From a3adb93589ca611ec49b5caa80f5c1eb8cb6a006 Mon Sep 17 00:00:00 2001 From: LiuYuhui Date: Mon, 15 Jun 2020 15:40:48 +0800 Subject: [PATCH 1/9] Add NonParametricMachine class (#5055) * add nonparametric machine * fix notebooks --- .../classification/Classification.ipynb | 23 +++++++--- doc/ipython-notebooks/multiclass/KNN.ipynb | 9 ++-- examples/meta/src/evaluation/clustering.sg.in | 4 +- .../src/multiclass/k_nearest_neighbours.sg.in | 4 +- .../large_margin_nearest_neighbours.sg.in | 4 +- .../python/evaluation_clustering_simple.py | 4 +- src/interfaces/swig/Classifier.i | 3 ++ src/interfaces/swig/Clustering.i | 2 + src/interfaces/swig/Machine.i | 1 + src/shogun/clustering/GMM.cpp | 4 +- src/shogun/clustering/KMeans.cpp | 1 + src/shogun/machine/DistanceMachine.cpp | 21 +++------ src/shogun/machine/DistanceMachine.h | 4 +- src/shogun/machine/Machine.cpp | 5 +++ src/shogun/machine/Machine.h | 9 ++++ src/shogun/machine/NonParametricMachine.h | 44 +++++++++++++++++++ src/shogun/metric/LMNNImpl.cpp | 3 +- src/shogun/multiclass/KNN.cpp | 37 ++++------------ src/shogun/multiclass/KNN.h | 2 +- tests/unit/multiclass/KNN_unittest.cc | 24 +++++----- 20 files changed, 129 insertions(+), 79 deletions(-) create mode 100644 src/shogun/machine/NonParametricMachine.h diff --git a/doc/ipython-notebooks/classification/Classification.ipynb b/doc/ipython-notebooks/classification/Classification.ipynb index 06c36aae98c..2e3bc138d8b 100644 --- a/doc/ipython-notebooks/classification/Classification.ipynb +++ b/doc/ipython-notebooks/classification/Classification.ipynb @@ -666,9 +666,22 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "figure = plt.figure(figsize=(30,9))\n", "plt.subplot(2,11,1)\n", @@ -682,8 +695,8 @@ "plot_binary_data(plt,feats_non_linear, labels_non_linear)\n", "\n", "for i in range(0,10):\n", - " plt.subplot(2,11,13+i)\n", - " plot_model(plt,classifiers_non_linear[i],feats_non_linear,labels_non_linear,fading=fadings[i])" + " plt.subplot(2,11,13+i)\n", + " plot_model(plt,classifiers_non_linear[i],feats_non_linear,labels_non_linear,fading=fadings[i])" ] }, { diff --git a/doc/ipython-notebooks/multiclass/KNN.ipynb b/doc/ipython-notebooks/multiclass/KNN.ipynb index 063a2569883..ade4a8fc10d 100644 --- a/doc/ipython-notebooks/multiclass/KNN.ipynb +++ b/doc/ipython-notebooks/multiclass/KNN.ipynb @@ -286,8 +286,7 @@ " labels.add_subset(idx_train)\n", "\n", " dist = sg.create_distance('EuclideanDistance')\n", - " dist.init(feats, feats)\n", - " knn = sg.create_machine(\"KNN\", k=k, distance=dist, labels=labels)\n", + " knn = sg.create_machine(\"KNN\", k=k, distance=dist)\n", " #knn.set_store_model_features(True)\n", " #FIXME: causes SEGFAULT\n", " if use_cover_tree:\n", @@ -295,10 +294,10 @@ " # knn.put('knn_solver', \"KNN_COVER_TREE\")\n", " else:\n", " knn.put('knn_solver', \"KNN_BRUTE\")\n", - " knn.train()\n", + " knn.train(feats, labels)\n", "\n", " evaluator = sg.create_evaluation(\"MulticlassAccuracy\")\n", - " pred = knn.apply()\n", + " pred = knn.apply(feats)\n", " acc_train[i, j] = evaluator.evaluate(pred, labels)\n", "\n", " feats.remove_subset()\n", @@ -490,7 +489,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/examples/meta/src/evaluation/clustering.sg.in b/examples/meta/src/evaluation/clustering.sg.in index a040d69bde7..5160da9fd49 100644 --- a/examples/meta/src/evaluation/clustering.sg.in +++ b/examples/meta/src/evaluation/clustering.sg.in @@ -18,8 +18,8 @@ RealMatrix centers = kmeans.get_real_matrix("cluster_centers") Labels labels_centroids = create_labels(f_labels_centroids) Features fea_centroids = create_features(centers) Distance d2 = create_distance("EuclideanDistance", lhs=fea_centroids, rhs=fea_centroids) -Machine knn = create_machine("KNN", k=1, distance=d2, labels=labels_centroids) -knn.train() +Machine knn = create_machine("KNN", k=1, distance=d2) +knn.train(fea_centroids, labels_centroids) Labels gnd_hat = knn.apply(features_train) #![assign_labels] diff --git a/examples/meta/src/multiclass/k_nearest_neighbours.sg.in b/examples/meta/src/multiclass/k_nearest_neighbours.sg.in index 390f044b78f..332866a9e83 100644 --- a/examples/meta/src/multiclass/k_nearest_neighbours.sg.in +++ b/examples/meta/src/multiclass/k_nearest_neighbours.sg.in @@ -11,7 +11,7 @@ Labels labels_test = create_labels(f_labels_test) #![create_features] #![choose_distance] -Distance d = create_distance("EuclideanDistance", lhs=features_train, rhs=features_train) +Distance d = create_distance("EuclideanDistance") #![choose_distance] #![create_instance] @@ -20,7 +20,7 @@ Machine knn = create_machine("KNN", k=k, distance=d, labels=labels_train) #![create_instance] #![train_and_apply] -knn.train() +knn.train(features_train, labels_train) MulticlassLabels labels_predict = knn.apply_multiclass(features_test) #![train_and_apply] diff --git a/examples/meta/src/multiclass/large_margin_nearest_neighbours.sg.in b/examples/meta/src/multiclass/large_margin_nearest_neighbours.sg.in index 5f9cd3b672c..5b254385b75 100644 --- a/examples/meta/src/multiclass/large_margin_nearest_neighbours.sg.in +++ b/examples/meta/src/multiclass/large_margin_nearest_neighbours.sg.in @@ -21,8 +21,8 @@ Distance lmnn_distance = lmnn.get_distance() #![train_metric] #![train_and_apply] -Machine knn = create_machine("KNN", k=k, distance=lmnn_distance, labels=labels_train) -knn.train() +Machine knn = create_machine("KNN", k=k, distance=lmnn_distance) +knn.train(features_train, labels_train) MulticlassLabels labels_predict = knn.apply_multiclass(features_test) #![train_and_apply] diff --git a/examples/undocumented/python/evaluation_clustering_simple.py b/examples/undocumented/python/evaluation_clustering_simple.py index b0ddfcb03a2..64c6cccc9d9 100644 --- a/examples/undocumented/python/evaluation_clustering_simple.py +++ b/examples/undocumented/python/evaluation_clustering_simple.py @@ -25,8 +25,8 @@ def assign_labels(data, centroids, ncenters): fea_centroids = sg.create_features(centroids) distance = sg.create_distance('EuclideanDistance') distance.init(fea_centroids, fea_centroids) - knn = sg.create_machine("KNN", k=1, distance=distance, labels=labels) - knn.train() + knn = sg.create_machine("KNN", k=1, distance=distance) + knn.train(fea_centroids, labels) return knn.apply(data) def evaluation_clustering_simple (n_data=100, sqrt_num_blobs=4, distance=5): diff --git a/src/interfaces/swig/Classifier.i b/src/interfaces/swig/Classifier.i index 9da45046125..48002eae9c6 100644 --- a/src/interfaces/swig/Classifier.i +++ b/src/interfaces/swig/Classifier.i @@ -30,6 +30,9 @@ /* Include Class Headers to make them visible from within the target language */ %include +%include +%include +%include %include %include %include diff --git a/src/interfaces/swig/Clustering.i b/src/interfaces/swig/Clustering.i index 8029f2d1beb..42634009a20 100644 --- a/src/interfaces/swig/Clustering.i +++ b/src/interfaces/swig/Clustering.i @@ -17,6 +17,8 @@ SHARED_RANDOM_INTERFACE(shogun::DistanceMachine) %shared_ptr(shogun::GMM) /* Include Class Headers to make them visible from within the target language */ +%include +%include %include RANDOM_INTERFACE(DistanceMachine) %include diff --git a/src/interfaces/swig/Machine.i b/src/interfaces/swig/Machine.i index c676dcd49a8..5aeb1546984 100644 --- a/src/interfaces/swig/Machine.i +++ b/src/interfaces/swig/Machine.i @@ -9,3 +9,4 @@ SHARED_RANDOM_INTERFACE(shogun::Machine) %shared_ptr(shogun::LinearMachine) %shared_ptr(shogun::DistanceMachine) %shared_ptr(shogun::IterativeMachine) +%shared_ptr(shogun::NonParametricMachine) diff --git a/src/shogun/clustering/GMM.cpp b/src/shogun/clustering/GMM.cpp index af2eddc78a4..66c96ae2c57 100644 --- a/src/shogun/clustering/GMM.cpp +++ b/src/shogun/clustering/GMM.cpp @@ -774,8 +774,8 @@ SGMatrix GMM::alpha_init(SGMatrix init_means) SGVector label_num(init_means.num_cols); linalg::range_fill(label_num); - auto knn=std::make_shared(1, std::make_shared(), std::make_shared(label_num)); - knn->train(std::make_shared>(init_means)); + auto knn=std::make_shared(1, std::make_shared()); + knn->train(std::make_shared>(init_means), std::make_shared(label_num)); auto init_labels = knn->apply(features)->as(); SGMatrix alpha(num_vectors, index_t(m_components.size())); diff --git a/src/shogun/clustering/KMeans.cpp b/src/shogun/clustering/KMeans.cpp index f3922077b29..01f06fb4a06 100644 --- a/src/shogun/clustering/KMeans.cpp +++ b/src/shogun/clustering/KMeans.cpp @@ -181,6 +181,7 @@ void KMeans::Lloyd_KMeans(SGMatrix centers, int32_t num_centers) bool KMeans::train_machine(std::shared_ptr data) { + m_features = data; initialize_training(data); Lloyd_KMeans(cluster_centers, k); compute_cluster_variances(); diff --git a/src/shogun/machine/DistanceMachine.cpp b/src/shogun/machine/DistanceMachine.cpp index 75310c62149..4c571e6c9a1 100644 --- a/src/shogun/machine/DistanceMachine.cpp +++ b/src/shogun/machine/DistanceMachine.cpp @@ -17,7 +17,7 @@ using namespace shogun; DistanceMachine::DistanceMachine() -: Machine() +: NonParametricMachine() { init(); } @@ -99,6 +99,7 @@ void DistanceMachine::distances_rhs(SGVector& result, index_t idx_b1, std::shared_ptr DistanceMachine::apply_multiclass(std::shared_ptr data) { + if (data) { /* set distance features to given ones and apply to all */ @@ -118,30 +119,20 @@ std::shared_ptr DistanceMachine::apply_multiclass(std::shared_ return apply_multiclass(all); } return NULL; + } float64_t DistanceMachine::apply_one(int32_t num) { /* number of clusters */ - auto lhs=distance->get_lhs(); + const auto& lhs=distance->get_lhs(); int32_t num_clusters=lhs->get_num_vectors(); /* (multiple threads) calculate distances to all cluster centers */ SGVector dists(num_clusters); distances_lhs(dists, 0, num_clusters-1, num); - - /* find cluster index with smallest distance */ - float64_t result=dists.vector[0]; - index_t best_index=0; - for (index_t i=1; i #include - +#include namespace shogun { @@ -24,7 +24,7 @@ namespace shogun * * A distance machine is based on a a-priori choosen distance. */ -class DistanceMachine : public Machine +class DistanceMachine : public NonParametricMachine { public: /** default constructor */ diff --git a/src/shogun/machine/Machine.cpp b/src/shogun/machine/Machine.cpp index 09bce852c85..e9267ddcaf6 100644 --- a/src/shogun/machine/Machine.cpp +++ b/src/shogun/machine/Machine.cpp @@ -71,6 +71,11 @@ bool Machine::train(std::shared_ptr data) return result; } +bool Machine::train(const std::shared_ptr& data, const std::shared_ptr& lab){ + set_labels(lab); + return train(data); +} + void Machine::set_labels(std::shared_ptr lab) { if (lab != NULL) diff --git a/src/shogun/machine/Machine.h b/src/shogun/machine/Machine.h index 98b2627c208..95562b68769 100644 --- a/src/shogun/machine/Machine.h +++ b/src/shogun/machine/Machine.h @@ -154,6 +154,15 @@ class Machine : public StoppableSGObject */ virtual bool train(std::shared_ptr data=NULL); + /** train machine + * + * @param data training data + * @param lab training label + * + * @return whether training was successful + */ + virtual bool train(const std::shared_ptr& data, const std::shared_ptr& lab); + /** apply machine to data * if data is not specified apply to the current features * diff --git a/src/shogun/machine/NonParametricMachine.h b/src/shogun/machine/NonParametricMachine.h new file mode 100644 index 00000000000..bb698bb9949 --- /dev/null +++ b/src/shogun/machine/NonParametricMachine.h @@ -0,0 +1,44 @@ +/* + * This software is distributed under BSD 3-clause license (see LICENSE file). + * + * Authors: Yuhui Liu + */ + +#ifndef NONPARAMETRCMACHINE_H_ +#define NONPARAMETRCMACHINE_H_ + +#include + +namespace shogun +{ + + class NonParametricMachine : public Machine + { + public: + NonParametricMachine(): Machine() + { + //TODO : when all refactor is done, m_labels should be removed from + //Machine Class + // SG_ADD( + // &m_labels, "labels", "labels used in train machine algorithm", + // ParameterProperties::READONLY); + SG_ADD(&m_features, "features_train", + "Training features of nonparametric model", + ParameterProperties::READONLY); + } + virtual ~NonParametricMachine() + { + } + + const char* get_name() const override{ return "NonParametricMachine"; } + + protected: + + std::shared_ptr m_features; + + //TODO + // when all refactor is done, we should use this m_labels + // std::shared_ptr m_labels; + }; +} // namespace shogun +#endif \ No newline at end of file diff --git a/src/shogun/metric/LMNNImpl.cpp b/src/shogun/metric/LMNNImpl.cpp index c533b87491b..1ac1fbeefd0 100644 --- a/src/shogun/metric/LMNNImpl.cpp +++ b/src/shogun/metric/LMNNImpl.cpp @@ -141,7 +141,8 @@ SGMatrix LMNNImpl::find_target_nn(const std::shared_ptr>(slice_mat); auto labels_slice = std::make_shared(labels_vec); - auto knn = std::make_shared(k+1, std::make_shared(features_slice, features_slice), labels_slice); + auto knn = std::make_shared(k+1, std::make_shared()); + knn->train(features_slice, labels_slice); SGMatrix target_slice = knn->nearest_neighbors(); // sanity check ASSERT(target_slice.num_rows==k+1 && target_slice.num_cols==slice_size) diff --git a/src/shogun/multiclass/KNN.cpp b/src/shogun/multiclass/KNN.cpp index 6e0190a858d..b490a01cb0b 100644 --- a/src/shogun/multiclass/KNN.cpp +++ b/src/shogun/multiclass/KNN.cpp @@ -27,7 +27,7 @@ KNN::KNN() init(); } -KNN::KNN(int32_t k, const std::shared_ptr& d, const std::shared_ptr& trainlab, KNN_SOLVER knn_solver) +KNN::KNN(int32_t k, const std::shared_ptr& d, KNN_SOLVER knn_solver) : DistanceMachine() { init(); @@ -35,11 +35,8 @@ KNN::KNN(int32_t k, const std::shared_ptr& d, const std::shared_ptrget_num_labels(); m_knn_solver=knn_solver; } @@ -76,16 +73,13 @@ bool KNN::train_machine(std::shared_ptr data) { require(m_labels, "No training labels provided."); require(distance, "No training distance provided."); - - if (data) - { - require( + require( m_labels->get_num_labels() == data->get_num_vectors(), "Number of training vectors ({}) does not match number of labels " "({})", data->get_num_vectors(), m_labels->get_num_labels()); - distance->init(data, data); - } + m_features = data; + distance->init(data, data); SGVector lab=multiclass_labels(m_labels)->get_int_labels(); m_train_labels=lab.clone(); @@ -158,9 +152,8 @@ SGMatrix KNN::nearest_neighbors() std::shared_ptr KNN::apply_multiclass(std::shared_ptr data) { - if (data) - init_distance(data); - + init_distance(data); + m_features = data; //redirecting to fast (without sorting) classify if k==1 if (m_k == 1) return classify_NN(); @@ -206,21 +199,9 @@ std::shared_ptr KNN::classify_NN() COMPUTATION_CONTROLLERS // get distances from i-th test example to 0..num_m_train_labels-1 train examples distances_lhs(distances,0,m_train_labels.vlen-1,i); - int32_t j; - - // assuming 0th train examples as nearest to i-th test example - int32_t out_idx = 0; - float64_t min_dist = distances.vector[0]; - - // searching for nearest neighbor by comparing distances - for (j=0; jset_label(i,m_train_labels.vector[out_idx]+m_min_label); diff --git