svc(prob, w, eps, Cp, Cn, SolverType.L2R_L1LOSS_SVC_DUAL); + } + case L2R_L1LOSS_SVC_DUAL: { + iter = solve_l2r_l1l2_svc(prob, param, w, Cp, Cn, dual_solver_max_iter); + if (iter >= dual_solver_max_iter) + info("%nWARNING: reaching max number of iterations%nUsing -s 2 may be faster (also see FAQ)%n%n"); break; + } case L1R_L2LOSS_SVC: { Problem prob_col = transpose(prob); - solve_l1r_l2_svc(prob_col, w, primal_solver_tol, Cp, Cn); + solve_l1r_l2_svc(prob_col, param, w, Cp, Cn, primal_solver_tol, param.max_iters); break; } case L1R_LR: { Problem prob_col = transpose(prob); - solve_l1r_lr(prob_col, w, primal_solver_tol, Cp, Cn); + solve_l1r_lr(prob_col, param, w, Cp, Cn, primal_solver_tol, param.max_iters); break; } - case L2R_LR_DUAL: - solve_l2r_lr_dual(prob, w, eps, Cp, Cn); + case L2R_LR_DUAL: { + iter = solve_l2r_lr_dual(prob, param, w, Cp, Cn, dual_solver_max_iter); + if (iter >= dual_solver_max_iter) { + info("%nWARNING: reaching max number of iterations%nSwitching to use -s 0%n%n"); + // primal_solver_tol obtained from eps for dual may be too loose + primal_solver_tol *= 0.1; + L2R_LrFunction fun_obj = new L2R_LrFunction(prob, param, C); + Newton newton_obj = new Newton(fun_obj, primal_solver_tol, param.max_iters); + newton_obj.newton(w); + } break; + } case L2R_L2LOSS_SVR: { - double[] C = new double[prob.l]; - for (int i = 0; i < prob.l; i++) - C[i] = param.C; + L2R_L2_SvrFunction fun_obj = new L2R_L2_SvrFunction(prob, param, C); + Newton newton_obj = new Newton(fun_obj, primal_solver_tol, param.max_iters); + newton_obj.newton(w); + break; + + } + case L2R_L1LOSS_SVR_DUAL: { + iter = solve_l2r_l1l2_svr(prob, param, w, dual_solver_max_iter); + if (iter >= dual_solver_max_iter) + info("%nWARNING: reaching max number of iterations%nUsing -s 11 may be faster (also see FAQ)%n%n"); - fun_obj = new L2R_L2_SvrFunction(prob, C, param.p); - Tron tron_obj = new Tron(fun_obj, param.eps); - tron_obj.tron(w); break; } - case L2R_L1LOSS_SVR_DUAL: - case L2R_L2LOSS_SVR_DUAL: - solve_l2r_l1l2_svr(prob, w, param); + case L2R_L2LOSS_SVR_DUAL: { + iter = solve_l2r_l1l2_svr(prob, param, w, dual_solver_max_iter); + if (iter >= dual_solver_max_iter) { + info("%nWARNING: reaching max number of iterations%nSwitching to use -s 11%n%n"); + // primal_solver_tol obtained from eps for dual may be too loose + primal_solver_tol *= 0.001; + L2R_L2_SvrFunction fun_obj = new L2R_L2_SvrFunction(prob, param, C); + Newton newton_obj = new Newton(fun_obj, primal_solver_tol, param.max_iters); + newton_obj.newton(w); + } break; - + } default: throw new IllegalStateException("unknown solver type: " + param.solverType); } } + // Calculate the initial C for parameter selection + private static double calc_start_C(Problem prob, Parameter param) { + int i; + double xTx, max_xTx; + max_xTx = 0; + for (i = 0; i < prob.l; i++) { + xTx = 0; + for (Feature xi : prob.x[i]) { + double val = xi.getValue(); + xTx += val * val; + } + if (xTx > max_xTx) + max_xTx = xTx; + } + + double min_C = 1.0; + if (param.getSolverType() == L2R_LR) + min_C = 1.0 / (prob.l * max_xTx); + else if (param.getSolverType() == L2R_L2LOSS_SVC) + min_C = 1.0 / (2 * prob.l * max_xTx); + else if (param.getSolverType() == L2R_L2LOSS_SVR) { + double sum_y, loss, y_abs; + double delta2 = 0.1; + sum_y = 0; + loss = 0; + for (i = 0; i < prob.l; i++) { + y_abs = Math.abs(prob.y[i]); + sum_y += y_abs; + loss += Math.max(y_abs - param.p, 0.0) * Math.max(y_abs - param.p, 0.0); + } + if (loss > 0) + min_C = delta2 * delta2 * loss / (8 * sum_y * sum_y * max_xTx); + else + min_C = Double.POSITIVE_INFINITY; + } + + return Math.pow(2, Math.floor(Math.log(min_C) / Math.log(2.0))); + } + + private static double calc_max_p(Problem prob) { + int i; + double max_p = 0.0; + for (i = 0; i < prob.l; i++) + max_p = Math.max(max_p, Math.abs(prob.y[i])); + + return max_p; + } + + public static ParameterCSearchResult find_parameter_C(Problem prob, Parameter param_tmp, double start_C, double max_C, int[] fold_start, int[] perm, + Problem[] subprob, int nr_fold) { + double best_C; + double best_score = Double.NaN; + // variables for CV + int i; + double[] target = new double[prob.l]; + + // variables for warm start + double ratio = 2; + double[][] prev_w = new double[nr_fold][]; + for (i = 0; i < nr_fold; i++) + prev_w[i] = null; + int num_unchanged_w = 0; + PrintStream default_print_string = DEBUG_OUTPUT; + + if (param_tmp.getSolverType() == L2R_LR || param_tmp.getSolverType() == L2R_L2LOSS_SVC) + best_score = 0.0; + else if (param_tmp.getSolverType() == L2R_L2LOSS_SVR) + best_score = Double.POSITIVE_INFINITY; + best_C = start_C; + + param_tmp.C = start_C; + while (param_tmp.C <= max_C) { + //Output disabled for running CV at a particular C + disableDebugOutput(); + + for (i = 0; i < nr_fold; i++) { + int j; + int begin = fold_start[i]; + int end = fold_start[i + 1]; + + param_tmp.init_sol = prev_w[i]; + Model submodel = train(subprob[i], param_tmp); + + int total_w_size; + if (submodel.nr_class == 2) + total_w_size = subprob[i].n; + else + total_w_size = subprob[i].n * submodel.nr_class; + + if (prev_w[i] == null) { + prev_w[i] = new double[total_w_size]; + for (j = 0; j < total_w_size; j++) + prev_w[i][j] = submodel.w[j]; + } else if (num_unchanged_w >= 0) { + double norm_w_diff = 0; + for (j = 0; j < total_w_size; j++) { + norm_w_diff += (submodel.w[j] - prev_w[i][j]) * (submodel.w[j] - prev_w[i][j]); + prev_w[i][j] = submodel.w[j]; + } + norm_w_diff = Math.sqrt(norm_w_diff); + + if (norm_w_diff > 1e-15) + num_unchanged_w = -1; + } else { + for (j = 0; j < total_w_size; j++) + prev_w[i][j] = submodel.w[j]; + } + + for (j = begin; j < end; j++) + target[perm[j]] = predict(submodel, prob.x[perm[j]]); + } + setDebugOutput(default_print_string); + + if (param_tmp.getSolverType() == L2R_LR || param_tmp.getSolverType() == L2R_L2LOSS_SVC) { + int total_correct = 0; + for (i = 0; i < prob.l; i++) + if (target[i] == prob.y[i]) + ++total_correct; + double current_rate = (double)total_correct / prob.l; + if (current_rate > best_score) { + best_C = param_tmp.C; + best_score = current_rate; + } + + info("log2c=%7.2f\trate=%g%n", Math.log(param_tmp.C) / Math.log(2.0), 100.0 * current_rate); + } else if (param_tmp.getSolverType() == L2R_L2LOSS_SVR) { + double total_error = 0.0; + for (i = 0; i < prob.l; i++) { + double y = prob.y[i]; + double v = target[i]; + total_error += (v - y) * (v - y); + } + double current_error = total_error / prob.l; + if (current_error < best_score) { + best_C = param_tmp.C; + best_score = current_error; + } + + info("log2c=%7.2f\tp=%7.2f\tMean squared error=%g%n", Math.log(param_tmp.C) / Math.log(2.0), param_tmp.p, current_error); + } + + num_unchanged_w++; + if (num_unchanged_w == 5) + break; + param_tmp.C = param_tmp.C * ratio; + } + + if (param_tmp.C > max_C) + info("WARNING: maximum C reached.%n"); + return new ParameterCSearchResult(best_C, best_score); + } + public static void disableDebugOutput() { setDebugOutput(null); } @@ -1849,12 +2529,16 @@ public static void setDebugOutput(PrintStream debugOutput) { } } + public static int getVersion() { + return VERSION; + } + /** * resets the PRNG * - * this is i.a. needed for regression testing (eg. the Weka wrapper) + * @deprecated Use {@link Parameter#setRandom(Random)} instead */ + @Deprecated public static void resetRandom() { - random = new Random(DEFAULT_RANDOM_SEED); } } diff --git a/src/main/java/de/bwaldvogel/liblinear/Model.java b/src/main/java/de/bwaldvogel/liblinear/Model.java index 280b134..6eba91f 100644 --- a/src/main/java/de/bwaldvogel/liblinear/Model.java +++ b/src/main/java/de/bwaldvogel/liblinear/Model.java @@ -1,37 +1,42 @@ package de.bwaldvogel.liblinear; -import static de.bwaldvogel.liblinear.Linear.copyOf; +import static de.bwaldvogel.liblinear.SolverType.*; import java.io.File; import java.io.IOException; import java.io.Reader; import java.io.Serializable; import java.io.Writer; +import java.nio.file.Path; import java.util.Arrays; +import java.util.Objects; /** *

Model stores the model obtained from the training procedure

* - *

use {@link Linear#loadModel(File)} and {@link Linear#saveModel(File, Model)} to load/save it

+ *

use {@link Linear#loadModel(Path)} and {@link Linear#saveModel(Path, Model)} to load/save it

*/ public final class Model implements Serializable { private static final long serialVersionUID = -6456047576741854834L; - double bias; + double bias; /** label of each class */ - int[] label; + int[] label; - int nr_class; + int nr_class; - int nr_feature; + int nr_feature; - SolverType solverType; + SolverType solverType; /** feature weight array */ - double[] w; + double[] w; + + /** one-class SVM only */ + double rho; /** * @return number of classes @@ -48,11 +53,16 @@ public int getNrFeature() { } public int[] getLabels() { - return copyOf(label, nr_class); + return Arrays.copyOf(label, nr_class); + } + + public SolverType getSolverType() { + return solverType; } /** - * The nr_feature*nr_class array w gives feature weights. We use one + * The array w gives feature weights; its size is + * nr_feature*nr_class but is nr_feature if nr_class = 2. We use one * against the rest for multi-class classification, so each feature * index corresponds to nr_class weight values. Weights are * organized in the following way @@ -71,7 +81,7 @@ public int[] getLabels() { * @return a copy of the feature weight array as described */ public double[] getFeatureWeights() { - return Linear.copyOf(w, w.length); + return Arrays.copyOf(w, w.length); } /** @@ -88,6 +98,84 @@ public double getBias() { return bias; } + private double get_w_value(int idx, int label_idx) { + if (idx < 0 || idx > nr_feature) { + return 0; + } + if (solverType.isSupportVectorRegression() || solverType.isOneClass()) { + return w[idx]; + } else { + if (label_idx < 0 || label_idx >= nr_class) { + return 0; + } + if (nr_class == 2 && solverType != MCSVM_CS) { + if (label_idx == 0) { + return w[idx]; + } else { + return -w[idx]; + } + } else { + return w[idx * nr_class + label_idx]; + } + } + } + + /** + * This function gives the coefficient for the feature with feature index = + * feat_idx and the class with label index = label_idx. Note that feat_idx + * starts from 1, while label_idx starts from 0. If feat_idx is not in the + * valid range (1 to nr_feature), then a zero value will be returned. For + * classification models, if label_idx is not in the valid range (0 to + * nr_class-1), then a zero value will be returned; for regression models, + * label_idx is ignored. + * + * @since 1.95 + */ + // feat_idx: starting from 1 to nr_feature + // label_idx: starting from 0 to nr_class-1 for classification models; + // for regression and one-class SVM models, label_idx is + // ignored. + public double getDecfunCoef(int featIdx, int labelIdx) { + if (featIdx > nr_feature) { + return 0; + } + return get_w_value(featIdx - 1, labelIdx); + } + + /** + * This function gives the bias term corresponding to the class with the + * label_idx. For classification models, if label_idx is not in a valid range + * (0 to nr_class-1), then a zero value will be returned; for regression + * models, label_idx is ignored. + * + * @since 1.95 + */ + public double getDecfunBias(int labelIdx) { + if (solverType.isOneClass()) { + throw new IllegalArgumentException("Can not be called for a one-class SVM model"); + } + int biasIdx = nr_feature; + if (bias <= 0) { + return 0; + } else { + return bias * get_w_value(biasIdx, labelIdx); + } + } + + /** + * This function gives rho, the bias term used in one-class SVM only. + * + * This function can only be called for a one-class SVM model. + * + * @since 2.40 + */ + public double getDecfunRho() { + if (!solverType.isOneClass()) { + throw new IllegalArgumentException("Can be called only for a one-class SVM model"); + } + return rho; + } + @Override public String toString() { StringBuilder sb = new StringBuilder("Model"); @@ -100,34 +188,28 @@ public String toString() { @Override public int hashCode() { - final int prime = 31; - int result = 1; - long temp; - temp = Double.doubleToLongBits(bias); - result = prime * result + (int)(temp ^ (temp >>> 32)); - result = prime * result + Arrays.hashCode(label); - result = prime * result + nr_class; - result = prime * result + nr_feature; - result = prime * result + ((solverType == null) ? 0 : solverType.hashCode()); - result = prime * result + Arrays.hashCode(w); + int result = Objects.hash(getBias(), nr_class, nr_feature, getSolverType(), rho); + result = 31 * result + Arrays.hashCode(label); + result = 31 * result + arrayHashCode(w); return result; } @Override - public boolean equals(Object obj) { - if (this == obj) return true; - if (obj == null) return false; - if (getClass() != obj.getClass()) return false; - Model other = (Model)obj; - if (Double.doubleToLongBits(bias) != Double.doubleToLongBits(other.bias)) return false; - if (!Arrays.equals(label, other.label)) return false; - if (nr_class != other.nr_class) return false; - if (nr_feature != other.nr_feature) return false; - if (solverType == null) { - if (other.solverType != null) return false; - } else if (!solverType.equals(other.solverType)) return false; - if (!equals(w, other.w)) return false; - return true; + public boolean equals(Object o) { + if (this == o) { + return true; + } + if (o == null || getClass() != o.getClass()) { + return false; + } + Model model = (Model)o; + return Double.compare(model.getBias(), getBias()) == 0 + && nr_class == model.nr_class + && nr_feature == model.nr_feature + && Double.compare(model.rho, rho) == 0 + && Arrays.equals(label, model.label) + && getSolverType() == model.getSolverType() + && arrayEquals(w, model.w); } /** @@ -135,24 +217,53 @@ public boolean equals(Object obj) { * * @see Linear#saveModel(java.io.Writer, Model) */ - protected static boolean equals(double[] a, double[] a2) { - if (a == a2) return true; - if (a == null || a2 == null) return false; + private static boolean arrayEquals(double[] a, double[] a2) { + if (a == a2) + return true; + if (a == null || a2 == null) + return false; int length = a.length; - if (a2.length != length) return false; + if (a2.length != length) + return false; for (int i = 0; i < length; i++) - if (a[i] != a2[i]) return false; + if (a[i] != a2[i]) + return false; return true; } /** - * see {@link Linear#saveModel(java.io.File, Model)} + * see {@link Arrays#hashCode(double[])} but treat 0.0 and -0.0 the same */ - public void save(File file) throws IOException { - Linear.saveModel(file, this); + private static int arrayHashCode(double[] w) { + if (w == null) + return 0; + + int result = 1; + for (double element : w) { + if (element == -0.0) { + element = 0.0; + } + result = 31 * result + Double.hashCode(element); + } + return result; + } + + /** + * @deprecated use {@link Model#save(Path)} instead + */ + @Deprecated + public void save(File modelFile) throws IOException { + save(modelFile.toPath()); + } + + /** + * see {@link Linear#saveModel(Path, Model)} + */ + public void save(Path modelPath) throws IOException { + Linear.saveModel(modelPath, this); } /** @@ -163,10 +274,18 @@ public void save(Writer writer) throws IOException { } /** - * see {@link Linear#loadModel(File)} + * @deprecated use {@link Model#load(Path)} instead + */ + @Deprecated + public static Model load(File modelFile) throws IOException { + return load(modelFile.toPath()); + } + + /** + * see {@link Linear#loadModel(Path)} */ - public static Model load(File file) throws IOException { - return Linear.loadModel(file); + public static Model load(Path modelPath) throws IOException { + return Linear.loadModel(modelPath); } /** diff --git a/src/main/java/de/bwaldvogel/liblinear/MutableDouble.java b/src/main/java/de/bwaldvogel/liblinear/MutableDouble.java new file mode 100644 index 0000000..7dbfe35 --- /dev/null +++ b/src/main/java/de/bwaldvogel/liblinear/MutableDouble.java @@ -0,0 +1,27 @@ +package de.bwaldvogel.liblinear; + +class MutableDouble { + + private boolean initialized; + private double value; + + public MutableDouble() { + } + + public MutableDouble(double value) { + set(value); + } + + void set(double value) { + this.value = value; + this.initialized = true; + } + + double get() { + if (!initialized) { + throw new IllegalStateException("Value not yet initialized"); + } + return value; + } + +} diff --git a/src/main/java/de/bwaldvogel/liblinear/MutableInt.java b/src/main/java/de/bwaldvogel/liblinear/MutableInt.java new file mode 100644 index 0000000..b6e3f4e --- /dev/null +++ b/src/main/java/de/bwaldvogel/liblinear/MutableInt.java @@ -0,0 +1,24 @@ +package de.bwaldvogel.liblinear; + +class MutableInt { + + private boolean initialized; + private int value; + + public MutableInt(int value) { + set(value); + } + + void set(int value) { + this.value = value; + this.initialized = true; + } + + int get() { + if (!initialized) { + throw new IllegalStateException("Value not yet initialized"); + } + return value; + } + +} diff --git a/src/main/java/de/bwaldvogel/liblinear/Newton.java b/src/main/java/de/bwaldvogel/liblinear/Newton.java new file mode 100644 index 0000000..8e0a63e --- /dev/null +++ b/src/main/java/de/bwaldvogel/liblinear/Newton.java @@ -0,0 +1,161 @@ +package de.bwaldvogel.liblinear; + +import static de.bwaldvogel.liblinear.Linear.*; + + +class Newton { + + private final Function fun_obj; + private final double eps; + private final int max_iter; + private final double eps_cg; + + Newton(Function fun_obj, double eps, int max_iter) { + this(fun_obj, eps, max_iter, 0.5); + } + + Newton(Function fun_obj, double eps, int max_iter, double eps_cg) { + this.fun_obj = fun_obj; + this.eps = eps; + this.max_iter = max_iter; + this.eps_cg = eps_cg; + } + + void newton(double[] w) { + int n = fun_obj.get_nr_variable(); + int i, cg_iter; + double step_size; + double f, fold, actred; + double init_step_size = 1; + boolean search = true; + int iter = 1; + MutableInt inc = new MutableInt(1); + double[] s = new double[n]; + double[] r = new double[n]; + double[] g = new double[n]; + + final double alpha_pcg = 0.01; + double[] M = new double[n]; + + // calculate gradient norm at w=0 for stopping condition. + double[] w0 = new double[n]; + for (i = 0; i < n; i++) + w0[i] = 0; + fun_obj.fun(w0); + fun_obj.grad(w0, g); + + double gnorm0 = Blas.dnrm2_(n, g, inc); + + f = fun_obj.fun(w); + fun_obj.grad(w, g); + double gnorm = Blas.dnrm2_(n, g, inc); + info("init f %5.3e |g| %5.3e%n", f, gnorm); + + if (gnorm <= eps * gnorm0) + search = false; + + while (iter <= max_iter && search) { + fun_obj.get_diag_preconditioner(M); + for (i = 0; i < n; i++) + M[i] = (1 - alpha_pcg) + alpha_pcg * M[i]; + cg_iter = pcg(g, M, s, r); + + fold = f; + MutableDouble fReference = new MutableDouble(f); + step_size = fun_obj.linesearch_and_update(w, s, fReference, g, init_step_size); + f = fReference.get(); + + if (step_size == 0) { + info("WARNING: line search fails%n"); + break; + } + + fun_obj.grad(w, g); + gnorm = Blas.dnrm2_(n, g, inc); + + info("iter %2d f %5.3e |g| %5.3e CG %3d step_size %4.2e%n", iter, f, gnorm, cg_iter, step_size); + + if (gnorm <= eps * gnorm0) + break; + if (f < -1.0e+32) { + info("WARNING: f < -1.0e+32%n"); + break; + } + actred = fold - f; + if (Math.abs(actred) <= 1.0e-12 * Math.abs(f)) { + info("WARNING: actred too small%n"); + break; + } + + iter++; + } + + if (iter >= max_iter) + info("%nWARNING: reaching max number of Newton iterations%n"); + } + + private int pcg(double[] g, double[] M, double[] s, double[] r) { + int i, inc = 1; + int n = fun_obj.get_nr_variable(); + double one = 1; + double[] d = new double[n]; + double[] Hd = new double[n]; + double zTr, znewTrnew, alpha, beta, cgtol, dHd; + double[] z = new double[n]; + double Q = 0, newQ, Qdiff; + + for (i = 0; i < n; i++) { + s[i] = 0; + r[i] = -g[i]; + z[i] = r[i] / M[i]; + d[i] = z[i]; + } + + zTr = Blas.ddot_(n, z, inc, r, inc); + double gMinv_norm = Math.sqrt(zTr); + cgtol = Math.min(eps_cg, Math.sqrt(gMinv_norm)); + int cg_iter = 0; + int max_cg_iter = Math.max(n, 5); + + while (cg_iter < max_cg_iter) { + cg_iter++; + + fun_obj.Hv(d, Hd); + dHd = Blas.ddot_(n, d, inc, Hd, inc); + // avoid 0/0 in getting alpha + if (dHd <= 1.0e-16) + break; + + alpha = zTr / dHd; + Blas.daxpy_(n, alpha, d, inc, s, inc); + alpha = -alpha; + Blas.daxpy_(n, alpha, Hd, inc, r, inc); + + // Using quadratic approximation as CG stopping criterion + newQ = -0.5 * (Blas.ddot_(n, s, inc, r, inc) - Blas.ddot_(n, s, inc, g, inc)); + Qdiff = newQ - Q; + if (newQ <= 0 && Qdiff <= 0) { + if (cg_iter * Qdiff >= cgtol * newQ) + break; + } else { + info("WARNING: quadratic approximation > 0 or increasing in CG%n"); + break; + } + Q = newQ; + + for (i = 0; i < n; i++) + z[i] = r[i] / M[i]; + znewTrnew = Blas.ddot_(n, z, inc, r, inc); + beta = znewTrnew / zTr; + Blas.dscal_(n, beta, d, inc); + Blas.daxpy_(n, one, z, inc, d, inc); + zTr = znewTrnew; + } + + if (cg_iter == max_cg_iter) + info("WARNING: reaching maximal number of CG steps%n"); + + return (cg_iter); + } + +} diff --git a/src/main/java/de/bwaldvogel/liblinear/Parameter.java b/src/main/java/de/bwaldvogel/liblinear/Parameter.java index 46df099..6608592 100644 --- a/src/main/java/de/bwaldvogel/liblinear/Parameter.java +++ b/src/main/java/de/bwaldvogel/liblinear/Parameter.java @@ -1,34 +1,72 @@ package de.bwaldvogel.liblinear; -import static de.bwaldvogel.liblinear.Linear.copyOf; +import java.io.ByteArrayInputStream; +import java.io.ByteArrayOutputStream; +import java.io.IOException; +import java.io.ObjectInputStream; +import java.io.ObjectOutputStream; +import java.util.Arrays; +import java.util.Random; -public final class Parameter { +public final class Parameter implements Cloneable { - double C; + private static final long DEFAULT_RANDOM_SEED = 0L; - /** stopping criteria */ - double eps; + double C; + + /** stopping tolerance */ + double eps; + + int max_iters = 1000; // maximal iterations SolverType solverType; - double[] weight = null; + double[] weight = null; + + int[] weightLabel = null; + + double p = 0.1; + + double nu = 0.5; + + /** + * Initial-solution specification (only supported for {@link SolverType#L2R_LR} and {@link SolverType#L2R_L2LOSS_SVC}) + */ + double[] init_sol = null; - int[] weightLabel = null; + boolean regularize_bias = true; - double p; + Random random = new Random(DEFAULT_RANDOM_SEED); - public Parameter( SolverType solver, double C, double eps ) { - this(solver, C, eps, 0.1); + public Parameter(SolverType solver, double C, double eps) { + setSolverType(solver); + setC(C); + setEps(eps); } - public Parameter( SolverType solverType, double C, double eps, double p ) { + public Parameter(SolverType solver, double C, int max_iters, double eps) { + setSolverType(solver); + setC(C); + setEps(eps); + setMaxIters(max_iters); + } + + public Parameter(SolverType solverType, double C, double eps, double p) { setSolverType(solverType); setC(C); setEps(eps); setP(p); } + public Parameter(SolverType solverType, double C, double eps, int max_iters, double p) { + setSolverType(solverType); + setC(C); + setEps(eps); + setMaxIters(max_iters); + setP(p); + } + /** *

nr_weight, weight_label, and weight are used to change the penalty * for some classes (If the weight for a class is not changed, it is @@ -42,25 +80,32 @@ public Parameter( SolverType solverType, double C, double eps, double p ) { * just set nr_weight to 0.

*/ public void setWeights(double[] weights, int[] weightLabels) { - if (weights == null) throw new IllegalArgumentException("'weight' must not be null"); + if (weights == null) + throw new IllegalArgumentException("'weight' must not be null"); if (weightLabels == null || weightLabels.length != weights.length) throw new IllegalArgumentException("'weightLabels' must have same length as 'weight'"); - this.weightLabel = copyOf(weightLabels, weightLabels.length); - this.weight = copyOf(weights, weights.length); + this.weightLabel = Arrays.copyOf(weightLabels, weightLabels.length); + this.weight = Arrays.copyOf(weights, weights.length); } /** * @see #setWeights(double[], int[]) */ public double[] getWeights() { - return copyOf(weight, weight.length); + if (weight == null) { + return null; + } + return Arrays.copyOf(weight, weight.length); } /** * @see #setWeights(double[], int[]) */ public int[] getWeightLabels() { - return copyOf(weightLabel, weightLabel.length); + if (weightLabel == null) { + return null; + } + return Arrays.copyOf(weightLabel, weightLabel.length); } /** @@ -68,7 +113,8 @@ public int[] getWeightLabels() { * @see #setWeights(double[], int[]) */ public int getNumWeights() { - if (weight == null) return 0; + if (weight == null) + return 0; return weight.length; } @@ -76,7 +122,8 @@ public int getNumWeights() { * C is the cost of constraints violation. (we usually use 1 to 1000) */ public void setC(double C) { - if (C <= 0) throw new IllegalArgumentException("C must not be <= 0"); + if (C <= 0) + throw new IllegalArgumentException("C must not be <= 0"); this.C = C; } @@ -88,7 +135,8 @@ public double getC() { * eps is the stopping criterion. (we usually use 0.01). */ public void setEps(double eps) { - if (eps <= 0) throw new IllegalArgumentException("eps must not be <= 0"); + if (eps <= 0) + throw new IllegalArgumentException("eps must not be <= 0"); this.eps = eps; } @@ -96,8 +144,19 @@ public double getEps() { return eps; } + public void setMaxIters(int iters) { + if (iters <= 0) + throw new IllegalArgumentException("max iters not be <= 0"); + this.max_iters = iters; + } + + public int getMaxIters() { + return max_iters; + } + public void setSolverType(SolverType solverType) { - if (solverType == null) throw new IllegalArgumentException("solver type must not be null"); + if (solverType == null) + throw new IllegalArgumentException("solver type must not be null"); this.solverType = solverType; } @@ -105,16 +164,88 @@ public SolverType getSolverType() { return solverType; } - /** * set the epsilon in loss function of epsilon-SVR (default 0.1) */ public void setP(double p) { - if (p < 0) throw new IllegalArgumentException("p must not be less than 0"); + if (p < 0) + throw new IllegalArgumentException("p must not be less than 0"); this.p = p; } public double getP() { return p; } + + /* + * Sets the initial-solution specification. + * Only supported for {@link SolverType#L2R_LR} and {@link SolverType#L2R_L2LOSS_SVC}. + */ + public void setInitSol(double[] init_sol) { + if (init_sol == null) + this.init_sol = null; + else { + this.init_sol = Arrays.copyOf(init_sol, init_sol.length); + } + } + + public double[] getInitSol() { + if (init_sol == null) { + return null; + } + return Arrays.copyOf(init_sol, init_sol.length); + } + + public void setNu(double nu) { + if (nu <= 0) + throw new IllegalArgumentException("nu must not be <=0"); + if (nu >= 1) + throw new IllegalArgumentException("nu must not be >=1"); + this.nu = nu; + } + + public double getNu() { + return nu; + } + + public void setRegularizeBias(boolean regularizeBias) { + this.regularize_bias = regularizeBias; + } + + public boolean isRegularizeBias() { + return regularize_bias; + } + + public void setRandom(Random random) { + this.random = random; + } + + @Override + public Parameter clone() { + Parameter clone = new Parameter(solverType, C, eps, max_iters, p); + clone.weight = weight == null ? null : weight.clone(); + clone.weightLabel = weightLabel == null ? null : weightLabel.clone(); + clone.init_sol = init_sol; + clone.p = p; + clone.nu = nu; + clone.regularize_bias = regularize_bias; + clone.random = deepClone(random); + return clone; + } + + private static Random deepClone(Random random) { + try { + try (ByteArrayOutputStream baos = new ByteArrayOutputStream(); + ObjectOutputStream out = new ObjectOutputStream(baos);) { + out.writeObject(random); + try (ByteArrayInputStream bis = new ByteArrayInputStream(baos.toByteArray()); + ObjectInputStream in = new ObjectInputStream(bis)) { + return (Random)in.readObject(); + } + } + } catch (IOException | ClassNotFoundException e) { + throw new RuntimeException("Failed to clone " + random, e); + } + } + } diff --git a/src/main/java/de/bwaldvogel/liblinear/ParameterCSearchResult.java b/src/main/java/de/bwaldvogel/liblinear/ParameterCSearchResult.java new file mode 100644 index 0000000..07b3c13 --- /dev/null +++ b/src/main/java/de/bwaldvogel/liblinear/ParameterCSearchResult.java @@ -0,0 +1,21 @@ +package de.bwaldvogel.liblinear; + +public class ParameterCSearchResult { + + private final double bestC; + private final double bestScore; + + public ParameterCSearchResult(double bestC, double bestScore) { + this.bestC = bestC; + this.bestScore = bestScore; + } + + public double getBestC() { + return bestC; + } + + public double getBestScore() { + return bestScore; + } + +} diff --git a/src/main/java/de/bwaldvogel/liblinear/ParameterSearchResult.java b/src/main/java/de/bwaldvogel/liblinear/ParameterSearchResult.java new file mode 100644 index 0000000..f88f96f --- /dev/null +++ b/src/main/java/de/bwaldvogel/liblinear/ParameterSearchResult.java @@ -0,0 +1,26 @@ +package de.bwaldvogel.liblinear; + +public class ParameterSearchResult { + + private final double bestC; + private final double bestScore; + private final double bestP; + + public ParameterSearchResult(double bestC, double bestScore, double bestP) { + this.bestC = bestC; + this.bestScore = bestScore; + this.bestP = bestP; + } + + public double getBestC() { + return bestC; + } + + public double getBestScore() { + return bestScore; + } + + public double getBestP() { + return bestP; + } +} diff --git a/src/main/java/de/bwaldvogel/liblinear/Predict.java b/src/main/java/de/bwaldvogel/liblinear/Predict.java index ce81e90..7d87e9b 100644 --- a/src/main/java/de/bwaldvogel/liblinear/Predict.java +++ b/src/main/java/de/bwaldvogel/liblinear/Predict.java @@ -1,20 +1,16 @@ package de.bwaldvogel.liblinear; -import static de.bwaldvogel.liblinear.Linear.atof; -import static de.bwaldvogel.liblinear.Linear.atoi; -import static de.bwaldvogel.liblinear.Linear.closeQuietly; -import static de.bwaldvogel.liblinear.Linear.printf; -import static de.bwaldvogel.liblinear.Linear.info; +import static de.bwaldvogel.liblinear.Linear.*; import java.io.BufferedReader; import java.io.BufferedWriter; -import java.io.File; import java.io.FileInputStream; import java.io.FileOutputStream; import java.io.IOException; import java.io.InputStreamReader; import java.io.OutputStreamWriter; import java.io.Writer; +import java.nio.file.Paths; import java.util.ArrayList; import java.util.Formatter; import java.util.List; @@ -25,14 +21,12 @@ public class Predict { - private static boolean flag_predict_probability = false; - - private static final Pattern COLON = Pattern.compile(":"); + private static final Pattern COLON = Pattern.compile(":"); /** *

Note: The streams are NOT closed

*/ - static void doPredict(BufferedReader reader, Writer writer, Model model) throws IOException { + static void doPredict(BufferedReader reader, Writer writer, Model model, boolean flag_predict_probability) throws IOException { int correct = 0; int total = 0; double error = 0; @@ -66,7 +60,7 @@ static void doPredict(BufferedReader reader, Writer writer, Model model) throws String line = null; while ((line = reader.readLine()) != null) { - List x = new ArrayList(); + List x = new ArrayList<>(); StringTokenizer st = new StringTokenizer(line, " \t\n"); double target_label; try { @@ -115,7 +109,7 @@ static void doPredict(BufferedReader reader, Writer writer, Model model) throws printf(out, "\n"); } else { predict_label = Linear.predict(model, nodes); - printf(out, "%g\n", predict_label); + printf(out, "%.17g\n", predict_label); } if (predict_label == target_label) { @@ -150,11 +144,14 @@ private static void exit_with_help() { } public static void main(String[] argv) throws IOException { + // Note: This flag is _static_ in predict.c but it causes a thread-safety issue as reported in https://github.com/bwaldvogel/liblinear-java/issues/38 + boolean flag_predict_probability = false; int i; // parse options for (i = 0; i < argv.length; i++) { - if (argv[i].charAt(0) != '-') break; + if (argv[i].charAt(0) != '-') + break; ++i; switch (argv[i - 1].charAt(1)) { case 'b': @@ -180,18 +177,12 @@ public static void main(String[] argv) throws IOException { exit_with_help(); } - BufferedReader reader = null; - Writer writer = null; - try { - reader = new BufferedReader(new InputStreamReader(new FileInputStream(argv[i]), Linear.FILE_CHARSET)); - writer = new BufferedWriter(new OutputStreamWriter(new FileOutputStream(argv[i + 2]), Linear.FILE_CHARSET)); - - Model model = Linear.loadModel(new File(argv[i + 1])); - doPredict(reader, writer, model); - } - finally { - closeQuietly(reader); - closeQuietly(writer); + try (FileInputStream in = new FileInputStream(argv[i]); + BufferedReader reader = new BufferedReader(new InputStreamReader(in, Linear.FILE_CHARSET)); + FileOutputStream out = new FileOutputStream(argv[i + 2]); + Writer writer = new BufferedWriter(new OutputStreamWriter(out, Linear.FILE_CHARSET))) { + Model model = Linear.loadModel(Paths.get(argv[i + 1])); + doPredict(reader, writer, model, flag_predict_probability); } } } diff --git a/src/main/java/de/bwaldvogel/liblinear/Problem.java b/src/main/java/de/bwaldvogel/liblinear/Problem.java index f9cad56..983d766 100644 --- a/src/main/java/de/bwaldvogel/liblinear/Problem.java +++ b/src/main/java/de/bwaldvogel/liblinear/Problem.java @@ -2,6 +2,9 @@ import java.io.File; import java.io.IOException; +import java.io.InputStream; +import java.nio.charset.Charset; +import java.nio.file.Path; /** @@ -34,13 +37,13 @@ public class Problem { /** the number of training data */ - public int l; + public int l; /** the number of features (including the bias feature if bias >= 0) */ - public int n; + public int n; /** an array containing the target values */ - public double[] y; + public double[] y; /** array of sparse feature nodes */ public Feature[][] x; @@ -49,12 +52,49 @@ public class Problem { * If bias >= 0, we assume that one additional feature is added * to the end of each data instance */ - public double bias; + public double bias = -1; /** - * see {@link Train#readProblem(File, double)} + * @deprecated use {@link Problem#readFromFile(Path, double)} instead */ + @Deprecated public static Problem readFromFile(File file, double bias) throws IOException, InvalidInputDataException { - return Train.readProblem(file, bias); + return readFromFile(file.toPath(), bias); + } + + /** + * see {@link Train#readProblem(Path, double)} + */ + public static Problem readFromFile(Path path, double bias) throws IOException, InvalidInputDataException { + return Train.readProblem(path, bias); + } + + /** + * @deprecated use {@link Problem#readFromFile(Path, Charset, double)} instead + */ + @Deprecated + public static Problem readFromFile(File file, Charset charset, double bias) throws IOException, InvalidInputDataException { + return readFromFile(file.toPath(), charset, bias); + } + + /** + * see {@link Train#readProblem(Path, Charset, double)} + */ + public static Problem readFromFile(Path path, Charset charset, double bias) throws IOException, InvalidInputDataException { + return Train.readProblem(path, charset, bias); + } + + /** + * see {@link Train#readProblem(InputStream, double)} + */ + public static Problem readFromStream(InputStream inputStream, double bias) throws IOException, InvalidInputDataException { + return Train.readProblem(inputStream, bias); + } + + /** + * see {@link Train#readProblem(InputStream, Charset, double)} + */ + public static Problem readFromStream(InputStream inputStream, Charset charset, double bias) throws IOException, InvalidInputDataException { + return Train.readProblem(inputStream, charset, bias); } } diff --git a/src/main/java/de/bwaldvogel/liblinear/SolverMCSVM_CS.java b/src/main/java/de/bwaldvogel/liblinear/SolverMCSVM_CS.java index 106598d..2c7e22f 100644 --- a/src/main/java/de/bwaldvogel/liblinear/SolverMCSVM_CS.java +++ b/src/main/java/de/bwaldvogel/liblinear/SolverMCSVM_CS.java @@ -1,8 +1,9 @@ package de.bwaldvogel.liblinear; -import static de.bwaldvogel.liblinear.Linear.copyOf; -import static de.bwaldvogel.liblinear.Linear.info; -import static de.bwaldvogel.liblinear.Linear.swap; +import static de.bwaldvogel.liblinear.Linear.