a/src/shogun/multiclass/KNN.h b/src/shogun/multiclass/KNN.h index f12c9a1388c..e0f98f367ca 100644 --- a/src/shogun/multiclass/KNN.h +++ b/src/shogun/multiclass/KNN.h @@ -80,7 +80,7 @@ class KNN : public DistanceMachine * @param d distance * @param trainlab labels for training */ - KNN(int32_t k, const std::shared_ptr& d, const std::shared_ptr& trainlab, KNN_SOLVER knn_solver=KNN_BRUTE); + KNN(int32_t k, const std::shared_ptr& d, KNN_SOLVER knn_solver=KNN_BRUTE); ~KNN() override; diff --git a/tests/unit/multiclass/KNN_unittest.cc b/tests/unit/multiclass/KNN_unittest.cc index dd4a23b468e..fd12ac84da3 100644 --- a/tests/unit/multiclass/KNN_unittest.cc +++ b/tests/unit/multiclass/KNN_unittest.cc @@ -85,8 +85,8 @@ class KNNTest : public ::testing::Test // typedef ::testing::Types KNNTypes; TEST_F(KNNTest, brute_solver) { - auto knn = std::make_shared(k, distance, labels, KNN_BRUTE); - knn->train(features); + auto knn = std::make_shared(k, distance, KNN_BRUTE); + knn->train(features, labels); auto output = knn->apply(features_test)->as(); for ( index_t i = 0; i < labels_test->get_num_labels(); ++i ) @@ -95,8 +95,8 @@ TEST_F(KNNTest, brute_solver) TEST_F(KNNTest, kdtree_solver) { - auto knn = std::make_shared(k, distance, labels, KNN_KDTREE); - knn->train(features); + auto knn = std::make_shared(k, distance, KNN_KDTREE); + knn->train(features, labels); auto output = knn->apply(features_test)->as(); for ( index_t i = 0; i < labels_test->get_num_labels(); ++i ) @@ -106,8 +106,8 @@ TEST_F(KNNTest, kdtree_solver) TEST_F(KNNTest, lsh_solver) { - auto knn = std::make_shared(k, distance, labels, KNN_LSH); - knn->train(features); + auto knn = std::make_shared(k, distance, KNN_LSH); + knn->train(features, labels); auto output = knn->apply(features_test)->as(); for ( index_t i = 0; i < labels_test->get_num_labels(); ++i ) @@ -117,11 +117,11 @@ TEST_F(KNNTest, lsh_solver) TEST_F(KNNTest, lsh_solver_sparse) { - auto knn = std::make_shared(k, distance, labels, KNN_LSH); + auto knn = std::make_shared(k, distance, KNN_LSH); // TODO: the sparse features should be actually sparse auto features_sparse = std::make_shared>(features); auto features_test_sparse = std::make_shared>(features_test); - knn->train(features_sparse); + knn->train(features_sparse, labels); auto output = knn->apply(features_test_sparse)->as(); for ( index_t i = 0; i < labels_test->get_num_labels(); ++i ) @@ -153,12 +153,12 @@ TEST(KNN, classify_multiple_brute) int32_t k=4; auto distance = std::make_shared(); - auto knn=std::make_shared (k, distance, labels, KNN_BRUTE); + auto knn=std::make_shared (k, distance, KNN_BRUTE); features->add_subset(train); labels->add_subset(train); - knn->train(features); + knn->train(features, labels); // classify for multiple k features_test->add_subset(test); @@ -203,12 +203,12 @@ TEST(KNN, classify_multiple_kdtree) int32_t k=4; auto distance = std::make_shared(); - auto knn=std::make_shared(k, distance, labels, KNN_KDTREE); + auto knn=std::make_shared(k, distance, KNN_KDTREE); features->add_subset(train); labels->add_subset(train); - knn->train(features); + knn->train(features, labels); // classify for multiple k features_test->add_subset(test); From 5e91dd0b16e9f84ce2b44f904290399e1303cea6 Mon Sep 17 00:00:00 2001 From: LiuYuhui Date: Mon, 22 Jun 2020 05:51:08 +0800 Subject: [PATCH 2/9] Refactor gaussian process machine (#5072) * refactor gp machine --- .../src/gaussian_process/classifier.sg.in | 4 +-- .../gaussian_process/sparse_regression.sg.in | 4 +-- src/interfaces/swig/GaussianProcess.i | 4 +-- .../GaussianProcessClassification.cpp | 27 --------------- src/shogun/machine/GaussianProcess.cpp | 2 +- src/shogun/machine/GaussianProcess.h | 3 +- .../regression/GaussianProcessRegression.cpp | 34 ++----------------- .../GaussianProcessRegression_unittest.cc | 22 ++++++------ 8 files changed, 24 insertions(+), 76 deletions(-) diff --git a/examples/meta/src/gaussian_process/classifier.sg.in b/examples/meta/src/gaussian_process/classifier.sg.in index cc007183063..02d93825ecc 100644 --- a/examples/meta/src/gaussian_process/classifier.sg.in +++ b/examples/meta/src/gaussian_process/classifier.sg.in @@ -18,11 +18,11 @@ MeanFunction mean_function = create_gp_mean("ConstMean") #![create_instance] LikelihoodModel gauss_likelihood = create_gp_likelihood("SoftMaxLikelihood") Inference inference_method = create_gp_inference("MultiLaplaceInferenceMethod", kernel=k, mean_function=mean_function, likelihood_model=gauss_likelihood) -GaussianProcess gp_classifier = create_gaussian_process("GaussianProcessClassification", inference_method=inference_method, seed=1, labels=labels_train) +GaussianProcess gp_classifier = create_gaussian_process("GaussianProcessClassification", inference_method=inference_method, seed=1) #![create_instance] #![train_and_apply] -gp_classifier.train(features_train) +gp_classifier.train(features_train, labels_train) MulticlassLabels labels_predict = gp_classifier.apply_multiclass(features_test) #![train_and_apply] diff --git a/examples/meta/src/gaussian_process/sparse_regression.sg.in b/examples/meta/src/gaussian_process/sparse_regression.sg.in index b070b7e4990..7cdee6dc614 100644 --- a/examples/meta/src/gaussian_process/sparse_regression.sg.in +++ b/examples/meta/src/gaussian_process/sparse_regression.sg.in @@ -30,11 +30,11 @@ Inference inference_method = create_gp_inference("FITCInferenceMethod", kernel=k #![create_inference] #![create_instance] -GaussianProcess gp_regression = create_gaussian_process("GaussianProcessRegression", inference_method=inference_method, labels=labels_train, inducing_features=inducing_features) +GaussianProcess gp_regression = create_gaussian_process("GaussianProcessRegression", inference_method=inference_method, inducing_features=inducing_features) #![create_instance] #![train_and_apply] -gp_regression.train(features_train) +gp_regression.train(features_train, labels_train) RegressionLabels labels_predict = gp_regression.apply_regression(features_test) #![train_and_apply] diff --git a/src/interfaces/swig/GaussianProcess.i b/src/interfaces/swig/GaussianProcess.i index f31adfb4416..fb4af0bd84f 100644 --- a/src/interfaces/swig/GaussianProcess.i +++ b/src/interfaces/swig/GaussianProcess.i @@ -12,10 +12,10 @@ SHARED_RANDOM_INTERFACE(shogun::Inference) SHARED_RANDOM_INTERFACE(shogun::LikelihoodModel) %shared_ptr(shogun::GaussianProcess) SHARED_RANDOM_INTERFACE(shogun::GaussianProcess) - +SHARED_RANDOM_INTERFACE(shogun::NonParametricMachine) /* These functions return new Objects */ -RANDOM_INTERFACE(Machine) +RANDOM_INTERFACE(NonParametricMachine) /* Include Class Headers to make them visible from within the target language */ %include diff --git a/src/shogun/classifier/GaussianProcessClassification.cpp b/src/shogun/classifier/GaussianProcessClassification.cpp index 58dd4d9f7c7..a058e38d4cc 100644 --- a/src/shogun/classifier/GaussianProcessClassification.cpp +++ b/src/shogun/classifier/GaussianProcessClassification.cpp @@ -71,18 +71,6 @@ std::shared_ptr GaussianProcessClassification::apply_multiclas require(m_method->supports_multiclass(), "{} with {} doesn't support " "multi classification\n", m_method->get_name(), lik->get_name()); - // if regression data equals to NULL, then apply classification on training - // features - if (!data) - { - if (m_method->get_inference_type()==INF_SPARSE) - { - not_implemented(SOURCE_LOCATION); - } - else - data=m_method->get_features(); - } - const index_t n=data->get_num_vectors(); SGVector