*; + +import java.util.Arrays; +import java.util.Random; /** @@ -36,26 +37,19 @@ class SolverMCSVM_CS { private final double[] G; private final int max_iter; private final int w_size, l; - private final int nr_class; - private final Problem prob; - - public SolverMCSVM_CS( Problem prob, int nr_class, double[] C ) { - this(prob, nr_class, C, 0.1); - } - - public SolverMCSVM_CS( Problem prob, int nr_class, double[] C, double eps ) { - this(prob, nr_class, C, eps, 100000); - } - + private final int nr_class; + private final Problem prob; + private final Random random; - public SolverMCSVM_CS( Problem prob, int nr_class, double[] weighted_C, double eps, int max_iter ) { + public SolverMCSVM_CS(Problem prob, int nr_class, double[] C, double eps, Random random) { this.w_size = prob.n; this.l = prob.l; this.nr_class = nr_class; this.eps = eps; - this.max_iter = max_iter; + this.random = random; + this.max_iter = 100000; this.prob = prob; - this.C = weighted_C; + this.C = C; this.B = new double[nr_class]; this.G = new double[nr_class]; } @@ -66,8 +60,10 @@ private int GETI(int i) { private boolean be_shrunk(int i, int m, int yi, double alpha_i, double minG) { double bound = 0; - if (m == yi) bound = C[GETI(i)]; - if (alpha_i == bound && G[m] < minG) return true; + if (m == yi) + bound = C[GETI(i)]; + if (alpha_i == bound && G[m] < minG) + return true; return false; } @@ -122,7 +118,7 @@ public void solve(double[] w) { for (i = 0; i < active_size; i++) { // int j = i+rand()%(active_size-i); - int j = i + Linear.random.nextInt(active_size - i); + int j = i + random.nextInt(active_size - i); swap(index, i, j); } for (s = 0; s < active_size; s++) { @@ -138,7 +134,8 @@ public void solve(double[] w) { if (Ai > 0) { for (m = 0; m < active_size_i[i]; m++) G[m] = 1; - if (y_index[i] < active_size_i[i]) G[y_index[i]] = 0; + if (y_index[i] < active_size_i[i]) + G[y_index[i]] = 0; for (Feature xi : prob.x[i]) { // double *w_i = &w[(xi.index-1)*nr_class]; @@ -152,8 +149,10 @@ public void solve(double[] w) { double minG = Double.POSITIVE_INFINITY; double maxG = Double.NEGATIVE_INFINITY; for (m = 0; m < active_size_i[i]; m++) { - if (alpha_i.get(alpha_index_i.get(m)) < 0 && G[m] < minG) minG = G[m]; - if (G[m] > maxG) maxG = G[m]; + if (alpha_i.get(alpha_index_i.get(m)) < 0 && G[m] < minG) + minG = G[m]; + if (G[m] > maxG) + maxG = G[m]; } if (y_index[i] < active_size_i[i]) { if (alpha_i.get((int)prob.y[i]) < C[GETI(i)] && G[y_index[i]] < minG) { @@ -170,7 +169,8 @@ public void solve(double[] w) { swap(G, m, active_size_i[i]); if (y_index[i] == active_size_i[i]) y_index[i] = m; - else if (y_index[i] == m) y_index[i] = active_size_i[i]; + else if (y_index[i] == m) + y_index[i] = active_size_i[i]; break; } active_size_i[i]--; @@ -237,7 +237,8 @@ public void solve(double[] w) { } info("%noptimization finished, #iter = %d%n", iter); - if (iter >= max_iter) info("%nWARNING: reaching max number of iterations%n"); + if (iter >= max_iter) + info("%nWARNING: reaching max number of iterations%n"); // calculate objective value double v = 0; @@ -247,7 +248,8 @@ public void solve(double[] w) { v = 0.5 * v; for (i = 0; i < l * nr_class; i++) { v += alpha[i]; - if (Math.abs(alpha[i]) > 0) nSV++; + if (Math.abs(alpha[i]) > 0) + nSV++; } for (i = 0; i < l; i++) v -= alpha[i * nr_class + (int)prob.y[i]]; @@ -260,10 +262,11 @@ private void solve_sub_problem(double A_i, int yi, double C_yi, int active_i, do int r; assert active_i <= B.length; // no padding - double[] D = copyOf(B, active_i); + double[] D = Arrays.copyOf(B, active_i); // clone(D, B, active_i); - if (yi < active_i) D[yi] += A_i * C_yi; + if (yi < active_i) + D[yi] += A_i * C_yi; // qsort(D, active_i, sizeof(double), compare_double); ArraySorter.reversedMergesort(D); diff --git a/src/main/java/de/bwaldvogel/liblinear/SolverType.java b/src/main/java/de/bwaldvogel/liblinear/SolverType.java index 533e091..9914f00 100644 --- a/src/main/java/de/bwaldvogel/liblinear/SolverType.java +++ b/src/main/java/de/bwaldvogel/liblinear/SolverType.java @@ -81,23 +81,32 @@ public enum SolverType { */ L2R_L1LOSS_SVR_DUAL(13, false, true), + /** + * one-class support vector machine (dual) + * + * @since 2.40 + */ + ONECLASS_SVM(21, false, false), + ; private final boolean logisticRegressionSolver; private final boolean supportVectorRegression; private final int id; - private SolverType( int id, boolean logisticRegressionSolver, boolean supportVectorRegression ) { + SolverType(int id, boolean logisticRegressionSolver, boolean supportVectorRegression) { this.id = id; this.logisticRegressionSolver = logisticRegressionSolver; this.supportVectorRegression = supportVectorRegression; } - private static Map SOLVERS_BY_ID = new HashMap(); + private static final Map SOLVERS_BY_ID = new HashMap<>(); + static { for (SolverType solverType : SolverType.values()) { SolverType old = SOLVERS_BY_ID.put(Integer.valueOf(solverType.getId()), solverType); - if (old != null) throw new Error("duplicate solver type ID: " + solverType.getId()); + if (old != null) + throw new Error("duplicate solver type ID: " + solverType.getId()); } } @@ -126,4 +135,12 @@ public boolean isLogisticRegressionSolver() { public boolean isSupportVectorRegression() { return supportVectorRegression; } + + /** + * @since 2.40 + */ + public boolean isOneClass() { + return equals(ONECLASS_SVM); + } + } diff --git a/src/main/java/de/bwaldvogel/liblinear/SparseOperator.java b/src/main/java/de/bwaldvogel/liblinear/SparseOperator.java new file mode 100644 index 0000000..a09c691 --- /dev/null +++ b/src/main/java/de/bwaldvogel/liblinear/SparseOperator.java @@ -0,0 +1,52 @@ +package de.bwaldvogel.liblinear; + +class SparseOperator { + + static double nrm2_sq(Feature[] x) { + double ret = 0; + for (Feature feature : x) { + ret += feature.getValue() * feature.getValue(); + } + return (ret); + } + + static double dot(double[] s, Feature[] x) { + double ret = 0; + for (Feature feature : x) { + ret += s[feature.getIndex() - 1] * feature.getValue(); + } + return (ret); + } + + static double sparse_dot(Feature[] x1, Feature[] x2) { + double ret = 0; + + int pos1 = 0; + int pos2 = 0; + + while (pos1 < x1.length && pos2 < x2.length) { + int index1 = x1[pos1].getIndex(); + int index2 = x2[pos2].getIndex(); + if (index1 == index2) { + ret += x1[pos1].getValue() * x2[pos2].getValue(); + pos1++; + pos2++; + } else { + if (index1 > index2) { + pos2++; + } else { + pos1++; + } + } + } + + return (ret); + } + + static void axpy(double a, Feature[] x, double[] y) { + for (Feature feature : x) { + y[feature.getIndex() - 1] += a * feature.getValue(); + } + } + +} diff --git a/src/main/java/de/bwaldvogel/liblinear/Train.java b/src/main/java/de/bwaldvogel/liblinear/Train.java index 5fd7060..8489d1e 100644 --- a/src/main/java/de/bwaldvogel/liblinear/Train.java +++ b/src/main/java/de/bwaldvogel/liblinear/Train.java @@ -1,12 +1,17 @@ package de.bwaldvogel.liblinear; -import static de.bwaldvogel.liblinear.Linear.atof; -import static de.bwaldvogel.liblinear.Linear.atoi; +import static de.bwaldvogel.liblinear.Linear.*; +import static de.bwaldvogel.liblinear.SolverType.*; import java.io.BufferedReader; import java.io.File; -import java.io.FileReader; import java.io.IOException; +import java.io.InputStream; +import java.io.InputStreamReader; +import java.nio.charset.Charset; +import java.nio.file.Files; +import java.nio.file.Path; +import java.nio.file.Paths; import java.util.ArrayList; import java.util.List; import java.util.NoSuchElementException; @@ -20,6 +25,10 @@ public static void main(String[] args) throws IOException, InvalidInputDataExcep } private double bias = 1; + private boolean find_parameters = false; + private boolean C_specified = false; + private boolean P_specified = false; + private boolean solver_specified = false; private boolean cross_validation = false; private String inputFilename; private String modelFilename; @@ -27,6 +36,25 @@ public static void main(String[] args) throws IOException, InvalidInputDataExcep private Parameter param = null; private Problem prob = null; + private void do_find_parameters() { + double start_C, start_p; + if (C_specified) + start_C = param.C; + else + start_C = -1.0; + if (P_specified) + start_p = param.p; + else + start_p = -1.0; + + System.out.printf("Doing parameter search with %d-fold cross validation.%n", nr_fold); + ParameterSearchResult result = Linear.findParameters(prob, param, nr_fold, start_C, start_p); + if (param.getSolverType() == L2R_LR || param.getSolverType() == L2R_L2LOSS_SVC) + System.out.printf("Best C = %g CV accuracy = %g%%\n", result.getBestC(), 100.0 * result.getBestScore()); + else if (param.getSolverType() == L2R_L2LOSS_SVR) + System.out.printf("Best C = %g Best p = %g CV MSE = %g\n", result.getBestC(), result.getBestP(), result.getBestScore()); + } + private void do_cross_validation() { double total_error = 0; @@ -56,7 +84,8 @@ private void do_cross_validation() { } else { int total_correct = 0; for (int i = 0; i < prob.l; i++) - if (target[i] == prob.y[i]) ++total_correct; + if (target[i] == prob.y[i]) + ++total_correct; System.out.printf("correct: %d%n", total_correct); System.out.printf("Cross Validation Accuracy = %g%%%n", 100.0 * total_correct / prob.l); @@ -68,75 +97,91 @@ private void exit_with_help() { + "options:%n" + "-s type : set type of solver (default 1)%n" + " for multi-class classification%n" - + " 0 -- L2-regularized logistic regression (primal)%n" - + " 1 -- L2-regularized L2-loss support vector classification (dual)%n" - + " 2 -- L2-regularized L2-loss support vector classification (primal)%n" - + " 3 -- L2-regularized L1-loss support vector classification (dual)%n" - + " 4 -- support vector classification by Crammer and Singer%n" - + " 5 -- L1-regularized L2-loss support vector classification%n" - + " 6 -- L1-regularized logistic regression%n" - + " 7 -- L2-regularized logistic regression (dual)%n" + + " 0 -- L2-regularized logistic regression (primal)%n" + + " 1 -- L2-regularized L2-loss support vector classification (dual)%n" + + " 2 -- L2-regularized L2-loss support vector classification (primal)%n" + + " 3 -- L2-regularized L1-loss support vector classification (dual)%n" + + " 4 -- support vector classification by Crammer and Singer%n" + + " 5 -- L1-regularized L2-loss support vector classification%n" + + " 6 -- L1-regularized logistic regression%n" + + " 7 -- L2-regularized logistic regression (dual)%n" + " for regression%n" - + " 11 -- L2-regularized L2-loss support vector regression (primal)%n" - + " 12 -- L2-regularized L2-loss support vector regression (dual)%n" - + " 13 -- L2-regularized L1-loss support vector regression (dual)%n" + + " 11 -- L2-regularized L2-loss support vector regression (primal)%n" + + " 12 -- L2-regularized L2-loss support vector regression (dual)%n" + + " 13 -- L2-regularized L1-loss support vector regression (dual)%n" + + " for outlier detection%n" + + " 21 -- one-class support vector machine (dual)%n" + "-c cost : set the parameter C (default 1)%n" + "-p epsilon : set the epsilon in loss function of SVR (default 0.1)%n" + + "-n nu : set the parameter nu of one-class SVM (default 0.5)%n" + "-e epsilon : set tolerance of termination criterion%n" - + " -s 0 and 2%n" + " |f'(w)|_2 <= eps*min(pos,neg)/l*|f'(w0)|_2,%n" - + " where f is the primal function and pos/neg are # of%n" - + " positive/negative data (default 0.01)%n" + " -s 11%n" - + " |f'(w)|_2 <= eps*|f'(w0)|_2 (default 0.001)%n" - + " -s 1, 3, 4 and 7%n" + " Dual maximal violation <= eps; similar to libsvm (default 0.1)%n" - + " -s 5 and 6%n" - + " |f'(w)|_1 <= eps*min(pos,neg)/l*|f'(w0)|_1,%n" - + " where f is the primal function (default 0.01)%n" - + " -s 12 and 13\n" - + " |f'(alpha)|_1 <= eps |f'(alpha0)|,\n" - + " where f is the dual function (default 0.1)\n" + + " -s 0 and 2%n" + + " |f'(w)|_2 <= eps*min(pos,neg)/l*|f'(w0)|_2,%n" + + " where f is the primal function and pos/neg are # of%n" + + " positive/negative data (default 0.01)%n" + + " -s 11%n" + + " |f'(w)|_2 <= eps*|f'(w0)|_2 (default 0.0001)%n" + + " -s 1, 3, 4, 7, and 21%n" + + " Dual maximal violation <= eps; similar to libsvm (default 0.1 except 0.01 for -s 21)%n" + + " -s 5 and 6%n" + + " |f'(w)|_1 <= eps*min(pos,neg)/l*|f'(w0)|_1,%n" + + " where f is the primal function (default 0.01)%n" + + " -s 12 and 13%n" + + " |f'(alpha)|_1 <= eps |f'(alpha0)|,%n" + + " where f is the dual function (default 0.1)%n" + "-B bias : if bias >= 0, instance x becomes [x; bias]; if < 0, no bias term added (default -1)%n" + + "-R : not regularize the bias; must with -B 1 to have the bias; DON'T use this unless you know what it is%n" + + " (for -s 0, 2, 5, 6, 11)%n" + "-wi weight: weights adjust the parameter C of different classes (see README for details)%n" + "-v n: n-fold cross validation mode%n" + + "-C : find parameters (C for -s 0, 2 and C, p for -s 11)%n" + "-q : quiet mode (no outputs)%n"); System.exit(1); } - - Problem getProblem() { + public Problem getProblem() { return prob; } - double getBias() { + public double getBias() { return bias; } - Parameter getParameter() { + public Parameter getParameter() { return param; } - void parse_command_line(String argv[]) { + public void parse_command_line(String argv[]) { int i; // eps: see setting below - param = new Parameter(SolverType.L2R_L2LOSS_SVC_DUAL, 1, Double.POSITIVE_INFINITY, 0.1); + param = new Parameter(L2R_L2LOSS_SVC_DUAL, 1, Double.POSITIVE_INFINITY, 0.1); // default values bias = -1; cross_validation = false; // parse options for (i = 0; i < argv.length; i++) { - if (argv[i].charAt(0) != '-') break; - if (++i >= argv.length) exit_with_help(); + if (argv[i].charAt(0) != '-') + break; + if (++i >= argv.length) + exit_with_help(); switch (argv[i - 1].charAt(1)) { case 's': param.solverType = SolverType.getById(atoi(argv[i])); + solver_specified = true; break; case 'c': param.setC(atof(argv[i])); + C_specified = true; break; case 'p': + P_specified = true; param.setP(atof(argv[i])); break; + case 'n': + param.nu = atof(argv[i]); + break; case 'e': param.setEps(atof(argv[i])); break; @@ -161,6 +206,14 @@ void parse_command_line(String argv[]) { i--; Linear.disableDebugOutput(); break; + case 'C': + find_parameters = true; + i--; + break; + case 'R': + param.regularize_bias = false; + i--; + break; default: System.err.println("unknown option"); exit_with_help(); @@ -169,7 +222,8 @@ void parse_command_line(String argv[]) { // determine filenames - if (i >= argv.length) exit_with_help(); + if (i >= argv.length) + exit_with_help(); inputFilename = argv[i]; @@ -181,6 +235,19 @@ void parse_command_line(String argv[]) { modelFilename = argv[i].substring(p) + ".model"; } + // default solver for parameter selection is L2R_L2LOSS_SVC + if (find_parameters) { + if (!cross_validation) + nr_fold = 5; + if (!solver_specified) { + System.err.printf("Solver not specified. Using -s 2%n"); + param.setSolverType(L2R_L2LOSS_SVC); + } else if (param.getSolverType() != L2R_LR && param.getSolverType() != L2R_L2LOSS_SVC && param.getSolverType() != L2R_L2LOSS_SVR) { + System.err.printf("Warm-start parameter search only available for -s 0, -s 2 and -s 11%n"); + exit_with_help(); + } + } + if (param.eps == Double.POSITIVE_INFINITY) { switch (param.solverType) { case L2R_LR: @@ -188,7 +255,7 @@ void parse_command_line(String argv[]) { param.setEps(0.01); break; case L2R_L2LOSS_SVR: - param.setEps(0.001); + param.setEps(0.0001); break; case L2R_L2LOSS_SVC_DUAL: case L2R_L1LOSS_SVC_DUAL: @@ -204,6 +271,9 @@ void parse_command_line(String argv[]) { case L2R_L2LOSS_SVR_DUAL: param.setEps(0.1); break; + case ONECLASS_SVM: + param.setEps(0.01); + break; default: throw new IllegalStateException("unknown solver type: " + param.solverType); } @@ -215,82 +285,123 @@ void parse_command_line(String argv[]) { * @param file the SVM file * @throws IOException obviously in case of any I/O exception ;) * @throws InvalidInputDataException if the input file is not correctly formatted + * @deprecated use {@link Train#readProblem(Path, double)} instead */ + @Deprecated public static Problem readProblem(File file, double bias) throws IOException, InvalidInputDataException { - BufferedReader fp = new BufferedReader(new FileReader(file)); - List vy = new ArrayList(); - List vx = new ArrayList(); + return readProblem(file.toPath(), bias); + } + + /** + * reads a problem from LibSVM format + * @throws IOException obviously in case of any I/O exception ;) + * @throws InvalidInputDataException if the input file is not correctly formatted + */ + public static Problem readProblem(Path path, double bias) throws IOException, InvalidInputDataException { + try (InputStream inputStream = Files.newInputStream(path)) { + return readProblem(inputStream, bias); + } + } + + /** + * @deprecated use {@link Train#readProblem(Path, Charset, double)} instead + */ + @Deprecated + public static Problem readProblem(File file, Charset charset, double bias) throws IOException, InvalidInputDataException { + return readProblem(file.toPath(), charset, bias); + } + + public static Problem readProblem(Path path, Charset charset, double bias) throws IOException, InvalidInputDataException { + try (InputStream inputStream = Files.newInputStream(path)) { + return readProblem(inputStream, charset, bias); + } + } + + public static Problem readProblem(InputStream inputStream, double bias) throws IOException, InvalidInputDataException { + return readProblem(inputStream, Charset.defaultCharset(), bias); + } + + public static Problem readProblem(InputStream inputStream, Charset charset, double bias) throws IOException, InvalidInputDataException { + BufferedReader fp = new BufferedReader(new InputStreamReader(inputStream, charset)); + List vy = new ArrayList<>(); + List vx = new ArrayList<>(); int max_index = 0; int lineNr = 0; - try { - while (true) { - String line = fp.readLine(); - if (line == null) break; - lineNr++; + while (true) { + String line = fp.readLine(); + if (line == null) + break; + lineNr++; + + StringTokenizer st = new StringTokenizer(line, " \t\n\r\f:"); + String token; + try { + token = st.nextToken(); + } catch (NoSuchElementException e) { + throw new InvalidInputDataException("empty line", lineNr, e); + } - StringTokenizer st = new StringTokenizer(line, " \t\n\r\f:"); - String token; - try { - token = st.nextToken(); - } catch (NoSuchElementException e) { - throw new InvalidInputDataException("empty line", file, lineNr, e); - } + try { + vy.add(atof(token)); + } catch (NumberFormatException e) { + throw new InvalidInputDataException("invalid label: " + token, lineNr, e); + } + int m = st.countTokens() / 2; + Feature[] x; + if (bias >= 0) { + x = new Feature[m + 1]; + } else { + x = new Feature[m]; + } + int indexBefore = 0; + for (int j = 0; j < m; j++) { + + token = st.nextToken(); + int index; try { - vy.add(atof(token)); + index = atoi(token); } catch (NumberFormatException e) { - throw new InvalidInputDataException("invalid label: " + token, file, lineNr, e); + throw new InvalidInputDataException("invalid index: " + token, lineNr, e); } - int m = st.countTokens() / 2; - Feature[] x; - if (bias >= 0) { - x = new Feature[m + 1]; - } else { - x = new Feature[m]; - } - int indexBefore = 0; - for (int j = 0; j < m; j++) { - - token = st.nextToken(); - int index; - try { - index = atoi(token); - } catch (NumberFormatException e) { - throw new InvalidInputDataException("invalid index: " + token, file, lineNr, e); - } + // assert that indices are valid and sorted + if (index <= 0) + throw new InvalidInputDataException("invalid index: " + index, lineNr); + if (index <= indexBefore) + throw new InvalidInputDataException("indices must be sorted in ascending order", lineNr); + indexBefore = index; - // assert that indices are valid and sorted - if (index < 0) throw new InvalidInputDataException("invalid index: " + index, file, lineNr); - if (index <= indexBefore) throw new InvalidInputDataException("indices must be sorted in ascending order", file, lineNr); - indexBefore = index; - - token = st.nextToken(); - try { - double value = atof(token); - x[j] = new FeatureNode(index, value); - } catch (NumberFormatException e) { - throw new InvalidInputDataException("invalid value: " + token, file, lineNr); - } - } - if (m > 0) { - max_index = Math.max(max_index, x[m - 1].getIndex()); + token = st.nextToken(); + try { + double value = atof(token); + x[j] = new FeatureNode(index, value); + } catch (NumberFormatException e) { + throw new InvalidInputDataException("invalid value: " + token, lineNr); } - - vx.add(x); + } + if (m > 0) { + max_index = Math.max(max_index, x[m - 1].getIndex()); } - return constructProblem(vy, vx, max_index, bias); - } - finally { - fp.close(); + vx.add(x); } + + return constructProblem(vy, vx, max_index, bias); + } + + public void readProblem(Path path) throws IOException, InvalidInputDataException { + prob = Train.readProblem(path, bias); + } + + public void readProblem(String filename) throws IOException, InvalidInputDataException { + readProblem(filename, bias); } - void readProblem(String filename) throws IOException, InvalidInputDataException { - prob = Train.readProblem(new File(filename), bias); + public void readProblem(String filename, double bias) throws IOException, InvalidInputDataException { + prob = Train.readProblem(Paths.get(filename), bias); } private static int[] addToArray(int[] array, int newElement) { @@ -341,11 +452,21 @@ private static Problem constructProblem(List vy, List vx, int private void run(String[] args) throws IOException, InvalidInputDataException { parse_command_line(args); readProblem(inputFilename); - if (cross_validation) + if (find_parameters) { + do_find_parameters(); + } else if (cross_validation) do_cross_validation(); else { Model model = Linear.train(prob, param); - Linear.saveModel(new File(modelFilename), model); + Linear.saveModel(Paths.get(modelFilename), model); } } + + boolean isFindParameters() { + return find_parameters; + } + + int getNumFolds() { + return nr_fold; + } } diff --git a/src/main/java/de/bwaldvogel/liblinear/Tron.java b/src/main/java/de/bwaldvogel/liblinear/Tron.java deleted file mode 100644 index 23adf07..0000000 --- a/src/main/java/de/bwaldvogel/liblinear/Tron.java +++ /dev/null @@ -1,260 +0,0 @@ -package de.bwaldvogel.liblinear; - -import static de.bwaldvogel.liblinear.Linear.info; - -/** - * Trust Region Newton Method optimization - */ -class Tron { - - private final Function fun_obj; - - private final double eps; - - private final int max_iter; - - public Tron( final Function fun_obj ) { - this(fun_obj, 0.1); - } - - public Tron( final Function fun_obj, double eps ) { - this(fun_obj, eps, 1000); - } - - public Tron( final Function fun_obj, double eps, int max_iter ) { - this.fun_obj = fun_obj; - this.eps = eps; - this.max_iter = max_iter; - } - - void tron(double[] w) { - // Parameters for updating the iterates. - double eta0 = 1e-4, eta1 = 0.25, eta2 = 0.75; - - // Parameters for updating the trust region size delta. - double sigma1 = 0.25, sigma2 = 0.5, sigma3 = 4; - - int n = fun_obj.get_nr_variable(); - int i, cg_iter; - double delta, snorm, one = 1.0; - double alpha, f, fnew, prered, actred, gs; - int search = 1, iter = 1; - double[] s = new double[n]; - double[] r = new double[n]; - double[] w_new = new double[n]; - double[] g = new double[n]; - - for (i = 0; i < n; i++) - w[i] = 0; - - f = fun_obj.fun(w); - fun_obj.grad(w, g); - delta = euclideanNorm(g); - double gnorm1 = delta; - double gnorm = gnorm1; - - if (gnorm <= eps * gnorm1) search = 0; - - iter = 1; - - while (iter <= max_iter && search != 0) { - cg_iter = trcg(delta, g, s, r); - - System.arraycopy(w, 0, w_new, 0, n); - daxpy(one, s, w_new); - - gs = dot(g, s); - prered = -0.5 * (gs - dot(s, r)); - fnew = fun_obj.fun(w_new); - - // Compute the actual reduction. - actred = f - fnew; - - // On the first iteration, adjust the initial step bound. - snorm = euclideanNorm(s); - if (iter == 1) delta = Math.min(delta, snorm); - - // Compute prediction alpha*snorm of the step. - if (fnew - f - gs <= 0) - alpha = sigma3; - else - alpha = Math.max(sigma1, -0.5 * (gs / (fnew - f - gs))); - - // Update the trust region bound according to the ratio of actual to - // predicted reduction. - if (actred < eta0 * prered) - delta = Math.min(Math.max(alpha, sigma1) * snorm, sigma2 * delta); - else if (actred < eta1 * prered) - delta = Math.max(sigma1 * delta, Math.min(alpha * snorm, sigma2 * delta)); - else if (actred < eta2 * prered) - delta = Math.max(sigma1 * delta, Math.min(alpha * snorm, sigma3 * delta)); - else - delta = Math.max(delta, Math.min(alpha * snorm, sigma3 * delta)); - - info("iter %2d act %5.3e pre %5.3e delta %5.3e f %5.3e |g| %5.3e CG %3d%n", iter, actred, prered, delta, f, gnorm, cg_iter); - - if (actred > eta0 * prered) { - iter++; - System.arraycopy(w_new, 0, w, 0, n); - f = fnew; - fun_obj.grad(w, g); - - gnorm = euclideanNorm(g); - if (gnorm <= eps * gnorm1) break; - } - if (f < -1.0e+32) { - info("WARNING: f < -1.0e+32%n"); - break; - } - if (Math.abs(actred) <= 0 && prered <= 0) { - info("WARNING: actred and prered <= 0%n"); - break; - } - if (Math.abs(actred) <= 1.0e-12 * Math.abs(f) && Math.abs(prered) <= 1.0e-12 * Math.abs(f)) { - info("WARNING: actred and prered too small%n"); - break; - } - } - } - - private int trcg(double delta, double[] g, double[] s, double[] r) { - int n = fun_obj.get_nr_variable(); - double one = 1; - double[] d = new double[n]; - double[] Hd = new double[n]; - double rTr, rnewTrnew, cgtol; - - for (int i = 0; i < n; i++) { - s[i] = 0; - r[i] = -g[i]; - d[i] = r[i]; - } - cgtol = 0.1 * euclideanNorm(g); - - int cg_iter = 0; - rTr = dot(r, r); - - while (true) { - if (euclideanNorm(r) <= cgtol) break; - cg_iter++; - fun_obj.Hv(d, Hd); - - double alpha = rTr / dot(d, Hd); - daxpy(alpha, d, s); - if (euclideanNorm(s) > delta) { - info("cg reaches trust region boundary%n"); - alpha = -alpha; - daxpy(alpha, d, s); - - double std = dot(s, d); - double sts = dot(s, s); - double dtd = dot(d, d); - double dsq = delta * delta; - double rad = Math.sqrt(std * std + dtd * (dsq - sts)); - if (std >= 0) - alpha = (dsq - sts) / (std + rad); - else - alpha = (rad - std) / dtd; - daxpy(alpha, d, s); - alpha = -alpha; - daxpy(alpha, Hd, r); - break; - } - alpha = -alpha; - daxpy(alpha, Hd, r); - rnewTrnew = dot(r, r); - double beta = rnewTrnew / rTr; - scale(beta, d); - daxpy(one, r, d); - rTr = rnewTrnew; - } - - return (cg_iter); - } - - /** - * constant times a vector plus a vector - * - * 
-     * vector2 += constant * vector1
-     *
- * - * @since 1.8 - */ - private static void daxpy(double constant, double vector1[], double vector2[]) { - if (constant == 0) return; - - assert vector1.length == vector2.length; - for (int i = 0; i < vector1.length; i++) { - vector2[i] += constant * vector1[i]; - } - } - - /** - * returns the dot product of two vectors - * - * @since 1.8 - */ - private static double dot(double vector1[], double vector2[]) { - - double product = 0; - assert vector1.length == vector2.length; - for (int i = 0; i < vector1.length; i++) { - product += vector1[i] * vector2[i]; - } - return product; - - } - - /** - * returns the euclidean norm of a vector - * - * @since 1.8 - */ - private static double euclideanNorm(double vector[]) { - - int n = vector.length; - - if (n < 1) { - return 0; - } - - if (n == 1) { - return Math.abs(vector[0]); - } - - // this algorithm is (often) more accurate than just summing up the squares and taking the square-root afterwards - - double scale = 0; // scaling factor that is factored out - double sum = 1; // basic sum of squares from which scale has been factored out - for (int i = 0; i < n; i++) { - if (vector[i] != 0) { - double abs = Math.abs(vector[i]); - // try to get the best scaling factor - if (scale < abs) { - double t = scale / abs; - sum = 1 + sum * (t * t); - scale = abs; - } else { - double t = abs / scale; - sum += t * t; - } - } - } - - return scale * Math.sqrt(sum); - } - - /** - * scales a vector by a constant - * - * @since 1.8 - */ - private static void scale(double constant, double vector[]) { - if (constant == 1.0) return; - for (int i = 0; i < vector.length; i++) { - vector[i] *= constant; - } - - } -} diff --git a/src/test/java/de/bwaldvogel/liblinear/ArrayPointerTest.java b/src/test/java/de/bwaldvogel/liblinear/ArrayPointerTest.java index d7a7166..4a897ba 100644 --- a/src/test/java/de/bwaldvogel/liblinear/ArrayPointerTest.java +++ b/src/test/java/de/bwaldvogel/liblinear/ArrayPointerTest.java @@ -1,60 +1,55 @@ package de.bwaldvogel.liblinear; -import static org.fest.assertions.Assertions.assertThat; -import static org.fest.assertions.Fail.fail; +import static org.assertj.core.api.Assertions.*; -import org.junit.Test; +import org.junit.jupiter.api.Test; -public class ArrayPointerTest { +class ArrayPointerTest { @Test - public void testGetIntArrayPointer() { + void testGetIntArrayPointer() { int[] foo = new int[] {1, 2, 3, 4, 6}; IntArrayPointer pFoo = new IntArrayPointer(foo, 2); assertThat(pFoo.get(0)).isEqualTo(3); assertThat(pFoo.get(1)).isEqualTo(4); assertThat(pFoo.get(2)).isEqualTo(6); - try { - pFoo.get(3); - fail("ArrayIndexOutOfBoundsException expected"); - } catch (ArrayIndexOutOfBoundsException e) {} + + assertThatExceptionOfType(ArrayIndexOutOfBoundsException.class) + .isThrownBy(() -> pFoo.get(3)); } @Test - public void testSetIntArrayPointer() { + void testSetIntArrayPointer() { int[] foo = new int[] {1, 2, 3, 4, 6}; IntArrayPointer pFoo = new IntArrayPointer(foo, 2); pFoo.set(2, 5); assertThat(foo).isEqualTo(new int[] {1, 2, 3, 4, 5}); - try { - pFoo.set(3, 0); - fail("ArrayIndexOutOfBoundsException expected"); - } catch (ArrayIndexOutOfBoundsException e) {} + + assertThatExceptionOfType(ArrayIndexOutOfBoundsException.class) + .isThrownBy(() -> pFoo.set(3, 0)); } @Test - public void testGetDoubleArrayPointer() { + void testGetDoubleArrayPointer() { double[] foo = new double[] {1, 2, 3, 4, 6}; DoubleArrayPointer pFoo = new DoubleArrayPointer(foo, 2); assertThat(pFoo.get(0)).isEqualTo(3); assertThat(pFoo.get(1)).isEqualTo(4); assertThat(pFoo.get(2)).isEqualTo(6); - try { - pFoo.get(3); - fail("ArrayIndexOutOfBoundsException expected"); - } catch (ArrayIndexOutOfBoundsException e) {} + + assertThatExceptionOfType(ArrayIndexOutOfBoundsException.class) + .isThrownBy(() -> pFoo.get(3)); } @Test - public void testSetDoubleArrayPointer() { + void testSetDoubleArrayPointer() { double[] foo = new double[] {1, 2, 3, 4, 6}; DoubleArrayPointer pFoo = new DoubleArrayPointer(foo, 2); pFoo.set(2, 5); assertThat(foo).isEqualTo(new double[] {1, 2, 3, 4, 5}); - try { - pFoo.set(3, 0); - fail("ArrayIndexOutOfBoundsException expected"); - } catch (ArrayIndexOutOfBoundsException e) {} + + assertThatExceptionOfType(ArrayIndexOutOfBoundsException.class) + .isThrownBy(() -> pFoo.set(3, 0)); } } diff --git a/src/test/java/de/bwaldvogel/liblinear/ArraySorterTest.java b/src/test/java/de/bwaldvogel/liblinear/ArraySorterTest.java index 51a0b9e..3fdf9cf 100644 --- a/src/test/java/de/bwaldvogel/liblinear/ArraySorterTest.java +++ b/src/test/java/de/bwaldvogel/liblinear/ArraySorterTest.java @@ -1,22 +1,23 @@ package de.bwaldvogel.liblinear; -import static de.bwaldvogel.liblinear.Linear.swap; -import static org.fest.assertions.Assertions.assertThat; +import static de.bwaldvogel.liblinear.Linear.