mean=get_mean_vector(data); const index_t C=mean.vlen/n; @@ -110,21 +98,6 @@ std::shared_ptr GaussianProcessClassification::apply_binary( require(m_method->supports_binary(), "{} with {} doesn't support " "binary classification\n", m_method->get_name(), lik->get_name()); - if (!data) - { - if (m_method->get_inference_type()== INF_FITC_LAPLACE_SINGLE) - { -#ifdef USE_GPL_SHOGUN - auto fitc_method = m_method->as(); - data=fitc_method->get_inducing_features(); -#else - gpl_only(SOURCE_LOCATION); -#endif //USE_GPL_SHOGUN - } - else - data=m_method->get_features(); - } - auto result=std::make_shared(get_mean_vector(data)); if (m_compute_variance) result->put("current_values", get_variance_vector(data)); diff --git a/src/shogun/machine/GaussianProcess.cpp b/src/shogun/machine/GaussianProcess.cpp index b6f6636d052..fb5aea01a02 100644 --- a/src/shogun/machine/GaussianProcess.cpp +++ b/src/shogun/machine/GaussianProcess.cpp @@ -23,7 +23,7 @@ using namespace shogun; using namespace Eigen; -GaussianProcess::GaussianProcess() : RandomMixin() +GaussianProcess::GaussianProcess() : RandomMixin() { init(); } diff --git a/src/shogun/machine/GaussianProcess.h b/src/shogun/machine/GaussianProcess.h index 6e4b936e693..da175de103e 100644 --- a/src/shogun/machine/GaussianProcess.h +++ b/src/shogun/machine/GaussianProcess.h @@ -18,6 +18,7 @@ #include #include #include +#include namespace shogun { @@ -33,7 +34,7 @@ namespace shogun * * where \f$m(x)\f$ - mean function, \f$k(x, x')\f$ - covariance function. */ - class GaussianProcess : public RandomMixin + class GaussianProcess : public RandomMixin { public: /** default constructor */ diff --git a/src/shogun/regression/GaussianProcessRegression.cpp b/src/shogun/regression/GaussianProcessRegression.cpp index 33495b00ebb..e2b56851b38 100644 --- a/src/shogun/regression/GaussianProcessRegression.cpp +++ b/src/shogun/regression/GaussianProcessRegression.cpp @@ -37,37 +37,9 @@ std::shared_ptr GaussianProcessRegression::apply_regression(st require(m_method->supports_regression(), "{} with {} doesn't support " "regression", m_method->get_name(), lik->get_name()); - - std::shared_ptr result; - - // if regression data equals to NULL, then apply regression on training - // features - if (!data) - { - std::shared_ptr feat; - - // use inducing features for FITC inference method - if (m_method->get_inference_type()==INF_FITC_REGRESSION) - { - auto fitc_method = m_method->as(); - feat=fitc_method->get_inducing_features(); - } - else - feat=m_method->get_features(); - - result=std::make_shared(get_mean_vector(feat)); - if (m_compute_variance) - result->put("current_values", get_variance_vector(feat)); - - - } - else - { - result=std::make_shared(get_mean_vector(data)); - if (m_compute_variance) - result->put("current_values", get_variance_vector(data)); - } - + auto result=std::make_shared(get_mean_vector(data)); + if (m_compute_variance) + result->set_values(get_variance_vector(data)); return result; } diff --git a/tests/unit/regression/GaussianProcessRegression_unittest.cc b/tests/unit/regression/GaussianProcessRegression_unittest.cc index acf9200bc30..276a990631a 100644 --- a/tests/unit/regression/GaussianProcessRegression_unittest.cc +++ b/tests/unit/regression/GaussianProcessRegression_unittest.cc @@ -198,16 +198,17 @@ TEST(GaussianProcessRegression, apply_regression_on_training_features) auto liklihood=std::make_shared(0.25); // specify GP regression with exact inference - auto inf=std::make_shared(kernel, features_train, - mean, labels_train, liklihood); - + auto inf=std::make_shared(); + inf->set_mean(mean); + inf->set_kernel(kernel); + inf->set_model(liklihood); auto gpr=std::make_shared(inf); // train model - gpr->train(); + gpr->train(features_train, labels_train); // apply regression - auto predictions=gpr->apply_regression(); + auto predictions=gpr->apply_regression(features_train); SGVector prediction_vector=predictions->get_labels(); // comparison of predictions with result from GPML package: @@ -452,16 +453,17 @@ TEST(GaussianProcessRegression,apply_regression_scaled_kernel) auto lik=std::make_shared(0.25); // specify GP regression with exact inference - auto inf=std::make_shared(kernel, features_train, - mean, labels_train, lik); + auto inf=std::make_shared(); inf->set_scale(0.8); - + inf->set_mean(mean); + inf->set_kernel(kernel); + inf->set_model(lik); // create GPR and train auto gpr=std::make_shared(inf); - gpr->train(); + gpr->train(features_train, labels_train); // apply regression to train features - auto predictions=gpr->apply_regression(); + auto predictions=gpr->apply_regression(features_train); // comparison of predictions with result from GPML package SGVector mu=predictions->get_labels(); From 8e8149be4242939adb4e6985acc197734a67ca65 Mon Sep 17 00:00:00 2001 From: LiuYuhui Date: Wed, 1 Jul 2020 16:22:21 +0800 Subject: [PATCH 3/9] Refactor KernelMachine (#5075) * Refactor KernelMachine --- src/shogun/machine/KernelMachine.cpp | 10 +++------ src/shogun/machine/KernelMachine.h | 4 ++-- src/shogun/regression/KRRNystrom.cpp | 4 ++-- src/shogun/regression/KRRNystrom.h | 6 ++--- .../regression/KernelRidgeRegression.cpp | 22 +++++-------------- src/shogun/regression/KernelRidgeRegression.h | 2 +- .../observers/ParameterObserverCV_unittest.cc | 2 +- tests/unit/regression/krrnystrom_unittest.cc | 16 +++++++------- 8 files changed, 25 insertions(+), 41 deletions(-) diff --git a/src/shogun/machine/KernelMachine.cpp b/src/shogun/machine/KernelMachine.cpp index d8813cabb54..3a7239bf1b0 100644 --- a/src/shogun/machine/KernelMachine.cpp +++ b/src/shogun/machine/KernelMachine.cpp @@ -38,16 +38,14 @@ struct S_THREAD_PARAM_KERNEL_MACHINE }; #endif // DOXYGEN_SHOULD_SKIP_THIS -KernelMachine::KernelMachine() : Machine() +KernelMachine::KernelMachine() : NonParametricMachine() { init(); } KernelMachine::KernelMachine(const std::shared_ptr& k, SGVector alphas, - SGVector svs, float64_t b) : Machine() + SGVector svs, float64_t b) : KernelMachine() { - init(); - int32_t num_sv=svs.vlen; ASSERT(num_sv == alphas.vlen) create_new_model(num_sv); @@ -57,10 +55,8 @@ KernelMachine::KernelMachine(const std::shared_ptr& k, SGVector& machine) : Machine() +KernelMachine::KernelMachine(const std::shared_ptr& machine) : KernelMachine() { - init(); - SGVector alphas = machine->get_alphas().clone(); SGVector svs = machine->get_support_vectors().clone(); float64_t bias = machine->get_bias(); diff --git a/src/shogun/machine/KernelMachine.h b/src/shogun/machine/KernelMachine.h index f79b310b71f..15fc312215b 100644 --- a/src/shogun/machine/KernelMachine.h +++ b/src/shogun/machine/KernelMachine.h @@ -15,7 +15,7 @@ #include #include #include - +#include namespace shogun { @@ -41,7 +41,7 @@ class Features; * Using an a-priori choosen kernel, the \f$\alpha_i\f$ and bias are determined * in a training procedure. */ -class KernelMachine : public Machine +class KernelMachine : public NonParametricMachine { public: /** default constructor */ diff --git a/src/shogun/regression/KRRNystrom.cpp b/src/shogun/regression/KRRNystrom.cpp index 4a4ba94f3c6..43a1c9102e1 100644 --- a/src/shogun/regression/KRRNystrom.cpp +++ b/src/shogun/regression/KRRNystrom.cpp @@ -45,8 +45,8 @@ KRRNystrom::KRRNystrom() : RandomMixin() init(); } -KRRNystrom::KRRNystrom(float64_t tau, int32_t m, std::shared_ptr k, std::shared_ptr lab) -: RandomMixin(tau, std::move(k), std::move(lab)) +KRRNystrom::KRRNystrom(float64_t tau, int32_t m, std::shared_ptr k) +: RandomMixin(tau, std::move(k)) { init(); diff --git a/src/shogun/regression/KRRNystrom.h b/src/shogun/regression/KRRNystrom.h index 07fec24d201..d5e4784b241 100644 --- a/src/shogun/regression/KRRNystrom.h +++ b/src/shogun/regression/KRRNystrom.h @@ -77,7 +77,7 @@ class KRRNystrom : public RandomMixin * @param k kernel * @param lab labels */ - KRRNystrom(float64_t tau, int32_t m, std::shared_ptr k, std::shared_ptr lab); + KRRNystrom(float64_t tau, int32_t m, std::shared_ptr k); /** Default destructor */ ~KRRNystrom() override {} @@ -100,12 +100,12 @@ less than number of data points ({})", m_num_rkhs_basis, n); }; - bool train_machine(std::shared_ptrdata) override; - /** @return object name */ const char* get_name() const override { return "KRRNystrom"; } protected: + bool train_machine(std::shared_ptrdata) override; + /** Train regression using the Nyström method. * * @return boolean to indicate success diff --git a/src/shogun/regression/KernelRidgeRegression.cpp b/src/shogun/regression/KernelRidgeRegression.cpp index ea1768f51ec..b07216d822e 100644 --- a/src/shogun/regression/KernelRidgeRegression.cpp +++ b/src/shogun/regression/KernelRidgeRegression.cpp @@ -23,13 +23,12 @@ KernelRidgeRegression::KernelRidgeRegression() init(); } -KernelRidgeRegression::KernelRidgeRegression(float64_t tau, std::shared_ptr k, std::shared_ptr lab) +KernelRidgeRegression::KernelRidgeRegression(float64_t tau, std::shared_ptr k) : KernelMachine() { init(); set_tau(tau); - set_labels(std::move(lab)); set_kernel(std::move(k)); } @@ -66,21 +65,10 @@ bool KernelRidgeRegression::solve_krr_system() bool KernelRidgeRegression::train_machine(std::shared_ptrdata) { - require(m_labels, "No labels set"); - - if (data) - { - if (m_labels->get_num_labels() != data->get_num_vectors()) - error("Number of training vectors does not match number of labels"); - kernel->init(data, data); - } - ASSERT(kernel && kernel->has_features()) - - if (m_labels->get_num_labels() != kernel->get_num_vec_rhs()) - { - error("Number of labels does not match number of kernel" - " columns (num_labels={} cols={}", m_labels->get_num_labels(), kernel->get_num_vec_rhs()); - } + require(m_labels->get_num_labels() == data->get_num_vectors(), + "Number of training vectors does not match number of labels"); + require(kernel, "Kernel not set"); + kernel->init(data, data); // allocate alpha vector set_alphas(SGVector(m_labels->get_num_labels())); diff --git a/src/shogun/regression/KernelRidgeRegression.h b/src/shogun/regression/KernelRidgeRegression.h index 9d4b6b04030..3115d4622fa 100644 --- a/src/shogun/regression/KernelRidgeRegression.h +++ b/src/shogun/regression/KernelRidgeRegression.h @@ -61,7 +61,7 @@ class KernelRidgeRegression : public KernelMachine * @param k kernel * @param lab labels */ - KernelRidgeRegression(float64_t tau, std::shared_ptr k, std::shared_ptr lab); + KernelRidgeRegression(float64_t tau, std::shared_ptr k); /** default destructor */ ~KernelRidgeRegression() override {} diff --git a/tests/unit/lib/observers/ParameterObserverCV_unittest.cc b/tests/unit/lib/observers/ParameterObserverCV_unittest.cc index d71e2fef1a5..18ef81aa1b3 100644 --- a/tests/unit/lib/observers/ParameterObserverCV_unittest.cc +++ b/tests/unit/lib/observers/ParameterObserverCV_unittest.cc @@ -53,7 +53,7 @@ std::shared_ptr generate(bool locked = true) /* kernel ridge regression*/ float64_t tau = 0.0001; auto krr = - std::make_shared(tau, kernel, labels); + std::make_shared(tau, kernel); /* evaluation criterion */ auto eval_crit = std::make_shared(); diff --git a/tests/unit/regression/krrnystrom_unittest.cc b/tests/unit/regression/krrnystrom_unittest.cc index afcd994bfaa..1db56a3ad13 100644 --- a/tests/unit/regression/krrnystrom_unittest.cc +++ b/tests/unit/regression/krrnystrom_unittest.cc @@ -84,11 +84,11 @@ TEST(KRRNystrom, apply_and_compare_to_KRR_with_all_columns) /* kernel ridge regression and the nystrom approximation */ float64_t tau=0.01; - auto nystrom=std::make_shared(tau, num_vectors, kernel, labels); - auto krr=std::make_shared(tau, kernel_krr, labels_krr); + auto nystrom=std::make_shared(tau, num_vectors, kernel); + auto krr=std::make_shared(tau, kernel_krr); - nystrom->train(features); - krr->train(features); + nystrom->train(features, labels); + krr->train(features, labels_krr); SGVector alphas=nystrom->get_alphas(); SGVector alphas_krr=krr->get_alphas(); @@ -151,11 +151,11 @@ TEST(KRRNystrom, apply_and_compare_to_KRR_with_column_subset) /* kernel ridge regression and the nystrom approximation */ float64_t tau=0.01; - auto nystrom=std::make_shared(tau, num_basis_rkhs, kernel, labels); - auto krr=std::make_shared(tau, kernel_krr, labels_krr); + auto nystrom=std::make_shared(tau, num_basis_rkhs, kernel); + auto krr=std::make_shared(tau, kernel_krr); - nystrom->train(features); - krr->train(features); + nystrom->train(features, labels); + krr->train(features, labels_krr); auto result = nystrom->apply_regression(test_features); From a02018b88ebedac7ba63574daf8b5f573fc3a57d Mon Sep 17 00:00:00 2001 From: LiuYuhui Date: Sun, 12 Jul 2020 21:35:46 +0800 Subject: [PATCH 4/9] Refactor LinearMachine (#5089) * refactor linear machine * fix unit tests * update gpl submodule * use DotFeatures in train_machine --- data | 2 +- .../classification/Classification.ipynb | 37 ++++--------- .../classification/HashedDocDotFeatures.ipynb | 12 ++--- .../SupportVectorMachines.ipynb | 5 +- .../intro/Introduction.ipynb | 10 ++-- .../regression/Regression.ipynb | 21 ++++---- .../meta/src/base_api/dense_dispatching.sg.in | 6 +-- .../meta/src/binary/averaged_perceptron.sg.in | 4 +- .../binary/linear_discriminant_analysis.sg.in | 4 +- .../linear_support_vector_machine.sg.in | 4 +- .../newton_support_vector_machine.sg.in | 4 +- examples/meta/src/binary/perceptron.sg.in | 4 +- examples/meta/src/binary/svmlin.sg.in | 4 +- examples/meta/src/binary/svmocas.sg.in | 4 +- examples/meta/src/binary/svmsgd.sg.in | 4 +- .../src/evaluation/cross_validation.sg.in | 6 +-- .../meta/src/evaluation/multiclass_ovr.sg.in | 2 +- examples/meta/src/multiclass/linear.sg.in | 2 +- .../src/multiclass/logistic_regression.sg.in | 2 +- .../src/multiclass/multiclassliblinear.sg.in | 2 +- .../observers/least_angle_regression.sg.in | 4 +- .../regression/least_angle_regression.sg.in | 4 +- .../regression/linear_ridge_regression.sg.in | 8 +-- ...multitask_clustered_logistic_regression.py | 6 +-- ...nsfer_multitask_l12_logistic_regression.py | 6 +-- ...nsfer_multitask_leastsquares_regression.py | 6 +-- .../transfer_multitask_logistic_regression.py | 6 +-- ...fer_multitask_trace_logistic_regression.py | 6 +-- src/gpl | 2 +- src/shogun/classifier/AveragedPerceptron.cpp | 21 ++------ src/shogun/classifier/AveragedPerceptron.h | 6 ++- src/shogun/classifier/LDA.cpp | 37 +++++-------- src/shogun/classifier/LDA.h | 36 +++++-------- src/shogun/classifier/Perceptron.cpp | 15 ++---- src/shogun/classifier/Perceptron.h | 6 ++- src/shogun/classifier/svm/LibLinear.cpp | 32 +++-------- src/shogun/classifier/svm/LibLinear.h | 10 ++-- src/shogun/classifier/svm/NewtonSVM.cpp | 38 +++++-------- src/shogun/classifier/svm/NewtonSVM.h | 18 ++++--- src/shogun/classifier/svm/SGDQN.cpp | 41 +++----------- src/shogun/classifier/svm/SGDQN.h | 27 ++-------- src/shogun/classifier/svm/SVMOcas.cpp | 26 +++------ src/shogun/classifier/svm/SVMOcas.h | 12 ++--- src/shogun/evaluation/CrossValidation.cpp | 11 +++- src/shogun/latent/LatentSVM.cpp | 8 +-- src/shogun/machine/DirectorLinearMachine.h | 41 ++------------ src/shogun/machine/FeatureDispatchCRTP.h | 24 ++++++--- src/shogun/machine/IterativeMachine.h | 25 ++++----- src/shogun/machine/LinearMachine.cpp | 37 +++---------- src/shogun/machine/LinearMachine.h | 38 ++++++------- src/shogun/machine/LinearMulticlassMachine.h | 53 +++++++++++++------ src/shogun/machine/Machine.cpp | 27 ++++++++-- src/shogun/machine/Machine.h | 33 +++++++++++- src/shogun/machine/MulticlassMachine.cpp | 19 +++---- src/shogun/machine/MulticlassMachine.h | 3 +- src/shogun/machine/NonParametricMachine.h | 29 ++++++---- src/shogun/machine/Pipeline.cpp | 14 +++-- .../regression/KernelRidgeRegression.cpp | 9 ++-- .../regression/LeastAngleRegression.cpp | 18 +++++-- src/shogun/regression/LeastAngleRegression.h | 9 ++-- .../regression/LeastSquaresRegression.cpp | 5 +- .../regression/LeastSquaresRegression.h | 8 +-- .../regression/LinearRidgeRegression.cpp | 10 ++-- src/shogun/regression/LinearRidgeRegression.h | 8 +-- .../regression/svr/LibLinearRegression.cpp | 27 +++------- .../regression/svr/LibLinearRegression.h | 8 +-- .../DomainAdaptationMulticlassLibLinear.cpp | 6 +-- .../DomainAdaptationMulticlassLibLinear.h | 2 +- .../DomainAdaptationSVMLinear.cpp | 46 ++-------------- .../DomainAdaptationSVMLinear.h | 7 ++- .../transfer/multitask/LibLinearMTL.cpp | 47 +++++----------- src/shogun/transfer/multitask/LibLinearMTL.h | 24 ++------- tests/unit/classifier/LDA_unittest.cc | 6 +-- tests/unit/classifier/Perceptron_unittest.cc | 9 ++-- .../unit/classifier/svm/LibLinear_unittest.cc | 8 +-- tests/unit/classifier/svm/SVMOcas_unittest.cc | 4 +- .../evaluation/CrossValidation_unittest.cc | 5 +- .../machine/FeatureDispatchCRTP_unittest.cc | 27 +++++----- .../MulticlassLibLinear_unittest.cc | 5 +- .../LibLinearRegression_unittest.cc | 10 ++-- tests/unit/regression/lars_unittest.cc | 21 +++----- tests/unit/transfer/MALSAR_unittest.cc | 21 ++++---- 82 files changed, 512 insertions(+), 712 deletions(-) diff --git a/data b/data index 126500ba4b8..8b407372f39 160000 --- a/data +++ b/data @@ -1 +1 @@ -Subproject commit 126500ba4b8fec148a5e43f5376938c0b351d675 +Subproject commit 8b407372f396a95f4b8fafdbee5e7b7755fddef9 diff --git a/doc/ipython-notebooks/classification/Classification.ipynb b/doc/ipython-notebooks/classification/Classification.ipynb index 2e3bc138d8b..5ddbfc3afb4 100644 --- a/doc/ipython-notebooks/classification/Classification.ipynb +++ b/doc/ipython-notebooks/classification/Classification.ipynb @@ -212,10 +212,9 @@ "epsilon = 1e-3\n", "\n", "svm_linear = sg.create_machine(\"LibLinear\", C1=c, C2=c, \n", - " labels=shogun_labels_linear, \n", " epsilon=epsilon,\n", " liblinear_solver_type=\"L2R_L2LOSS_SVC\")\n", - "svm_linear.train(shogun_feats_linear)\n", + "svm_linear.train(shogun_feats_linear, shogun_labels_linear)\n", "classifiers_linear.append(svm_linear)\n", "classifiers_names.append(\"SVM Linear\")\n", "fadings.append(True)\n", @@ -224,11 +223,10 @@ "plt.title(\"Linear SVM - Linear Features\")\n", "plot_model(plt,svm_linear,feats_linear,labels_linear)\n", "\n", - "svm_non_linear = sg.create_machine(\"LibLinear\", C1=c, C2=c, \n", - " labels=shogun_labels_non_linear,\n", + "svm_non_linear = sg.create_machine(\"LibLinear\", C1=c, C2=c,\n", " epsilon=epsilon,\n", " liblinear_solver_type=\"L2R_L2LOSS_SVC\")\n", - "svm_non_linear.train(shogun_feats_non_linear)\n", + "svm_non_linear.train(shogun_feats_non_linear, shogun_labels_non_linear)\n", "classifiers_non_linear.append(svm_non_linear)\n", "\n", "plt.subplot(122)\n", @@ -484,8 +482,8 @@ "source": [ "gamma = 0.1\n", "\n", - "lda_linear = sg.create_machine('LDA', gamma=gamma, labels=shogun_labels_linear)\n", - "lda_linear.train(shogun_feats_linear)\n", + "lda_linear = sg.create_machine('LDA', gamma=gamma)\n", + "lda_linear.train(shogun_feats_linear, shogun_labels_linear)\n", "classifiers_linear.append(lda_linear)\n", "classifiers_names.append(\"LDA\")\n", "fadings.append(True)\n", @@ -495,8 +493,8 @@ "plt.title(\"LDA - Linear Features\")\n", "plot_model(plt,lda_linear,feats_linear,labels_linear)\n", "\n", - "lda_non_linear = sg.create_machine('LDA', gamma=gamma, labels=shogun_labels_non_linear)\n", - "lda_non_linear.train(shogun_feats_non_linear)\n", + "lda_non_linear = sg.create_machine('LDA', gamma=gamma)\n", + "lda_non_linear.train(shogun_feats_non_linear, shogun_labels_non_linear)\n", "classifiers_non_linear.append(lda_non_linear)\n", "\n", "plt.subplot(122)\n", @@ -666,22 +664,9 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "figure = plt.figure(figsize=(30,9))\n", "plt.subplot(2,11,1)\n", @@ -723,7 +708,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/doc/ipython-notebooks/classification/HashedDocDotFeatures.ipynb b/doc/ipython-notebooks/classification/HashedDocDotFeatures.ipynb index bc001cf9be1..b81728bfb61 100644 --- a/doc/ipython-notebooks/classification/HashedDocDotFeatures.ipynb +++ b/doc/ipython-notebooks/classification/HashedDocDotFeatures.ipynb @@ -190,7 +190,7 @@ "source": [ "C = 0.1\n", "epsilon = 0.01\n", - "svm = sg.create_machine(\"SVMOcas\", C1=C, C2=C, labels=labels, epsilon=epsilon)" + "svm = sg.create_machine(\"SVMOcas\", C1=C, C2=C, epsilon=epsilon)" ] }, { @@ -207,7 +207,7 @@ "metadata": {}, "outputs": [], "source": [ - "_=svm.train(hashed_feats)" + "_=svm.train(hashed_feats, labels)" ] }, { @@ -224,7 +224,7 @@ "metadata": {}, "outputs": [], "source": [ - "predicted_labels = svm.apply()\n", + "predicted_labels = svm.apply(hashed_feats)\n", "print(predicted_labels.get(\"labels\"))" ] }, @@ -286,8 +286,8 @@ "metadata": {}, "outputs": [], "source": [ - "svm.train(hashed_feats_quad)\n", - "predicted_labels = svm.apply()\n", + "svm.train(hashed_feats_quad, labels)\n", + "predicted_labels = svm.apply(hashed_feats_quad)\n", "print(predicted_labels.get(\"labels\"))" ] }, @@ -454,4 +454,4 @@ }, "nbformat": 4, "nbformat_minor": 1 -} +} \ No newline at end of file diff --git a/doc/ipython-notebooks/classification/SupportVectorMachines.ipynb b/doc/ipython-notebooks/classification/SupportVectorMachines.ipynb index b58eabeab28..e39e2c4f925 100644 --- a/doc/ipython-notebooks/classification/SupportVectorMachines.ipynb +++ b/doc/ipython-notebooks/classification/SupportVectorMachines.ipynb @@ -164,8 +164,7 @@ "svm=sg.create_machine('LibLinear', C1=C, C2=C, liblinear_solver_type='L2R_L2LOSS_SVC', epsilon=epsilon)\n", "\n", "#train\n", - "svm.put('labels', labels)\n", - "svm.train(feats_train)\n", + "svm.train(feats_train, labels)\n", "w=svm.get('w')\n", "b=svm.get('bias')" ] @@ -1001,7 +1000,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/doc/ipython-notebooks/intro/Introduction.ipynb b/doc/ipython-notebooks/intro/Introduction.ipynb index 94029f81a2f..c9feae74d1f 100644 --- a/doc/ipython-notebooks/intro/Introduction.ipynb +++ b/doc/ipython-notebooks/intro/Introduction.ipynb @@ -338,10 +338,10 @@ "#prameters to svm\n", "C=0.9\n", "\n", - "svm=sg.create_machine(\"LibLinear\", C1=C, C2=C, labels=labels, \n", + "svm=sg.create_machine(\"LibLinear\", C1=C, C2=C,\n", " liblinear_solver_type=\"L2R_L2LOSS_SVC\")\n", "#train\n", - "svm.train(feats_train)\n", + "svm.train(feats_train, labels)\n", "\n", "size=100" ] @@ -495,11 +495,11 @@ "label_e=trainlab[num_train:]\n", "labels_true=sg.create_labels(label_e)\n", "\n", - "svm=sg.create_machine(\"LibLinear\", C1=C, C2=C, labels=labels, \n", + "svm=sg.create_machine(\"LibLinear\", C1=C, C2=C,\n", " liblinear_solver_type=\"L2R_L2LOSS_SVC\")\n", "\n", "#train and evaluate\n", - "svm.train(feats_train)\n", + "svm.train(feats_train, labels)\n", "output=svm.apply(feats_evaluate)\n", "\n", "#use