*; +import static org.assertj.core.api.Assertions.*; import java.util.Random; -import org.junit.Test; +import org.junit.jupiter.api.Test; -public class ArraySorterTest { +class ArraySorterTest { - private Random random = new Random(); + private final Random random = new Random(); private void assertDescendingOrder(double[] array) { double before = array[0]; for (double d : array) { // accept that case - if (d == 0.0 && before == -0.0) continue; + if (d == 0.0 && before == -0.0) + continue; assertThat(d).isLessThanOrEqualTo(before); before = d; @@ -32,8 +33,7 @@ private void shuffleArray(double[] array) { } @Test - public void testReversedMergesort() { - + void testReversedMergesort() { for (int k = 1; k <= 16 * 8096; k *= 2) { // create random array double[] array = new double[k]; @@ -47,7 +47,7 @@ public void testReversedMergesort() { } @Test - public void testReversedMergesortWithMeanValues() { + void testReversedMergesortWithMeanValues() { double[] array = new double[] {1.0, -0.0, -1.1, 2.0, 3.0, 0.0, 4.0, -0.0, 0.0}; shuffleArray(array); ArraySorter.reversedMergesort(array); diff --git a/src/test/java/de/bwaldvogel/liblinear/FeatureNodeTest.java b/src/test/java/de/bwaldvogel/liblinear/FeatureNodeTest.java index 6af22b0..4a27fd7 100644 --- a/src/test/java/de/bwaldvogel/liblinear/FeatureNodeTest.java +++ b/src/test/java/de/bwaldvogel/liblinear/FeatureNodeTest.java @@ -1,25 +1,31 @@ package de.bwaldvogel.liblinear; -import static org.fest.assertions.Assertions.assertThat; +import static org.assertj.core.api.Assertions.*; -import org.junit.Test; +import org.junit.jupiter.api.Test; +import nl.jqno.equalsverifier.EqualsVerifier; +import nl.jqno.equalsverifier.Warning; -public class FeatureNodeTest { + +class FeatureNodeTest { @Test - public void testConstructorIndexZero() { - // since 1.5 there's no more exception here - new FeatureNode(0, 0); + void testConstructorIndexZero() { + assertThatExceptionOfType(IllegalArgumentException.class) + .isThrownBy(() -> new FeatureNode(0, 0)) + .withMessage("index must be > 0"); } - @Test(expected = IllegalArgumentException.class) - public void testConstructorIndexNegative() { - new FeatureNode(-1, 0); + @Test + void testConstructorIndexNegative() { + assertThatExceptionOfType(IllegalArgumentException.class) + .isThrownBy(() -> new FeatureNode(-1, 0)) + .withMessage("index must be > 0"); } @Test - public void testConstructorHappy() { + void testConstructorHappy() { Feature fn = new FeatureNode(25, 27.39); assertThat(fn.getIndex()).isEqualTo(25); assertThat(fn.getValue()).isEqualTo(27.39); @@ -28,4 +34,33 @@ public void testConstructorHappy() { assertThat(fn.getIndex()).isEqualTo(1); assertThat(fn.getValue()).isEqualTo(-0.22222); } + + @Test + void testEqualsAndHashCodeContract() throws Exception { + EqualsVerifier.forClass(FeatureNode.class) + .usingGetClass() + .suppress(Warning.NONFINAL_FIELDS) + .verify(); + } + + @Test + void testEqualsAndHashCodeNaNValue() throws Exception { + FeatureNode a = new FeatureNode(1, Double.NaN); + FeatureNode b = new FeatureNode(1, Double.NaN); + assertThat(a).isEqualTo(b); + assertThat(a).hasSameHashCodeAs(b); + } + + @Test + void testEqualsWithPositiveAndNegativeZeroValue() throws Exception { + FeatureNode a = new FeatureNode(1, -0.0); + FeatureNode b = new FeatureNode(1, +0.0); + assertThat(a).isNotEqualTo(b); + } + + @Test + void testToString() throws Exception { + assertThat(new FeatureNode(1, 2.0)).hasToString("FeatureNode(idx=1, value=2.0)"); + } + } diff --git a/src/test/java/de/bwaldvogel/liblinear/LinearTest.java b/src/test/java/de/bwaldvogel/liblinear/LinearTest.java index b6af1d7..b08bc89 100644 --- a/src/test/java/de/bwaldvogel/liblinear/LinearTest.java +++ b/src/test/java/de/bwaldvogel/liblinear/LinearTest.java @@ -1,39 +1,45 @@ package de.bwaldvogel.liblinear; -import static org.fest.assertions.Assertions.assertThat; -import static org.fest.assertions.Fail.fail; -import static org.mockito.Mockito.doThrow; -import static org.mockito.Mockito.times; -import static org.mockito.Mockito.verify; +import static de.bwaldvogel.liblinear.SolverType.*; +import static de.bwaldvogel.liblinear.TestUtils.*; +import static org.assertj.core.api.Assertions.*; +import static org.mockito.Mockito.*; -import java.io.File; import java.io.IOException; import java.io.Writer; +import java.nio.file.Path; +import java.nio.file.Paths; import java.util.ArrayList; +import java.util.Arrays; import java.util.Collections; +import java.util.EnumSet; import java.util.List; import java.util.Random; import java.util.Set; import java.util.TreeSet; -import org.fest.assertions.Delta; -import org.junit.BeforeClass; -import org.junit.Test; -import org.powermock.api.mockito.PowerMockito; +import org.assertj.core.data.Offset; +import org.junit.jupiter.api.BeforeEach; +import org.junit.jupiter.api.Test; +import org.junit.jupiter.api.io.TempDir; -public class LinearTest { +class LinearTest { - private static Random random = new Random(12345); + private static final Random random = new Random(12345); - @BeforeClass - public static void disableDebugOutput() { + @BeforeEach + public void reset() throws Exception { Linear.disableDebugOutput(); } - public static Model createRandomModel() { + static Model createRandomModel() { + return createRandomModel(L2R_LR); + } + + static Model createRandomModel(SolverType solverType) { Model model = new Model(); - model.solverType = SolverType.L2R_LR; + model.solverType = solverType; model.bias = 2; model.label = new int[] {1, Integer.MAX_VALUE, 2}; model.w = new double[model.label.length * 300]; @@ -48,10 +54,13 @@ public static Model createRandomModel() { model.nr_feature = model.w.length / model.label.length - 1; model.nr_class = model.label.length; + if (solverType.isOneClass()) { + model.rho = random.nextDouble(); + } return model; } - public static Problem createRandomProblem(int numClasses) { + private static Problem createRandomProblem(int numClasses) { Problem prob = new Problem(); prob.bias = -1; prob.l = random.nextInt(100) + 1; @@ -63,12 +72,12 @@ public static Problem createRandomProblem(int numClasses) { prob.y[i] = random.nextInt(numClasses); - Set randomNumbers = new TreeSet(); + Set randomNumbers = new TreeSet<>(); int num = random.nextInt(prob.n) + 1; for (int j = 0; j < num; j++) { randomNumbers.add(random.nextInt(prob.n) + 1); } - List randomIndices = new ArrayList(randomNumbers); + List randomIndices = new ArrayList<>(randomNumbers); Collections.sort(randomIndices); prob.x[i] = new FeatureNode[randomIndices.size()]; @@ -83,7 +92,7 @@ public static Problem createRandomProblem(int numClasses) { * create a very simple problem and check if the clearly separated examples are recognized as such */ @Test - public void testTrainPredict() { + void testTrainPredict() { Problem prob = new Problem(); prob.bias = -1; prob.l = 4; @@ -111,11 +120,17 @@ public void testTrainPredict() { prob.y[3] = 0; for (SolverType solver : SolverType.values()) { + if (solver.isOneClass()) { + continue; + } for (double C = 0.1; C <= 100.; C *= 1.2) { - // compared the behavior with the C version - if (C < 0.2) if (solver == SolverType.L1R_L2LOSS_SVC) continue; - if (C < 0.7) if (solver == SolverType.L1R_LR) continue; + if (C < 0.2) + if (solver == L1R_L2LOSS_SVC) + continue; + if (C < 0.7) + if (solver == L1R_LR) + continue; if (solver.isSupportVectorRegression()) { continue; @@ -125,7 +140,7 @@ public void testTrainPredict() { Model model = Linear.train(prob, param); double[] featureWeights = model.getFeatureWeights(); - if (solver == SolverType.MCSVM_CS) { + if (solver == MCSVM_CS) { assertThat(featureWeights.length).isEqualTo(8); } else { assertThat(featureWeights.length).isEqualTo(4); @@ -144,7 +159,7 @@ public void testTrainPredict() { for (double estimate : estimates) { estimationSum += estimate; } - assertThat(estimationSum).isEqualTo(1.0, Delta.delta(0.001)); + assertThat(estimationSum).isEqualTo(1.0, offset(0.001)); } i++; } @@ -153,13 +168,12 @@ public void testTrainPredict() { } @Test - public void testCrossValidation() throws Exception { - + void testCrossValidation() throws Exception { int numClasses = random.nextInt(10) + 1; Problem prob = createRandomProblem(numClasses); - Parameter param = new Parameter(SolverType.L2R_LR, 10, 0.01); + Parameter param = new Parameter(L2R_LR, 10, 0.01); int nr_fold = 10; double[] target = new double[prob.l]; Linear.crossValidation(prob, param, nr_fold, target); @@ -170,15 +184,11 @@ public void testCrossValidation() throws Exception { } @Test - public void testLoadSaveModel() throws Exception { - - Model model = null; + void testLoadSaveModel(@TempDir Path tempDir) throws Exception { for (SolverType solverType : SolverType.values()) { - model = createRandomModel(); - model.solverType = solverType; + Model model = createRandomModel(solverType); - File tempFile = File.createTempFile("liblinear", "modeltest"); - tempFile.deleteOnExit(); + Path tempFile = tempDir.resolve("modeltest-" + solverType); Linear.saveModel(tempFile, model); Model loadedModel = Linear.loadModel(tempFile); @@ -187,7 +197,73 @@ public void testLoadSaveModel() throws Exception { } @Test - public void testTrainUnsortedProblem() { + void testLoadEmptyModel(@TempDir Path tempDir) throws Exception { + Path modelPath = tempDir.resolve("empty-model"); + + List lines = Arrays.asList("solver_type L2R_LR", + "nr_class 2", + "label 1 2", + "nr_feature 0", + "bias -1.0", + "w"); + writeToFile(modelPath, lines); + + Model model = Model.load(modelPath); + assertThat(model.getSolverType()).isEqualTo(L2R_LR); + assertThat(model.getLabels()).containsExactly(1, 2); + assertThat(model.getNrClass()).isEqualTo(2); + assertThat(model.getNrFeature()).isEqualTo(0); + assertThat(model.getFeatureWeights()).isEmpty(); + assertThat(model.getBias()).isEqualTo(-1.0); + } + + @Test + void testLoadSimpleModel(@TempDir Path tempDir) throws Exception { + Path modelPath = tempDir.resolve("simple-model"); + + List lines = Arrays.asList("solver_type L2R_L2LOSS_SVR", + "nr_class 2", + "label 1 2", + "nr_feature 6", + "bias -1.0", + "w", + "0.1 0.2 0.3 ", + "0.4 0.5 0.6 "); + writeToFile(modelPath, lines); + + Model model = Model.load(modelPath); + assertThat(model.getSolverType()).isEqualTo(L2R_L2LOSS_SVR); + assertThat(model.getLabels()).containsExactly(1, 2); + assertThat(model.getNrClass()).isEqualTo(2); + assertThat(model.getNrFeature()).isEqualTo(6); + assertThat(model.getFeatureWeights()).containsExactly(0.1, 0.2, 0.3, 0.4, 0.5, 0.6); + assertThat(model.getBias()).isEqualTo(-1.0, offset(0.001)); + } + + @Test + void testLoadIllegalModel(@TempDir Path tempDir) throws Exception { + Path file = tempDir.resolve("illegal-model"); + + List lines = Arrays.asList("solver_type L2R_L2LOSS_SVR", + "nr_class 2", + "label 1 2", + "nr_feature 10", + "bias -1.0", + "w", + "0.1 0.2 0.3 ", + "0.4 0.5 " + repeat("0", 1024)); + writeToFile(file, lines); + + String x = repeat("0", 128); + + assertThatExceptionOfType(RuntimeException.class) + .isThrownBy(() -> Model.load(file)) + .withMessage("illegal weight in model file at index 5, with string content '" + x + + "', is not terminated with a whitespace character, or is longer than expected (128 characters max)."); + } + + @Test + void testTrainUnsortedProblem() { Problem prob = new Problem(); prob.bias = -1; prob.l = 1; @@ -201,17 +277,15 @@ public void testTrainUnsortedProblem() { prob.y = new double[4]; prob.y[0] = 0; - Parameter param = new Parameter(SolverType.L2R_LR, 10, 0.1); - try { - Linear.train(prob, param); - fail("IllegalArgumentException expected"); - } catch (IllegalArgumentException e) { - assertThat(e.getMessage()).contains("nodes").contains("sorted").contains("ascending").contains("order"); - } + Parameter param = new Parameter(L2R_LR, 10, 0.1); + + assertThatExceptionOfType(IllegalArgumentException.class) + .isThrownBy(() -> Linear.train(prob, param)) + .withMessageContainingAll("nodes", "sorted", "ascending", "order"); } @Test - public void testTrainTooLargeProblem() { + void testTrainTooLargeProblem() { Problem prob = new Problem(); prob.l = 1000; prob.n = 20000000; @@ -223,19 +297,91 @@ public void testTrainTooLargeProblem() { } for (SolverType solverType : SolverType.values()) { - if (solverType.isSupportVectorRegression()) continue; + if (solverType.isSupportVectorRegression() || solverType.isOneClass()) { + continue; + } Parameter param = new Parameter(solverType, 10, 0.1); - try { + + assertThatExceptionOfType(IllegalArgumentException.class) + .isThrownBy(() -> Linear.train(prob, param)) + .withMessageContainingAll("number of classes", "too large"); + } + } + + @Test + void testTrain_IllegalParameters_BiasWithOneClassSvm() { + Problem prob = createRandomProblem(2); + prob.bias = 1; + + Parameter param = new Parameter(ONECLASS_SVM, 10, 0.1); + + assertThatExceptionOfType(IllegalArgumentException.class) + .isThrownBy(() -> Linear.train(prob, param)) + .withMessage("prob->bias >=0, but this is ignored in ONECLASS_SVM"); + } + + @Test + void testTrain_IllegalParameters_RegularizeBias() { + Problem prob = createRandomProblem(2); + prob.bias = -1; + + Parameter param = new Parameter(L2R_L1LOSS_SVR_DUAL, 10, 0.1); + param.setRegularizeBias(false); + + assertThatExceptionOfType(IllegalArgumentException.class) + .isThrownBy(() -> Linear.train(prob, param)) + .withMessage("To not regularize bias, must specify -B 1 along with -R"); + + prob.bias = 1; + + assertThatExceptionOfType(IllegalArgumentException.class) + .isThrownBy(() -> Linear.train(prob, param)) + .withMessage("-R option supported only for solver L2R_LR, L2R_L2LOSS_SVC, L1R_L2LOSS_SVC, L1R_LR, and L2R_L2LOSS_SVR"); + + param.setSolverType(L1R_LR); + + Model model = Linear.train(prob, param); + assertThat(model.bias).isEqualTo(1.0); + } + + @Test + void testTrain_IllegalParameters_P() throws Exception { + Problem prob = createRandomProblem(2); + Parameter param = new Parameter(L2R_L2LOSS_SVR, 10, 0.1); + param.p = -1; + + assertThatExceptionOfType(IllegalArgumentException.class) + .isThrownBy(() -> Linear.train(prob, param)) + .withMessage("p < 0"); + + // It must not fail for solvers other than L2R_L2LOSS_SVR + for (SolverType solverType : values()) { + if (solverType != L2R_L2LOSS_SVR) { + param.solverType = solverType; Linear.train(prob, param); - fail("IllegalArgumentException expected"); - } catch (IllegalArgumentException e) { - assertThat(e.getMessage()).contains("number of classes").contains("too large"); } } } @Test - public void testPredictProbabilityWrongSolver() throws Exception { + void testTrain_IllegalParameters_InitialSol() { + Problem prob = createRandomProblem(2); + + Parameter param = new Parameter(L2R_L1LOSS_SVR_DUAL, 10, 0.1); + param.setInitSol(new double[prob.n]); + + assertThatExceptionOfType(IllegalArgumentException.class) + .isThrownBy(() -> Linear.train(prob, param)) + .withMessage("Initial-solution specification supported only for solvers L2R_LR, L2R_L2LOSS_SVC, and L2R_L2LOSS_SVR"); + + param.setSolverType(L2R_LR); + + Model model = Linear.train(prob, param); + assertThat(model).isNotNull(); + } + + @Test + void testPredictProbabilityWrongSolver() throws Exception { Problem prob = new Problem(); prob.l = 1; prob.n = 1; @@ -246,31 +392,18 @@ public void testPredictProbabilityWrongSolver() throws Exception { prob.y[i] = i; } - SolverType solverType = SolverType.L2R_L1LOSS_SVC_DUAL; - Parameter param = new Parameter(solverType, 10, 0.1); + Parameter param = new Parameter(L2R_L1LOSS_SVC_DUAL, 10, 0.1); Model model = Linear.train(prob, param); - try { - Linear.predictProbability(model, prob.x[0], new double[1]); - fail("IllegalArgumentException expected"); - } catch (IllegalArgumentException e) { - assertThat(e.getMessage()).isEqualTo("probability output is only supported for logistic regression." // - + " This is currently only supported by the following solvers:" // - + " L2R_LR, L1R_LR, L2R_LR_DUAL"); - } - } - - @Test - public void testRealloc() { - int[] f = new int[] {1, 2, 3}; - f = Linear.copyOf(f, 5); - f[3] = 4; - f[4] = 5; - assertThat(f).isEqualTo(new int[] {1, 2, 3, 4, 5}); + assertThatExceptionOfType(IllegalArgumentException.class) + .isThrownBy(() -> Linear.predictProbability(model, prob.x[0], new double[1])) + .withMessage("probability output is only supported for logistic regression." + + " This is currently only supported by the following solvers:" + + " L2R_LR, L1R_LR, L2R_LR_DUAL"); } @Test - public void testAtoi() { + void testAtoi() { assertThat(Linear.atoi("+25")).isEqualTo(25); assertThat(Linear.atoi("-345345")).isEqualTo(-345345); assertThat(Linear.atoi("+0")).isEqualTo(0); @@ -279,49 +412,44 @@ public void testAtoi() { assertThat(Linear.atoi("-2147483648")).isEqualTo(Integer.MIN_VALUE); } - @Test(expected = NumberFormatException.class) - public void testAtoiInvalidData() { - Linear.atoi("+"); - } + @Test + void testAtoiInvalidData() { + assertThatExceptionOfType(NumberFormatException.class) + .isThrownBy(() -> Linear.atoi("+")); - @Test(expected = NumberFormatException.class) - public void testAtoiInvalidData2() { - Linear.atoi("abc"); - } + assertThatExceptionOfType(NumberFormatException.class) + .isThrownBy(() -> Linear.atoi("abc")); - @Test(expected = NumberFormatException.class) - public void testAtoiInvalidData3() { - Linear.atoi(" "); + assertThatExceptionOfType(NumberFormatException.class) + .isThrownBy(() -> Linear.atoi(" ")); } @Test - public void testAtof() { + void testAtof() { assertThat(Linear.atof("+25")).isEqualTo(25); assertThat(Linear.atof("-25.12345678")).isEqualTo(-25.12345678); assertThat(Linear.atof("0.345345299")).isEqualTo(0.345345299); } - @Test(expected = NumberFormatException.class) - public void testAtofInvalidData() { - Linear.atof("0.5t"); + @Test + void testAtofInvalidData() { + assertThatExceptionOfType(NumberFormatException.class) + .isThrownBy(() -> Linear.atof("0.5t")); } @Test - public void testSaveModelWithIOException() throws Exception { + void testSaveModelWithIOException() throws Exception { Model model = createRandomModel(); - Writer out = PowerMockito.mock(Writer.class); + Writer out = mock(Writer.class); IOException ioException = new IOException("some reason"); doThrow(ioException).when(out).flush(); - try { - Linear.saveModel(out, model); - fail("IOException expected"); - } catch (IOException e) { - assertThat(e).isEqualTo(ioException); - } + assertThatExceptionOfType(IOException.class) + .isThrownBy(() -> Linear.saveModel(out, model)) + .withMessage("some reason"); verify(out).flush(); verify(out, times(1)).close(); @@ -350,7 +478,7 @@ public void testSaveModelWithIOException() throws Exception { * */ @Test - public void testTranspose() throws Exception { + void testTranspose() throws Exception { Problem prob = new Problem(); prob.bias = -1; prob.l = 4; @@ -429,7 +557,7 @@ public void testTranspose() throws Exception { * */ @Test - public void testTranspose2() throws Exception { + void testTranspose2() throws Exception { Problem prob = new Problem(); prob.bias = -1; prob.l = 5; @@ -543,8 +671,7 @@ public void testTranspose2() throws Exception { * */ @Test - public void testTranspose3() throws Exception { - + void testTranspose3() throws Exception { Problem prob = new Problem(); prob.l = 3; prob.n = 4; @@ -568,11 +695,7 @@ public void testTranspose3() throws Exception { prob.x[3][0] = new FeatureNode(3, 2); Problem transposed = Linear.transpose(prob); - assertThat(transposed.x).hasSize(4); - assertThat(transposed.x[0]).hasSize(2); - assertThat(transposed.x[1]).hasSize(2); - assertThat(transposed.x[2]).hasSize(2); - assertThat(transposed.x[3]).hasSize(2); + assertThat(transposed.x).hasDimensions(4, 2); assertThat(transposed.x[0][0]).isEqualTo(new FeatureNode(1, 2)); assertThat(transposed.x[0][1]).isEqualTo(new FeatureNode(2, 9)); @@ -586,4 +709,83 @@ public void testTranspose3() throws Exception { assertThat(transposed.x[3][0]).isEqualTo(new FeatureNode(1, 3)); assertThat(transposed.x[3][1]).isEqualTo(new FeatureNode(2, 3)); } + + @Test + void testFindBestParametersOnIrisDataSet() throws Exception { + Problem problem = Train.readProblem(Paths.get("src/test/resources/iris.scale"), -1); + Parameter param = new Parameter(L2R_L2LOSS_SVC, 1, 0.001, 0.1); + ParameterSearchResult result = Linear.findParameters(problem, param, 5, -1, -1); + assertThat(result.getBestC()).isEqualTo(4); + assertThat(result.getBestScore()).isEqualTo(0.88); + assertThat(result.getBestP()).isEqualTo(-1); + } + + @Test + void testFindBestParameterC_IllegalSolver() throws Exception { + Problem problem = Train.readProblem(Paths.get("src/test/resources/iris.scale"), -1); + + EnumSet supportedSolvers = EnumSet.of(L2R_LR, L2R_L2LOSS_SVC, L2R_L2LOSS_SVR); + for (SolverType illegalSolver : EnumSet.complementOf(supportedSolvers)) { + Parameter param = new Parameter(illegalSolver, 1, 0.001, 0.1); + assertThatExceptionOfType(IllegalArgumentException.class) + .isThrownBy(() -> Linear.findParameters(problem, param, 5, -1, -1)) + .withMessage("Unsupported solver: " + illegalSolver); + } + } + + @Test + void testFindBestParametersOnSpliceDataSet() throws Exception { + Problem problem = Train.readProblem(Paths.get("src/test/datasets/splice/splice"), -1); + Parameter param = new Parameter(L2R_L2LOSS_SVC, 1, 0.001, 0.1); + ParameterSearchResult result = Linear.findParameters(problem, param, 5, -1, -1); + assertThat(result.getBestC()).isEqualTo(0.001953125); + assertThat(result.getBestScore()).isEqualTo(0.81); + assertThat(result.getBestP()).isEqualTo(-1); + } + + @Test + void testFindBestParametersOnSpliceDataSet_L2R_LR() throws Exception { + Problem problem = Train.readProblem(Paths.get("src/test/datasets/splice/splice"), -1); + Parameter param = new Parameter(L2R_LR, 1, 0.001, 0.1); + ParameterSearchResult result = Linear.findParameters(problem, param, 5, -1, -1); + assertThat(result.getBestC()).isEqualTo(0.015625); + assertThat(result.getBestScore()).isEqualTo(0.812); + assertThat(result.getBestP()).isEqualTo(-1); + } + + @Test + void testFindBestParametersOnSpliceDataSet_L2R_L2LOSS_SVR() throws Exception { + Problem problem = Train.readProblem(Paths.get("src/test/datasets/splice/splice"), -1); + Parameter param = new Parameter(L2R_L2LOSS_SVR, 1, 0.001, 0.1); + ParameterSearchResult result = Linear.findParameters(problem, param, 5, -1, -1); + assertThat(result.getBestC()).isEqualTo(0.00390625); + assertThat(result.getBestScore()).isEqualTo(0.5699399182191544, Offset.offset(0.0000001)); + assertThat(result.getBestP()).isEqualTo(0.0); + } + + @Test + void testFindBestParametersOnDnaScaleDataSet() throws Exception { + Problem problem = Train.readProblem(Paths.get("src/test/datasets/dna.scale/dna.scale"), -1); + Parameter param = new Parameter(L2R_L2LOSS_SVC, 1, 0.001, 0.1); + ParameterSearchResult result = Linear.findParameters(problem, param, 5, -1, -1); + assertThat(result.getBestC()).isEqualTo(0.015625); + assertThat(result.getBestScore()).isEqualTo(0.9475); + assertThat(result.getBestP()).isEqualTo(-1); + } + + @Test + void testFindBestParametersOnDnaScaleDataSet_L2R_L2LOSS_SVR() throws Exception { + Problem problem = Train.readProblem(Paths.get("src/test/datasets/dna.scale/dna.scale"), -1); + Parameter param = new Parameter(L2R_L2LOSS_SVR, 1, 0.0001, 0.1); + ParameterSearchResult result = Linear.findParameters(problem, param, 5, -1, -1); + assertThat(result.getBestC()).isEqualTo(0.015625); + assertThat(result.getBestScore()).isEqualTo(0.29743037982927906, Offset.offset(0.0000001)); + assertThat(result.getBestP()).isEqualTo(0.15); + } + + @Test + void testGetVersion() throws Exception { + assertThat(Linear.getVersion()).isEqualTo(244); + } + } diff --git a/src/test/java/de/bwaldvogel/liblinear/ModelTest.java b/src/test/java/de/bwaldvogel/liblinear/ModelTest.java new file mode 100644 index 0000000..d9ebd4b --- /dev/null +++ b/src/test/java/de/bwaldvogel/liblinear/ModelTest.java @@ -0,0 +1,41 @@ +package de.bwaldvogel.liblinear; + +import static org.assertj.core.api.Assertions.*; + +import org.junit.jupiter.api.Test; + +import nl.jqno.equalsverifier.EqualsVerifier; +import nl.jqno.equalsverifier.Warning; + + +class ModelTest { + + @Test + void testEqualsAndHashCodeContract() throws Exception { + EqualsVerifier.forClass(Model.class) + .suppress(Warning.NONFINAL_FIELDS) + .verify(); + } + + @Test + void testEqualsAndHashCode_EmptyModel() throws Exception { + Model model1 = new Model(); + Model model2 = new Model(); + + assertThat(model1).hasSameHashCodeAs(model2); + assertThat(model1).isEqualTo(model2); + } + + @Test + void testEqualsAndHashCode_NegativeZeroesInWeightArray() throws Exception { + Model model1 = new Model(); + Model model2 = new Model(); + + model1.w = new double[] {1.0, 2.0, -0.0, -0.00}; + model2.w = new double[] {1.0, 2.0, +0.0, -0.000}; + + assertThat(model1).hasSameHashCodeAs(model2); + assertThat(model1).isEqualTo(model2); + } + +} diff --git a/src/test/java/de/bwaldvogel/liblinear/ParameterTest.java b/src/test/java/de/bwaldvogel/liblinear/ParameterTest.java index 4071369..70f222b 100644 --- a/src/test/java/de/bwaldvogel/liblinear/ParameterTest.java +++ b/src/test/java/de/bwaldvogel/liblinear/ParameterTest.java @@ -1,124 +1,202 @@ package de.bwaldvogel.liblinear; -import static org.fest.assertions.Assertions.assertThat; -import static org.junit.Assert.fail; +import static de.bwaldvogel.liblinear.SolverType.*; +import static org.assertj.core.api.Assertions.*; -import org.junit.Before; -import org.junit.Test; +import java.util.Random; +import org.junit.jupiter.api.BeforeEach; +import org.junit.jupiter.api.Test; -public class ParameterTest { - private Parameter _param; +class ParameterTest { - @Before + private Parameter param; + + @BeforeEach public void setUp() { - _param = new Parameter(SolverType.L2R_L1LOSS_SVC_DUAL, 100, 1e-3); + param = new Parameter(L2R_L1LOSS_SVC_DUAL, 100, 1e-3); } @Test - public void testSetWeights() { + void testDefaults() { + Parameter parameters = new Parameter(L1R_LR, 1, 0.1); + assertThat(parameters.getP()).isEqualTo(0.1); + assertThat(parameters.getMaxIters()).isEqualTo(1000); + } - assertThat(_param.weight).isNull(); - assertThat(_param.getNumWeights()).isEqualTo(0); + @Test + void testSetWeights() { + assertThat(param.weight).isNull(); + assertThat(param.getNumWeights()).isEqualTo(0); double[] weights = new double[] {0, 1, 2, 3, 4, 5}; int[] weightLabels = new int[] {1, 1, 1, 1, 2, 3}; - _param.setWeights(weights, weightLabels); + param.setWeights(weights, weightLabels); - assertThat(_param.getNumWeights()).isEqualTo(6); + assertThat(param.getNumWeights()).isEqualTo(6); // assert parameter uses a copy weights[0]++; - assertThat(_param.getWeights()[0]).isEqualTo(0); + assertThat(param.getWeights()[0]).isEqualTo(0); weightLabels[0]++; - assertThat(_param.getWeightLabels()[0]).isEqualTo(1); - - weights = new double[] {0, 1, 2, 3, 4, 5}; - weightLabels = new int[] {1}; - try { - _param.setWeights(weights, weightLabels); - fail("IllegalArgumentException expected"); - } catch (IllegalArgumentException e) { - assertThat(e.getMessage()).contains("same").contains("length"); - } + assertThat(param.getWeightLabels()[0]).isEqualTo(1); + } + + @Test + void testSetWeights_IllegalArgument() { + double[] weights = new double[] {0, 1, 2, 3, 4, 5}; + int[] weightLabels = new int[] {1}; + + assertThatExceptionOfType(IllegalArgumentException.class) + .isThrownBy(() -> param.setWeights(weights, weightLabels)) + .withMessageContaining("same", "length"); } @Test - public void testGetWeights() { + void testGetWeights() { double[] weights = new double[] {0, 1, 2, 3, 4, 5}; int[] weightLabels = new int[] {1, 1, 1, 1, 2, 3}; - _param.setWeights(weights, weightLabels); + param.setWeights(weights, weightLabels); - assertThat(_param.getWeights()).isEqualTo(weights); - _param.getWeights()[0]++; // shouldn't change the parameter as we should get a copy - assertThat(_param.getWeights()).isEqualTo(weights); + assertThat(param.getWeights()).isEqualTo(weights); + param.getWeights()[0]++; // shouldn't change the parameter as we should get a copy + assertThat(param.getWeights()).isEqualTo(weights); - assertThat(_param.getWeightLabels()).isEqualTo(weightLabels); - _param.getWeightLabels()[0]++; // shouldn't change the parameter as we should get a copy - assertThat(_param.getWeightLabels()[0]).isEqualTo(1); + assertThat(param.getWeightLabels()).isEqualTo(weightLabels); + param.getWeightLabels()[0]++; // shouldn't change the parameter as we should get a copy + assertThat(param.getWeightLabels()[0]).isEqualTo(1); } @Test - public void testSetC() { - _param.setC(0.0001); - assertThat(_param.getC()).isEqualTo(0.0001); - _param.setC(1); - _param.setC(100); - assertThat(_param.getC()).isEqualTo(100); - _param.setC(Double.MAX_VALUE); - - try { - _param.setC(-1); - fail("IllegalArgumentException expected"); - } catch (IllegalArgumentException e) { - assertThat(e.getMessage()).contains("must").contains("not").contains("<= 0"); - } + void testSetC() { + param.setC(0.0001); + assertThat(param.getC()).isEqualTo(0.0001); + param.setC(1); + param.setC(100); + assertThat(param.getC()).isEqualTo(100); + param.setC(Double.MAX_VALUE); + + assertThatExceptionOfType(IllegalArgumentException.class) + .isThrownBy(() -> param.setC(-1)) + .withMessageContainingAll("must", "not", "<= 0"); + + assertThatExceptionOfType(IllegalArgumentException.class) + .isThrownBy(() -> param.setC(0)) + .withMessageContainingAll("must", "not", "<= 0"); + } - try { - _param.setC(0); - fail("IllegalArgumentException expected"); - } catch (IllegalArgumentException e) { - assertThat(e.getMessage()).contains("must").contains("not").contains("<= 0"); - } + @Test + void testSetEps() { + param.setEps(0.0001); + assertThat(param.getEps()).isEqualTo(0.0001); + param.setEps(1); + param.setEps(100); + assertThat(param.getEps()).isEqualTo(100); + param.setEps(Double.MAX_VALUE); + + assertThatExceptionOfType(IllegalArgumentException.class) + .isThrownBy(() -> param.setEps(-1)) + .withMessageContainingAll("must", "not", "<= 0"); + + assertThatExceptionOfType(IllegalArgumentException.class) + .isThrownBy(() -> param.setEps(0)) + .withMessageContainingAll("must", "not", "<= 0"); } @Test - public void testSetEps() { - _param.setEps(0.0001); - assertThat(_param.getEps()).isEqualTo(0.0001); - _param.setEps(1); - _param.setEps(100); - assertThat(_param.getEps()).isEqualTo(100); - _param.setEps(Double.MAX_VALUE); - - try { - _param.setEps(-1); - fail("IllegalArgumentException expected"); - } catch (IllegalArgumentException e) { - assertThat(e.getMessage()).contains("must").contains("not").contains("<= 0"); + void testSetSolverType() { + for (SolverType type : SolverType.values()) { + param.setSolverType(type); + assertThat(param.getSolverType()).isEqualTo(type); } - try { - _param.setEps(0); - fail("IllegalArgumentException expected"); - } catch (IllegalArgumentException e) { - assertThat(e.getMessage()).contains("must").contains("not").contains("<= 0"); - } + assertThatExceptionOfType(IllegalArgumentException.class) + .isThrownBy(() -> param.setSolverType(null)) + .withMessageContainingAll("must", "not", "null"); } @Test - public void testSetSolverType() { - for (SolverType type : SolverType.values()) { - _param.setSolverType(type); - assertThat(_param.getSolverType()).isEqualTo(type); - } - try { - _param.setSolverType(null); - fail("IllegalArgumentException expected"); - } catch (IllegalArgumentException e) { - assertThat(e.getMessage()).contains("must").contains("not").contains("null"); - } + void testSetInitSol() { + assertThat(param.init_sol).isNull(); + + double[] init_sol = new double[] {0, 1, 2, 3, 4, 5}; + param.setInitSol(init_sol); + + // assert parameter uses a copy + init_sol[0]++; + assertThat(param.getInitSol()[0]).isEqualTo(0); + } + + @Test + void testSetP() throws Exception { + assertThatExceptionOfType(IllegalArgumentException.class) + .isThrownBy(() -> param.setP(-1)) + .withMessage("p must not be less than 0"); + } + + @Test + void testSetNu() throws Exception { + assertThatExceptionOfType(IllegalArgumentException.class) + .isThrownBy(() -> param.setNu(-0.1)) + .withMessage("nu must not be <=0"); + + assertThatExceptionOfType(IllegalArgumentException.class) + .isThrownBy(() -> param.setNu(0.0)) + .withMessage("nu must not be <=0"); + + assertThatExceptionOfType(IllegalArgumentException.class) + .isThrownBy(() -> param.setNu(1.0)) + .withMessage("nu must not be >=1"); + + assertThatExceptionOfType(IllegalArgumentException.class) + .isThrownBy(() -> param.setNu(1.5)) + .withMessage("nu must not be >=1"); + } + + @Test + void testGetInitSol() { + assertThat(param.getInitSol()).isNull(); + + double[] init_sol = new double[] {0, 1, 2, 3, 4, 5}; + param.setInitSol(init_sol); + + assertThat(param.getInitSol()).isNotNull(); + assertThat(param.getInitSol()).isEqualTo(init_sol); + param.getInitSol()[0]++; // shouldn't change the parameter as we should get a copy + assertThat(param.getInitSol()).isEqualTo(init_sol); + } + + @Test + void testClone_Simple() throws Exception { + Parameter parameter = new Parameter(L1R_LR, 123.456, 0.123); + Parameter clone = parameter.clone(); + assertThat(clone.getSolverType()).isEqualTo(L1R_LR); + assertThat(clone.getC()).isEqualTo(123.456); + assertThat(clone.getEps()).isEqualTo(0.123); + assertThat(clone.getWeights()).isNull(); + assertThat(clone.getWeightLabels()).isNull(); + assertThat(clone.getNumWeights()).isEqualTo(0); + } + + @Test + void testClone_Full() throws Exception { + Parameter parameter = new Parameter(L1R_LR, 123.456, 0.123, 9000, 1.2); + parameter.setWeights(new double[] {1, 2}, new int[] {3, 4}); + Random random = new Random(123); + parameter.setRandom(random); + Parameter clone = parameter.clone(); + assertThat(clone.getSolverType()).isEqualTo(L1R_LR); + assertThat(clone.getC()).isEqualTo(123.456); + assertThat(clone.getEps()).isEqualTo(0.123); + assertThat(clone.getMaxIters()).isEqualTo(9000); + assertThat(clone.getP()).isEqualTo(1.2); + assertThat(clone.getWeights()).containsExactly(1, 2); + assertThat(clone.getWeightLabels()).containsExactly(3, 4); + assertThat(clone.getNumWeights()).isEqualTo(2); + + assertThat(clone.random).isNotSameAs(random); + assertThat(random.nextInt()).isEqualTo(clone.random.nextInt()); } } diff --git a/src/test/java/de/bwaldvogel/liblinear/PredictTest.java b/src/test/java/de/bwaldvogel/liblinear/PredictTest.java index 034e957..da4d31f 100644 --- a/src/test/java/de/bwaldvogel/liblinear/PredictTest.java +++ b/src/test/java/de/bwaldvogel/liblinear/PredictTest.java @@ -1,54 +1,88 @@ package de.bwaldvogel.liblinear; -import static org.fest.assertions.Assertions.assertThat; -import static org.mockito.Mockito.mock; +import static org.assertj.core.api.Assertions.