AccuracyMeasure to get accuracy\n", @@ -688,7 +688,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/doc/ipython-notebooks/regression/Regression.ipynb b/doc/ipython-notebooks/regression/Regression.ipynb index 7f8b7b48de4..2c5d84a750e 100644 --- a/doc/ipython-notebooks/regression/Regression.ipynb +++ b/doc/ipython-notebooks/regression/Regression.ipynb @@ -142,8 +142,8 @@ "metadata": {}, "outputs": [], "source": [ - "ls = sg.create_machine(\"LeastSquaresRegression\", labels=labels_train, features=feats_train)\n", - "ls.train(feats_train)\n", + "ls = sg.create_machine(\"LeastSquaresRegression\")\n", + "ls.train(feats_train, labels_train)\n", "w = ls.get('w')\n", "print('Weights:')\n", "print(w)" @@ -244,8 +244,8 @@ "outputs": [], "source": [ "tau = 0.8\n", - "rr = sg.create_machine(\"LinearRidgeRegression\", tau=tau, features=feats_train, labels=labels_train)\n", - "rr.train(feats_train)\n", + "rr = sg.create_machine(\"LinearRidgeRegression\", tau=tau)\n", + "rr.train(feats_train, labels_train)\n", "w = rr.get('w')\n", "print(w)\n", "out = rr.apply(feats_test).get(\"labels\")" @@ -311,12 +311,12 @@ " preproc.fit(feats_train)\n", " processed_feats = preproc.transform(feats_train) \n", " weights = []\n", - " rr = sg.create_machine(\"LinearRidgeRegression\", tau=tau, labels=labels_train, use_bias=use_bias)\n", + " rr = sg.create_machine(\"LinearRidgeRegression\", tau=tau, use_bias=use_bias)\n", " \n", " #vary regularization\n", " for t in taus:\n", " rr.put('tau', t)\n", - " rr.train(processed_feats)\n", + " rr.train(processed_feats, labels_train)\n", " weights.append(rr.get(\"w\"))\n", " return weights, rr\n", "\n", @@ -553,8 +553,7 @@ "source": [ "#Train and generate weights\n", "la=sg.create_machine(\"LeastAngleRegression\")\n", - "la.put('labels', labels_train)\n", - "la.train(feats_train)\n", + "la.train(feats_train, labels_train)\n", "\n", "size=la.get(\"path_size\")\n", "print (\"Size of path is %s\" %size)" @@ -674,8 +673,8 @@ "width=0.5\n", "tau=0.5\n", "kernel=sg.create_kernel(\"GaussianKernel\", width=width)\n", - "krr=sg.create_machine(\"KernelRidgeRegression\", tau=tau, kernel=kernel, labels=train_labels)\n", - "krr.train(feats_train)\n", + "krr=sg.create_machine(\"KernelRidgeRegression\", tau=tau, kernel=kernel)\n", + "krr.train(feats_train, train_labels)\n", "\n", "feats_test=sg.create_features(x1.reshape(1,len(x1)))\n", "kernel.init(feats_train, feats_test)\n", @@ -887,7 +886,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/examples/meta/src/base_api/dense_dispatching.sg.in b/examples/meta/src/base_api/dense_dispatching.sg.in index eef4d5ff3a7..9539d9a9666 100644 --- a/examples/meta/src/base_api/dense_dispatching.sg.in +++ b/examples/meta/src/base_api/dense_dispatching.sg.in @@ -9,13 +9,13 @@ Labels labels_train = create_labels(f_labels_train) #![create_features] #![create_instance] -Machine lda = create_machine("LDA", labels=labels_train) +Machine lda = create_machine("LDA") #![create_instance] #![train_with_double] -lda.train(features_double) +lda.train(features_double, labels_train) #![train_with_double] #![train_with_float] -lda.train(features_float) +lda.train(features_float, labels_train) #![train_with_float] diff --git a/examples/meta/src/binary/averaged_perceptron.sg.in b/examples/meta/src/binary/averaged_perceptron.sg.in index d40c9ed1f29..d2a133529f8 100644 --- a/examples/meta/src/binary/averaged_perceptron.sg.in +++ b/examples/meta/src/binary/averaged_perceptron.sg.in @@ -11,11 +11,11 @@ Labels labels_test = create_labels(f_labels_test) #![create_features] #![set_parameters] -Machine perceptron = create_machine("AveragedPerceptron", labels=labels_train, learn_rate=1.0, max_iterations=1000) +Machine perceptron = create_machine("AveragedPerceptron", learn_rate=1.0, max_iterations=1000) #![set_parameters] #![train_and_apply] -perceptron.train(features_train) +perceptron.train(features_train, labels_train) Labels labels_predict = perceptron.apply(features_test) #![train_and_apply] diff --git a/examples/meta/src/binary/linear_discriminant_analysis.sg.in b/examples/meta/src/binary/linear_discriminant_analysis.sg.in index 02b6f65229a..628abd8d622 100644 --- a/examples/meta/src/binary/linear_discriminant_analysis.sg.in +++ b/examples/meta/src/binary/linear_discriminant_analysis.sg.in @@ -11,11 +11,11 @@ Labels labels_test = create_labels(f_labels_test) #![create_features] #![create_instance] -Machine lda = create_machine("LDA", labels=labels_train) +Machine lda = create_machine("LDA") #![create_instance] #![train_and_apply] -lda.train(features_train) +lda.train(features_train, labels_train) Labels labels_predict = lda.apply(features_test) RealVector labels = labels_predict.get_real_vector("labels") #![train_and_apply] diff --git a/examples/meta/src/binary/linear_support_vector_machine.sg.in b/examples/meta/src/binary/linear_support_vector_machine.sg.in index 00ed07bd8b8..49e00c825b4 100644 --- a/examples/meta/src/binary/linear_support_vector_machine.sg.in +++ b/examples/meta/src/binary/linear_support_vector_machine.sg.in @@ -16,11 +16,11 @@ real epsilon = 0.001 #![set_parameters] #![create_instance] -Machine svm = create_machine("LibLinear", C1=C, C2=C, labels=labels_train, epsilon=epsilon, liblinear_solver_type="L2R_L2LOSS_SVC", use_bias=True) +Machine svm = create_machine("LibLinear", C1=C, C2=C, epsilon=epsilon, liblinear_solver_type="L2R_L2LOSS_SVC", use_bias=True) #![create_instance] #![train_and_apply] -svm.train(features_train) +svm.train(features_train, labels_train) Labels labels_predict = svm.apply(features_test) #![train_and_apply] diff --git a/examples/meta/src/binary/newton_support_vector_machine.sg.in b/examples/meta/src/binary/newton_support_vector_machine.sg.in index 2bf77e06695..d919fed7de9 100644 --- a/examples/meta/src/binary/newton_support_vector_machine.sg.in +++ b/examples/meta/src/binary/newton_support_vector_machine.sg.in @@ -11,11 +11,11 @@ Labels labels_test = create_labels(f_labels_test) #![create_features] #![create_instance] -Machine svm = create_machine("NewtonSVM", labels=labels_train) +Machine svm = create_machine("NewtonSVM") #![create_instance] #![train_and_apply] -svm.train(features_train) +svm.train(features_train, labels_train) BinaryLabels labels_predict = svm.apply_binary(features_test) #![train_and_apply] diff --git a/examples/meta/src/binary/perceptron.sg.in b/examples/meta/src/binary/perceptron.sg.in index 5710db4be7c..807c6434ff0 100644 --- a/examples/meta/src/binary/perceptron.sg.in +++ b/examples/meta/src/binary/perceptron.sg.in @@ -11,11 +11,11 @@ Labels labels_test = create_labels(f_labels_test) #![create_features] #![create_instance] -Machine perceptron = create_machine("Perceptron", labels=labels_train, learn_rate=1.0, max_iterations=1000) +Machine perceptron = create_machine("Perceptron", learn_rate=1.0, max_iterations=1000) #![create_instance] #![train_and_apply] -perceptron.train(features_train) +perceptron.train(features_train, labels_train) Labels labels_predict = perceptron.apply(features_test) #![train_and_apply] diff --git a/examples/meta/src/binary/svmlin.sg.in b/examples/meta/src/binary/svmlin.sg.in index 1c241d6665a..8d361932b91 100644 --- a/examples/meta/src/binary/svmlin.sg.in +++ b/examples/meta/src/binary/svmlin.sg.in @@ -8,8 +8,8 @@ Features feats_test = create_features(f_feats_test) Labels labels_train = create_labels(f_labels_train) Labels labels_test = create_labels(f_labels_test) -Machine