*; import java.io.BufferedReader; +import java.io.ByteArrayOutputStream; import java.io.PrintStream; import java.io.StringReader; import java.io.StringWriter; import java.io.Writer; +import java.nio.file.Files; +import java.nio.file.Path; +import java.util.List; -import org.junit.Before; -import org.junit.Test; +import org.junit.jupiter.api.AfterEach; +import org.junit.jupiter.api.BeforeEach; +import org.junit.jupiter.api.Test; +import org.junit.jupiter.api.io.TempDir; -public class PredictTest { +class PredictTest { - private Model testModel = LinearTest.createRandomModel(); - private StringBuilder sb = new StringBuilder(); - private Writer writer = new StringWriter(); + private final Model testModel = LinearTest.createRandomModel(); + private final StringBuilder sb = new StringBuilder(); + private final Writer writer = new StringWriter(); - @Before + private final ByteArrayOutputStream byteArrayOutputStream = new ByteArrayOutputStream(); + + @BeforeEach public void setUp() { - System.setOut(mock(PrintStream.class)); // dev/null + Linear.setDebugOutput(new PrintStream(byteArrayOutputStream)); assertThat(testModel.getNrClass()).isGreaterThanOrEqualTo(2); assertThat(testModel.getNrFeature()).isGreaterThanOrEqualTo(10); } - private void testWithLines(StringBuilder sb) throws Exception { - BufferedReader reader = new BufferedReader(new StringReader(sb.toString())); + @AfterEach + public void tearDown() { + Linear.enableDebugOutput(); + } - Predict.doPredict(reader, writer, testModel); + private void testWithLines(StringBuilder sb) throws Exception { + try (StringReader stringReader = new StringReader(sb.toString()); + BufferedReader reader = new BufferedReader(stringReader)) { + Predict.doPredict(reader, writer, testModel, false); + } } - @Test(expected = RuntimeException.class) - public void testDoPredictCorruptLine() throws Exception { + @Test + void testDoPredictCorruptLine() throws Exception { sb.append(testModel.label[0]).append(" abc").append("\n"); - testWithLines(sb); + + assertThatExceptionOfType(RuntimeException.class) + .isThrownBy(() -> testWithLines(sb)) + .withMessage("Wrong input format at line 1"); } - @Test(expected = RuntimeException.class) - public void testDoPredictCorruptLine2() throws Exception { + @Test + void testDoPredictCorruptLine2() throws Exception { sb.append(testModel.label[0]).append(" 1:").append("\n"); - testWithLines(sb); + + assertThatExceptionOfType(RuntimeException.class) + .isThrownBy(() -> testWithLines(sb)) + .withMessage("Can't convert empty string to integer"); } @Test - public void testDoPredict() throws Exception { + void testDoPredict() throws Exception { sb.append(testModel.label[0]).append(" 1:0.32393").append("\n"); sb.append(testModel.label[1]).append(" 2:-71.555 9:88223").append("\n"); testWithLines(sb); assertThat(writer.toString()).isNotEmpty(); } + + @Test + void testTrainAndPredict(@TempDir Path tempDir) throws Exception { + String modelFile = tempDir.resolve("model").toString(); + Train.main(new String[] {"-s", "0", "src/test/datasets/dna.scale/dna.scale", modelFile}); + + Path predictionsFile = tempDir.resolve("predictions"); + Predict.main(new String[] {"-b", "1", "src/test/datasets/dna.scale/dna.scale.t", modelFile, predictionsFile.toString()}); + List predictions = Files.readAllLines(predictionsFile, Linear.FILE_CHARSET); + assertThat(predictions).hasSize(1187); + assertThat(predictions.get(0)).isEqualTo("labels 3 1 2"); + + String loggedString = byteArrayOutputStream.toString(); + assertThat(loggedString).containsPattern("Accuracy = 95[.,]0253% \\(1127/1186\\)"); + } } diff --git a/src/test/java/de/bwaldvogel/liblinear/RegressionTest.java b/src/test/java/de/bwaldvogel/liblinear/RegressionTest.java new file mode 100644 index 0000000..f1e6b30 --- /dev/null +++ b/src/test/java/de/bwaldvogel/liblinear/RegressionTest.java @@ -0,0 +1,378 @@ +package de.bwaldvogel.liblinear; + +import static de.bwaldvogel.liblinear.SolverType.*; +import static org.assertj.core.api.Assertions.*; + +import java.nio.charset.StandardCharsets; +import java.nio.file.Files; +import java.nio.file.Path; +import java.nio.file.Paths; +import java.nio.file.StandardOpenOption; +import java.util.ArrayList; +import java.util.Arrays; +import java.util.Collection; +import java.util.Collections; +import java.util.EnumSet; +import java.util.List; +import java.util.stream.Collectors; +import java.util.stream.Stream; + +import org.assertj.core.data.Offset; +import org.junit.jupiter.api.Test; +import org.junit.jupiter.api.io.TempDir; +import org.junit.jupiter.params.ParameterizedTest; +import org.junit.jupiter.params.provider.MethodSource; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + + +class RegressionTest { + + private static final Logger log = LoggerFactory.getLogger(RegressionTest.class); + + protected static final List SOLVERS = Stream.of(SolverType.values()) + .filter(solver -> !solver.isOneClass()) + .collect(Collectors.toList()); + + private static Collection data() { + List params = new ArrayList<>(); + for (String dataset : new String[] {"splice", "dna.scale"}) { + for (SolverType solverType : SOLVERS) { + for (int bias : new int[] {-1, 1}) { + for (boolean regularizeBias : new boolean[] {false, true}) { + if (!regularizeBias && bias != 1) { + continue; + } + if (!regularizeBias) { + // -R option supported only for solver L2R_LR, L2R_L2LOSS_SVC, L1R_L2LOSS_SVC, L1R_LR, and L2R_L2LOSS_SVR + if (!EnumSet.of(L2R_LR, L2R_L2LOSS_SVC, L1R_L2LOSS_SVC, L1R_LR, L2R_L2LOSS_SVR).contains(solverType)) { + continue; + } + } + params.add(new TestParams(dataset, solverType, bias, regularizeBias, getExpectedAccuracy(dataset, solverType, bias, regularizeBias))); + } + } + } + } + return params; + } + + private static Double getExpectedAccuracy(String dataset, SolverType solverType, int bias, boolean regularizeBias) { + if (solverType.isSupportVectorRegression() || solverType.isOneClass()) { + return null; + } + switch (dataset) { + case "splice": + switch (solverType) { + case L2R_LR: + switch (bias) { + case -1: + return 0.84184; + case 1: + return regularizeBias ? 0.84552 : 0.84414; + default: + throw new IllegalArgumentException("Unexpected bias: " + bias); + } + case L2R_L2LOSS_SVC_DUAL: + switch (bias) { + case -1: + return 0.84368; + case 1: + return 0.85241; + default: + throw new IllegalArgumentException("Unexpected bias: " + bias); + } + case L2R_L2LOSS_SVC: + switch (bias) { + case -1: + return 0.84276; + case 1: + return regularizeBias ? 0.85057 : 0.85149; + default: + throw new IllegalArgumentException("Unexpected bias: " + bias); + } + case L2R_L1LOSS_SVC_DUAL: + switch (bias) { + case -1: + return 0.83494; + case 1: + return 0.83402; + default: + throw new IllegalArgumentException("Unexpected bias: " + bias); + } + case MCSVM_CS: + switch (bias) { + case -1: + return 0.8377; + case 1: + return 0.83862; + default: + throw new IllegalArgumentException("Unexpected bias: " + bias); + } + case L1R_L2LOSS_SVC: + switch (bias) { + case -1: + return 0.8478; + case 1: + return regularizeBias ? 0.8478 : 0.84782; + default: + throw new IllegalArgumentException("Unexpected bias: " + bias); + } + case L1R_LR: + switch (bias) { + case -1: + return 0.8473; + case 1: + return regularizeBias ? 0.84782 : 0.84598; + default: + throw new IllegalArgumentException("Unexpected bias: " + bias); + } + case L2R_LR_DUAL: + switch (bias) { + case -1: + return 0.8423; + case 1: + return 0.8492; + default: + throw new IllegalArgumentException("Unexpected bias: " + bias); + } + default: + throw new IllegalArgumentException("Unexpected solverType: " + solverType); + } + case "dna.scale": + switch (solverType) { + case L2R_LR: + switch (bias) { + case -1: + return 0.94941; + case 1: + return regularizeBias ? 0.950253 : 0.951096; + default: + throw new IllegalArgumentException("Unexpected bias: " + bias); + } + case L2R_L2LOSS_SVC_DUAL: + switch (bias) { + case -1: + case 1: + return 0.9452; + default: + throw new IllegalArgumentException("Unexpected bias: " + bias); + } + case L2R_L2LOSS_SVC: + switch (bias) { + case -1: + return 0.94941; + case 1: + return 0.95194; + default: + throw new IllegalArgumentException("Unexpected bias: " + bias); + } + case L2R_L1LOSS_SVC_DUAL: + switch (bias) { + case -1: + return 0.94688; + case 1: + return 0.94604; + default: + throw new IllegalArgumentException("Unexpected bias: " + bias); + } + case MCSVM_CS: + switch (bias) { + case -1: + return 0.9292; + case 1: + return 0.92749; + default: + throw new IllegalArgumentException("Unexpected bias: " + bias); + } + case L1R_L2LOSS_SVC: + switch (bias) { + case -1: + return 0.9553; + case 1: + return regularizeBias ? 0.956998 : 0.95363; + default: + throw new IllegalArgumentException("Unexpected bias: " + bias); + } + case L1R_LR: + switch (bias) { + case -1: + return 0.9536; + case 1: + return regularizeBias ? 0.95194 : 0.95278; + default: + throw new IllegalArgumentException("Unexpected bias: " + bias); + } + case L2R_LR_DUAL: + switch (bias) { + case -1: + return 0.9486; + case 1: + return 0.94941; + default: + throw new IllegalArgumentException("Unexpected bias: " + bias); + } + default: + throw new IllegalArgumentException("Unexpected solverType: " + solverType); + } + default: + throw new IllegalArgumentException("Unknown expectation: " + dataset + ", " + solverType + ", " + bias); + } + } + + private static class TestParams { + + private final String dataset; + private final SolverType solverType; + private final int bias; + private final boolean regularizeBias; + private final Double expectedAccuracy; + + private TestParams(String dataset, SolverType solverType, int bias, boolean regularizeBias, Double expectedAccuracy) { + this.dataset = dataset; + this.solverType = solverType; + this.bias = bias; + this.regularizeBias = regularizeBias; + this.expectedAccuracy = expectedAccuracy; + } + + @Override + public String toString() { + return "dataset: " + dataset + ", solver: " + solverType + ", bias: " + bias + (!regularizeBias ? " (not regularized)" : ""); + } + } + + @ParameterizedTest + @MethodSource("data") + void regressionTest(TestParams params) throws Exception { + log.info("Running regression test for '{}'", params); + Path trainingFile = Paths.get("src/test/datasets", params.dataset, params.dataset); + Problem problem = Train.readProblem(trainingFile, params.bias); + Parameter parameter = new Parameter(params.solverType, 1, 0.1); + parameter.setRegularizeBias(params.regularizeBias); + Model model = Linear.train(problem, parameter); + Path testFile = Paths.get("src/test/datasets", params.dataset, params.dataset + ".t"); + Problem testProblem = Train.readProblem(testFile, params.bias); + + String filename = "predictions_" + params.solverType.name(); + if (!params.regularizeBias) { + filename += "_notRegularizedBias"; + } else { + filename += "_bias_" + params.bias; + } + Path expectedFile = Paths.get("src/test/resources/regression", params.dataset, filename); + final List expectedPredictions; + if (!Files.exists(expectedFile)) { + expectedPredictions = Collections.emptyList(); + log.warn("Recording predictions to {}", expectedFile); + } else { + expectedPredictions = Files.readAllLines(expectedFile, StandardCharsets.UTF_8); + assertThat(expectedPredictions).hasSize(testProblem.l); + assertThat(testProblem.x.length).isEqualTo(expectedPredictions.size()); + } + + int correctPredictions = 0; + + for (int i = 0; i < testProblem.l; i++) { + Feature[] x = testProblem.x[i]; + double[] predictedValues = new double[model.getNrClass()]; + final double prediction; + if (params.solverType.isLogisticRegressionSolver()) { + prediction = Linear.predictProbability(model, x, predictedValues); + } else { + prediction = Linear.predictValues(model, x, predictedValues); + } + + if (params.expectedAccuracy != null) { + int expectation = (int)testProblem.y[i]; + int actual = (int)prediction; + if (actual == expectation) { + correctPredictions++; + } + } + + if (expectedPredictions.isEmpty()) { + final String line; + if (model.getNrClass() == 2) { + line = predictedValues[0] + "\n"; + } else { + line = Arrays.stream(predictedValues) + .mapToObj(Double::toString) + .collect(Collectors.joining(" ")) + " \n"; + } + Files.createDirectories(expectedFile.getParent()); + Files.write(expectedFile, line.getBytes(StandardCharsets.UTF_8), StandardOpenOption.APPEND, StandardOpenOption.CREATE); + continue; + } + + List expectedValues = parseExpectedValues(expectedPredictions, i); + + Offset allowedOffset = Offset.offset(1e-9); + if (model.getNrClass() == 2) { + assertThat(expectedValues).hasSize(1); + assertThat(predictedValues[0]).isEqualTo(expectedValues.get(0), allowedOffset); + } else { + assertThat(expectedValues).hasSameSizeAs(predictedValues); + for (int n = 0; n < predictedValues.length; n++) { + assertThat(predictedValues[n]).isEqualTo(expectedValues.get(n), allowedOffset); + } + } + } + + if (params.expectedAccuracy != null) { + double accuracy = correctPredictions / (double)testProblem.l; + assertThat(accuracy).isEqualTo(params.expectedAccuracy.doubleValue(), Offset.offset(1e-4)); + } + } + + @Test + void testOneClass(@TempDir Path tempDir) throws Exception { + Path trainingFile = Paths.get("src/test/datasets/splice/splice"); + + Path spliceClass1 = tempDir.resolve("splice-class-1"); + Path spliceClass2 = tempDir.resolve("splice-class-2"); + + for (String line : Files.readAllLines(trainingFile, StandardCharsets.ISO_8859_1)) { + final Path targetFile; + if (line.startsWith("+1")) { + targetFile = spliceClass1; + } else { + targetFile = spliceClass2; + } + Files.write(targetFile, Arrays.asList(line), StandardCharsets.UTF_8, StandardOpenOption.APPEND, StandardOpenOption.CREATE); + } + + Problem problem1 = Train.readProblem(spliceClass1, StandardCharsets.UTF_8, -1); + Parameter param = new Parameter(ONECLASS_SVM, 1, 0.01); + param.setNu(0.1); + Model model = Linear.train(problem1, param); + + Model expectedModel = Model.load(Paths.get("src/test/resources/regression/splice/one_class_model")); + assertThat(expectedModel).isEqualTo(model); + + Problem problem2 = Train.readProblem(spliceClass2, StandardCharsets.UTF_8, -1); + + // expected values determined with C-version of liblinear (v2.41) + assertThat(calculatePredictionAccuracy(model, problem1)).isEqualTo(0.897485, Offset.strictOffset(1e-6)); + assertThat(calculatePredictionAccuracy(model, problem2)).isEqualTo(0.0703934, Offset.strictOffset(1e-6)); + } + + private static double calculatePredictionAccuracy(Model model, Problem problem) { + int correct = 0; + for (Feature[] x : problem.x) { + double prediction = Linear.predict(model, x); + if (prediction == problem.y[0]) { + correct++; + } + } + return (double)correct / problem.l; + } + + private List parseExpectedValues(List expectedPredictions, int i) { + return Stream.of(expectedPredictions.get(i) + .split(" ")) + .map(Double::parseDouble) + .collect(Collectors.toList()); + } + +} diff --git a/src/test/java/de/bwaldvogel/liblinear/SolverTypeTest.java b/src/test/java/de/bwaldvogel/liblinear/SolverTypeTest.java new file mode 100644 index 0000000..d941aee --- /dev/null +++ b/src/test/java/de/bwaldvogel/liblinear/SolverTypeTest.java @@ -0,0 +1,23 @@ +package de.bwaldvogel.liblinear; + +import static de.bwaldvogel.liblinear.SolverType.*; +import static org.assertj.core.api.Assertions.*; + +import org.junit.jupiter.api.Test; + + +class SolverTypeTest { + + @Test + void testIsSupportVectorRegression() throws Exception { + for (SolverType type : SolverType.values()) { + boolean regressionSolver = type.isSupportVectorRegression(); + // from check_regression_model() in linear.cpp + if (type == L2R_L2LOSS_SVR || type == L2R_L1LOSS_SVR_DUAL || type == L2R_L2LOSS_SVR_DUAL) { + assertThat(regressionSolver).withFailMessage(type + " is a regression solver").isTrue(); + } else { + assertThat(regressionSolver).withFailMessage(type + " is not a regression solver").isFalse(); + } + } + } +} diff --git a/src/test/java/de/bwaldvogel/liblinear/SparseOperatorTest.java b/src/test/java/de/bwaldvogel/liblinear/SparseOperatorTest.java new file mode 100644 index 0000000..ef3b59c --- /dev/null +++ b/src/test/java/de/bwaldvogel/liblinear/SparseOperatorTest.java @@ -0,0 +1,68 @@ +package de.bwaldvogel.liblinear; + +import static org.assertj.core.api.Assertions.*; + +import org.junit.jupiter.api.Test; + + +class SparseOperatorTest { + + @Test + void testNrm2Sq() throws Exception { + assertThat(SparseOperator.nrm2_sq(features())).isZero(); + assertThat(SparseOperator.nrm2_sq(features(1.0))).isEqualTo(1.0); + assertThat(SparseOperator.nrm2_sq(features(1.0, 2.0))).isEqualTo(1 + 4); + assertThat(SparseOperator.nrm2_sq(features(1.0, 2.0, 3.0))).isEqualTo(5 + 9); + } + + @Test + void testDot() throws Exception { + assertThat(SparseOperator.dot(new double[0], features())).isEqualTo(0.0); + assertThat(SparseOperator.dot(new double[] { 1.0 }, features(1.0))).isEqualTo(1.0); + assertThat(SparseOperator.dot(new double[] { 1.0, 2.0 }, features(1.0, 2.0))).isEqualTo(1 + 2 * 2); + assertThat(SparseOperator.dot(new double[] { 3.0, 2.0, 1.0 }, features(1.0, 2.0, 3.0))).isEqualTo(3 * 1 + 2 * 2 + 1 * 3); + } + + @Test + public void testSparseDot() throws Exception { + Feature[] features1 = new FeatureNode[] { + new FeatureNode(1, 2.0), + new FeatureNode(2, 3.0), + new FeatureNode(3, 0.5), + new FeatureNode(5, 2.0) + }; + + Feature[] features2 = new FeatureNode[] { + new FeatureNode(1, 2.0), + new FeatureNode(3, 1.0), + new FeatureNode(4, 1.0) + }; + + assertThat(SparseOperator.sparse_dot(features1, features2)).isEqualTo(2.0 * 2.0 + 0.5 * 1.0); + } + + @Test + void testAxpy() throws Exception { + assertThat(axpy(1.0, features())).isEmpty(); + assertThat(axpy(1.5, features(2.0))).containsExactly(1.5 * 2.0); + + double[] y = new double[] { 1.0, 2.0 }; + SparseOperator.axpy(1.5, features(2.0, 3.0), y); + assertThat(y).containsExactly(1.0 + 1.5 * 2.0, 2.0 + 1.5 * 3.0); + } + + private static double[] axpy(double a, Feature[] features) { + double[] result = new double[features.length]; + SparseOperator.axpy(a, features, result); + return result; + } + + private static Feature[] features(double... values) { + Feature[] features = new Feature[values.length]; + for (int i = 0; i < values.length; i++) { + features[i] = new FeatureNode(i + 1, values[i]); + } + return features; + } + +} diff --git a/src/test/java/de/bwaldvogel/liblinear/TestUtils.java b/src/test/java/de/bwaldvogel/liblinear/TestUtils.java new file mode 100644 index 0000000..e4c2a15 --- /dev/null +++ b/src/test/java/de/bwaldvogel/liblinear/TestUtils.java @@ -0,0 +1,32 @@ +package de.bwaldvogel.liblinear; + +import java.io.BufferedWriter; +import java.io.IOException; +import java.nio.charset.StandardCharsets; +import java.nio.file.Files; +import java.nio.file.Path; +import java.util.List; + + +final class TestUtils { + + private TestUtils() { + } + + static void writeToFile(Path file, List lines) throws IOException { + try (BufferedWriter bufferedWriter = Files.newBufferedWriter(file, StandardCharsets.UTF_8)) { + for (String line : lines) { + bufferedWriter.append(line).append("\n"); + } + bufferedWriter.flush(); + } + } + + static String repeat(String stringToRepeat, int numTimes) { + StringBuilder longString = new StringBuilder(); + for (int i = 0; i < numTimes; i++) { + longString.append(stringToRepeat); + } + return longString.toString(); + } +} diff --git a/src/test/java/de/bwaldvogel/liblinear/TrainTest.java b/src/test/java/de/bwaldvogel/liblinear/TrainTest.java index d064b74..5157daa 100644 --- a/src/test/java/de/bwaldvogel/liblinear/TrainTest.java +++ b/src/test/java/de/bwaldvogel/liblinear/TrainTest.java @@ -1,24 +1,62 @@ package de.bwaldvogel.liblinear; -import static org.fest.assertions.Assertions.assertThat; +import static de.bwaldvogel.liblinear.SolverType.*; +import static de.bwaldvogel.liblinear.TestUtils.*; +import static org.assertj.core.api.Assertions.*; -import java.io.BufferedWriter; -import java.io.File; -import java.io.FileWriter; -import java.util.ArrayList; -import java.util.Collection; +import java.io.ByteArrayInputStream; +import java.nio.charset.Charset; +import java.nio.charset.StandardCharsets; +import java.nio.file.Path; +import java.util.Arrays; +import java.util.Collections; +import java.util.List; -import org.junit.Test; +import org.junit.jupiter.api.BeforeEach; +import org.junit.jupiter.api.Test; +import org.junit.jupiter.api.io.TempDir; -public class TrainTest { +class TrainTest { + + @BeforeEach + public void reset() throws Exception { + Linear.disableDebugOutput(); + } + + @Test + void testDoCrossValidationOnIrisDataSet() throws Exception { + for (SolverType solver : SolverType.values()) { + if (solver.isOneClass()) { + continue; + } + Train.main(new String[] {"-v", "5", "-s", "" + solver.getId(), "src/test/resources/iris.scale"}); + } + } + + @Test + void testFindBestCOnIrisDataSet() throws Exception { + Train.main(new String[] {"-C", "src/test/resources/iris.scale"}); + } @Test - public void testParseCommandLine() { + void testFindBestCOnIrisDataSet_L2R_L2LOSS_SVR_DUAL() throws Exception { + Train.main(new String[] {"-s", "11", "-C", "src/test/resources/iris.scale"}); + } + + @Test + void testFindBestCOnSpliceDataSet_L2R_L2LOSS_SVR_DUAL() throws Exception { + Train.main(new String[] {"-s", "11", "-C", "src/test/datasets/splice/splice"}); + } + + @Test + void testParseCommandLine() { Train train = new Train(); for (SolverType solver : SolverType.values()) { train.parse_command_line(new String[] {"-B", "5.3", "-s", "" + solver.getId(), "-p", "0.01", "model-filename"}); + assertThat(train.isFindParameters()).isFalse(); + assertThat(train.getNumFolds()).isEqualTo(0); Parameter param = train.getParameter(); assertThat(param.solverType).isEqualTo(solver); // check default eps @@ -28,7 +66,9 @@ public void testParseCommandLine() { } else if (solver.getId() == 7) { assertThat(param.eps).isEqualTo(0.1); } else if (solver.getId() == 11) { - assertThat(param.eps).isEqualTo(0.001); + assertThat(param.eps).isEqualTo(0.0001);} + else if (solver.getId() == 21) { + assertThat(param.eps).isEqualTo(0.01); } else { assertThat(param.eps).isEqualTo(0.1); } @@ -38,9 +78,36 @@ public void testParseCommandLine() { } } + @Test + void testParseCommandLine_FindC_NoSolverSpecified() { + Train train = new Train(); + + train.parse_command_line(new String[] {"-C", "model-filename"}); + assertThat(train.isFindParameters()).isTrue(); + assertThat(train.getNumFolds()).isEqualTo(5); + Parameter param = train.getParameter(); + assertThat(param.solverType).isEqualTo(L2R_L2LOSS_SVC); + // check default eps + assertThat(param.eps).isEqualTo(0.01); + assertThat(param.p).isEqualTo(0.1); + } + + @Test + void testParseCommandLine_FindC_SolverAndNumFoldsSpecified() { + Train train = new Train(); + + train.parse_command_line(new String[] {"-s", "0", "-v", "10", "-C", "model-filename"}); + assertThat(train.isFindParameters()).isTrue(); + assertThat(train.getNumFolds()).isEqualTo(10); + Parameter param = train.getParameter(); + assertThat(param.solverType).isEqualTo(L2R_LR); + assertThat(param.eps).isEqualTo(0.01); + assertThat(param.p).isEqualTo(0.1); + } + @Test // https://github.com/bwaldvogel/liblinear-java/issues/4 - public void testParseWeights() throws Exception { + void testParseWeights() throws Exception { Train train = new Train(); train.parse_command_line(new String[] {"-v", "10", "-c", "10", "-w1", "1.234", "model-filename"}); Parameter parameter = train.getParameter(); @@ -54,28 +121,32 @@ public void testParseWeights() throws Exception { } @Test - public void testReadProblem() throws Exception { + void testParseCommandLine_regularizeBias() throws Exception { + Train train = new Train(); + train.parse_command_line(new String[] {"-R", "model-filename"}); + Parameter parameter = train.getParameter(); + assertThat(parameter.regularize_bias).isFalse(); - File file = File.createTempFile("svm", "test"); - file.deleteOnExit(); + train.parse_command_line(new String[] {"model-filename"}); + parameter = train.getParameter(); + assertThat(parameter.regularize_bias).isTrue(); + } - Collection lines = new ArrayList(); - lines.add("1 1:1 3:1 4:1 6:1"); - lines.add("2 2:1 3:1 5:1 7:1"); - lines.add("1 3:1 5:1"); - lines.add("1 1:1 4:1 7:1"); - lines.add("2 4:1 5:1 7:1"); - BufferedWriter writer = new BufferedWriter(new FileWriter(file)); - try { - for (String line : lines) - writer.append(line).append("\n"); - } - finally { - writer.close(); - } + @Test + void testReadProblem(@TempDir Path tempDir) throws Exception { + Path problemPath = tempDir.resolve("problem"); + + List lines = Arrays.asList( + "1 1:1 3:1 4:1 6:1", + "2 2:1 3:1 5:1 7:1", + "1 3:1 5:1", + "1 1:1 4:1 7:1", + "2 4:1 5:1 7:1"); + + writeToFile(problemPath, lines); Train train = new Train(); - train.readProblem(file.getAbsolutePath()); + train.readProblem(problemPath); Problem prob = train.getProblem(); assertThat(prob.bias).isEqualTo(1); @@ -83,43 +154,43 @@ public void testReadProblem() throws Exception { assertThat(prob.y).isEqualTo(new double[] {1, 2, 1, 1, 2}); assertThat(prob.n).isEqualTo(8); assertThat(prob.l).isEqualTo(prob.y.length); - assertThat(prob.x).hasSize(prob.y.length); + assertThat(prob.x.length).isEqualTo(prob.y.length); - for (Feature[] nodes : prob.x) { + validate(prob); + } - assertThat(nodes.length).isLessThanOrEqualTo(prob.n); - for (Feature node : nodes) { - // bias term - if (prob.bias >= 0 && nodes[nodes.length - 1] == node) { - assertThat(node.getIndex()).isEqualTo(prob.n); - assertThat(node.getValue()).isEqualTo(prob.bias); - } else { - assertThat(node.getIndex()).isLessThan(prob.n); - } - } - } + @Test + void testReadProblemFromStream() throws Exception { + String data = "1 1:1 3:1 4:1 6:1\n" + + "2 2:1 3:1 5:1 7:1\n" + + "1 3:1 5:1\n" + + "1 1:1 4:1 7:1\n" + + "2 4:1 5:1 7:1\n"; + + Charset charset = StandardCharsets.UTF_8; + Problem prob = Train.readProblem(new ByteArrayInputStream(data.getBytes(charset)), charset, 1); + assertThat(prob.bias).isEqualTo(1); + assertThat(prob.y).hasSize(5); + assertThat(prob.y).isEqualTo(new double[] {1, 2, 1, 1, 2}); + assertThat(prob.n).isEqualTo(8); + assertThat(prob.l).isEqualTo(prob.y.length); + assertThat(prob.x.length).isEqualTo(prob.y.length); + + validate(prob); } /** * unit-test for Issue #1 (http://github.com/bwaldvogel/liblinear-java/issues#issue/1) */ @Test - public void testReadProblemEmptyLine() throws Exception { + void testReadProblemEmptyLine(@TempDir Path tempDir) throws Exception { + Path file = tempDir.resolve("problem"); - File file = File.createTempFile("svm", "test"); - file.deleteOnExit(); + List lines = Arrays.asList( + "1 1:1 3:1 4:1 6:1", + "2 "); - Collection lines = new ArrayList(); - lines.add("1 1:1 3:1 4:1 6:1"); - lines.add("2 "); - BufferedWriter writer = new BufferedWriter(new FileWriter(file)); - try { - for (String line : lines) - writer.append(line).append("\n"); - } - finally { - writer.close(); - } + writeToFile(file, lines); Problem prob = Train.readProblem(file, -1.0); assertThat(prob.bias).isEqualTo(-1); @@ -127,86 +198,93 @@ public void testReadProblemEmptyLine() throws Exception { assertThat(prob.y).isEqualTo(new double[] {1, 2}); assertThat(prob.n).isEqualTo(6); assertThat(prob.l).isEqualTo(prob.y.length); - assertThat(prob.x).hasSize(prob.y.length); + assertThat(prob.x.length).isEqualTo(prob.y.length); assertThat(prob.x[0]).hasSize(4); assertThat(prob.x[1]).hasSize(0); } - @Test(expected = InvalidInputDataException.class) - public void testReadUnsortedProblem() throws Exception { - File file = File.createTempFile("svm", "test"); - file.deleteOnExit(); + @Test + void testReadUnsortedProblem(@TempDir Path tempDir) throws Exception { + Path file = tempDir.resolve("problem"); - Collection lines = new ArrayList(); - lines.add("1 1:1 3:1 4:1 6:1"); - lines.add("2 2:1 3:1 5:1 7:1"); - lines.add("1 3:1 5:1 4:1"); // here's the mistake: not correctly sorted + List lines = Arrays.asList( + "1 1:1 3:1 4:1 6:1", + "2 2:1 3:1 5:1 7:1", + "1 3:1 5:1 4:1"); // here's the mistake: not correctly sorted - BufferedWriter writer = new BufferedWriter(new FileWriter(file)); - try { - for (String line : lines) - writer.append(line).append("\n"); - } - finally { - writer.close(); - } + writeToFile(file, lines); Train train = new Train(); - train.readProblem(file.getAbsolutePath()); + + assertThatExceptionOfType(InvalidInputDataException.class) + .isThrownBy(() -> train.readProblem(file)) + .withMessage("indices must be sorted in ascending order"); } + @Test + void testReadProblemWithInvalidIndex(@TempDir Path tempDir) throws Exception { + Path file = tempDir.resolve("problem"); - @Test(expected = InvalidInputDataException.class) - public void testReadProblemWithInvalidIndex() throws Exception { - File file = File.createTempFile("svm", "test"); - file.deleteOnExit(); + List lines = Arrays.asList( + "1 1:1 3:1 4:1 6:1", + "2 2:1 3:1 5:1 -4:1"); - Collection lines = new ArrayList(); - lines.add("1 1:1 3:1 4:1 6:1"); - lines.add("2 2:1 3:1 5:1 -4:1"); + writeToFile(file, lines); - BufferedWriter writer = new BufferedWriter(new FileWriter(file)); - try { - for (String line : lines) - writer.append(line).append("\n"); - } - finally { - writer.close(); - } + Train train = new Train(); + + assertThatExceptionOfType(InvalidInputDataException.class) + .isThrownBy(() -> train.readProblem(file)) + .withMessage("invalid index: -4"); + } + + @Test + void testReadProblemWithZeroIndex(@TempDir Path tempDir) throws Exception { + Path file = tempDir.resolve("problem"); + + List lines = Collections.singletonList("1 0:1 1:1"); + + writeToFile(file, lines); Train train = new Train(); - try { - train.readProblem(file.getAbsolutePath()); - } catch (InvalidInputDataException e) { - throw e; - } + + assertThatExceptionOfType(InvalidInputDataException.class) + .isThrownBy(() -> train.readProblem(file)) + .withMessage("invalid index: 0"); } - @Test(expected = InvalidInputDataException.class) - public void testReadWrongProblem() throws Exception { - File file = File.createTempFile("svm", "test"); - file.deleteOnExit(); + @Test + void testReadWrongProblem(@TempDir Path tempDir) throws Exception { + Path file = tempDir.resolve("problem"); - Collection lines = new ArrayList(); - lines.add("1 1:1 3:1 4:1 6:1"); - lines.add("2 2:1 3:1 5:1 7:1"); - lines.add("1 3:1 5:a"); // here's the mistake: incomplete line + List lines = Arrays.asList( + "1 1:1 3:1 4:1 6:1", + "2 2:1 3:1 5:1 7:1", + "1 3:1 5:a"); // here's the mistake: incomplete line - BufferedWriter writer = new BufferedWriter(new FileWriter(file)); - try { - for (String line : lines) - writer.append(line).append("\n"); - } - finally { - writer.close(); - } + writeToFile(file, lines); Train train = new Train(); - try { - train.readProblem(file.getAbsolutePath()); - } catch (InvalidInputDataException e) { - throw e; + + assertThatExceptionOfType(InvalidInputDataException.class) + .isThrownBy(() -> train.readProblem(file)) + .withMessage("invalid value: a"); + } + + private void validate(Problem prob) { + for (Feature[] nodes : prob.x) { + assertThat(nodes.length).isLessThanOrEqualTo(prob.n); + for (Feature node : nodes) { + // bias term + if (prob.bias >= 0 && nodes[nodes.length - 1] == node) { + assertThat(node.getIndex()).isEqualTo(prob.n); + assertThat(node.getValue()).isEqualTo(prob.bias); + } else { + assertThat(node.getIndex()).isLessThan(prob.n); + } + } } } + } diff --git a/src/test/resources/iris.scale b/src/test/resources/iris.scale new file mode 100644 index 0000000..bfd8450 --- /dev/null +++ b/src/test/resources/iris.scale @@ -0,0 +1,150 @@ +1 1:-0.555556 2:0.25 3:-0.864407 4:-0.916667 +1 1:-0.666667 2:-0.166667 3:-0.864407 4:-0.916667 +1 1:-0.777778 3:-0.898305 4:-0.916667 +1 1:-0.833333 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b/src/test/resources/logback-test.xml @@ -0,0 +1,15 @@ + + + + + %d{HH:mm:ss.SSS} %-5level [%-7.7thread] [%-25.25logger{25}] - %msg%n + + + + + + + + + + \ No newline at end of file diff --git a/src/test/resources/regression/dna.scale/predictions_L1R_L2LOSS_SVC_bias_-1 b/src/test/resources/regression/dna.scale/predictions_L1R_L2LOSS_SVC_bias_-1 new file mode 100644 index 0000000..fae73c0 --- /dev/null +++ b/src/test/resources/regression/dna.scale/predictions_L1R_L2LOSS_SVC_bias_-1 @@ -0,0 +1,1186 @@ +3.081038970230975 -3.171042573144427 -5.7871684226903755 +-1.0266496527324533 -4.0779004308443625 1.3263339664003653 +0.06815097340784182 -3.390934849732564 1.6440761451039685 +1.5965841639962133 -2.8932593050962745 -5.177518756517459 +2.610620798382577 -2.903713895824613 -4.696897654153662 +-2.0585743125057583 1.3108353372146426 -2.089423647363394 +2.231793836386722 -5.122857613182071 -2.7004014986956837 +-2.0791896353376584 -0.8713546122872626 1.547791904566925 +0.7806869234570326 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