svm = create_machine("SVMLin", C1=0.9, C2=0.9, epsilon=0.00001, labels=labels_train) -svm.train(feats_train) +Machine svm = create_machine("SVMLin", C1=0.9, C2=0.9, epsilon=0.00001) +svm.train(feats_train, labels_train) RealVector weights = svm.get_real_vector("w") real bias = svm.get_real("bias") diff --git a/examples/meta/src/binary/svmocas.sg.in b/examples/meta/src/binary/svmocas.sg.in index 124f25fb132..19e640c7f18 100644 --- a/examples/meta/src/binary/svmocas.sg.in +++ b/examples/meta/src/binary/svmocas.sg.in @@ -9,11 +9,11 @@ Labels labels_train = create_labels(f_labels_train) #![create_features] #![create_classifier] -Machine svm = create_machine("SVMOcas", features=feats_train, labels=labels_train, C1=0.9, C2=0.9, epsilon=0.00001, use_bias=True) +Machine svm = create_machine("SVMOcas", C1=0.9, C2=0.9, epsilon=0.00001, use_bias=True) #![create_classifier] #![train_and_extract_weights] -svm.train() +svm.train(feats_train, labels_train) RealVector weights = svm.get_real_vector("w") real bias = svm.get_real("bias") #![train_and_extract_weights] diff --git a/examples/meta/src/binary/svmsgd.sg.in b/examples/meta/src/binary/svmsgd.sg.in index 75c158e1c06..29a7bb74129 100644 --- a/examples/meta/src/binary/svmsgd.sg.in +++ b/examples/meta/src/binary/svmsgd.sg.in @@ -8,8 +8,8 @@ Features feats_test = create_features(f_feats_test) Labels labels_train = create_labels(f_labels_train) Labels labels_test = create_labels(f_labels_test) -Machine svm = create_machine("SVMSGD", C1=0.9, C2=0.9, epochs=5, labels=labels_train) -svm.train(feats_train) +Machine svm = create_machine("SVMSGD", C1=0.9, C2=0.9, epochs=5) +svm.train(feats_train, labels_train) RealVector weights = svm.get_real_vector("w") real bias = svm.get_real("bias") diff --git a/examples/meta/src/evaluation/cross_validation.sg.in b/examples/meta/src/evaluation/cross_validation.sg.in index a33b205b1e9..3dd9ead79b2 100644 --- a/examples/meta/src/evaluation/cross_validation.sg.in +++ b/examples/meta/src/evaluation/cross_validation.sg.in @@ -16,7 +16,7 @@ real epsilon = 0.001 #![set_parameters] #![create_instance] -Machine svm = create_machine("LibLinear", labels=labels_train, epsilon=epsilon, C1=C, C2=C, liblinear_solver_type="L2R_L2LOSS_SVC", seed=2) +Machine svm = create_machine("LibLinear", epsilon=epsilon, C1=C, C2=C, liblinear_solver_type="L2R_L2LOSS_SVC", seed=2) #![create_instance] #![create_cross_validation] @@ -32,7 +32,7 @@ real stddev = result.get_real("std_dev") #![evaluate_and_get_result] #![get_results_test_data] -svm.train(features_train) +svm.train(features_train, labels_train) Labels labels_predict = svm.apply(features_test) real accuracy_test = evaluation_criterion.evaluate(labels_predict, labels_test) #![get_results_test_data] @@ -65,7 +65,7 @@ EvaluationResult result_lrr = cross_validation.evaluate() #![evaluate_and_get_result_REGRESSION] #![evaluate_error_REGRESSION] -lrr.train(reg_features_train) +lrr.train(reg_features_train, reg_labels_train) Labels reg_labels_predict = lrr.apply(reg_features_test) real mse = MSE_evaluation.evaluate(reg_labels_predict, reg_labels_test) #![evaluate_error_REGRESSION] diff --git a/examples/meta/src/evaluation/multiclass_ovr.sg.in b/examples/meta/src/evaluation/multiclass_ovr.sg.in index bb1fdd1d00b..ec953373a9c 100644 --- a/examples/meta/src/evaluation/multiclass_ovr.sg.in +++ b/examples/meta/src/evaluation/multiclass_ovr.sg.in @@ -12,7 +12,7 @@ Machine svm= create_machine("MulticlassLibLinear", C=1.0, labels=labels_train) #![train_and_apply] svm.train(feats_train) -Labels labels_predicted = svm.apply() +Labels labels_predicted = svm.apply(feats_train) RealVector labels = labels_predicted.get_real_vector("labels") #![train_and_apply] diff --git a/examples/meta/src/multiclass/linear.sg.in b/examples/meta/src/multiclass/linear.sg.in index a58892de26f..2a291f9f28f 100644 --- a/examples/meta/src/multiclass/linear.sg.in +++ b/examples/meta/src/multiclass/linear.sg.in @@ -19,7 +19,7 @@ MulticlassStrategy strategy=create_multiclass_strategy("MulticlassOneVsOneStrate #![choose_strategy] #![create_instance] -Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier, labels=labels_train) +Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier, labels = labels_train) #![create_instance] #![train_and_apply] diff --git a/examples/meta/src/multiclass/logistic_regression.sg.in b/examples/meta/src/multiclass/logistic_regression.sg.in index 8550ec5d469..618d8b75d7d 100644 --- a/examples/meta/src/multiclass/logistic_regression.sg.in +++ b/examples/meta/src/multiclass/logistic_regression.sg.in @@ -12,7 +12,7 @@ Labels labels_test = create_labels(f_labels_test) #![create_instance] -Machine classifier = create_machine("MulticlassLogisticRegression", m_z=1.0, labels=labels_train) +Machine classifier = create_machine("MulticlassLogisticRegression", m_z=1.0, labels = labels_train) #![create_instance] #![train_and_apply] diff --git a/examples/meta/src/multiclass/multiclassliblinear.sg.in b/examples/meta/src/multiclass/multiclassliblinear.sg.in index 5ce2e770f0c..55c9c19adb4 100644 --- a/examples/meta/src/multiclass/multiclassliblinear.sg.in +++ b/examples/meta/src/multiclass/multiclassliblinear.sg.in @@ -9,7 +9,7 @@ Labels labels_train = create_labels(label_train_multiclass) #![create_features] #![create_machine] -Machine classifier = create_machine("MulticlassLibLinear", C=1.0, labels=labels_train) +Machine classifier = create_machine("MulticlassLibLinear", C=1.0, labels = labels_train) #![create_machine] #![train_and_apply] diff --git a/examples/meta/src/observers/least_angle_regression.sg.in b/examples/meta/src/observers/least_angle_regression.sg.in index 046238dafdf..f368487ab7d 100644 --- a/examples/meta/src/observers/least_angle_regression.sg.in +++ b/examples/meta/src/observers/least_angle_regression.sg.in @@ -22,7 +22,7 @@ Features normalized_features_test = Normalize.transform(pruned_features_test) #![preprocess_features] #![create_instance] -Machine lars = create_machine("LeastAngleRegression", labels=labels_train, lasso=False, max_l1_norm=0.01) +Machine lars = create_machine("LeastAngleRegression", lasso=False, max_l1_norm=0.01) #![create_instance] #![create_observer] @@ -31,7 +31,7 @@ lars.subscribe(logger) #![create_observer] #![train_and_apply] -lars.train(normalized_features_train) +lars.train(normalized_features_train, labels_train) Labels labels_predict = lars.apply(normalized_features_test) #![train_and_apply] diff --git a/examples/meta/src/regression/least_angle_regression.sg.in b/examples/meta/src/regression/least_angle_regression.sg.in index 235373b013b..e47d70fb7c1 100644 --- a/examples/meta/src/regression/least_angle_regression.sg.in +++ b/examples/meta/src/regression/least_angle_regression.sg.in @@ -22,11 +22,11 @@ Features normalized_features_test = Normalize.transform(pruned_features_test) #![preprocess_features] #![create_instance] -Machine lars = create_machine("LeastAngleRegression", labels=labels_train, lasso=False, max_l1_norm=0.01) +Machine lars = create_machine("LeastAngleRegression", lasso=False, max_l1_norm=0.01) #![create_instance] #![train_and_apply] -lars.train(normalized_features_train) +lars.train(normalized_features_train, labels_train) Labels labels_predict = lars.apply(normalized_features_test) #[!extract_w] diff --git a/examples/meta/src/regression/linear_ridge_regression.sg.in b/examples/meta/src/regression/linear_ridge_regression.sg.in index 5be74a891a0..186366bf4ff 100644 --- a/examples/meta/src/regression/linear_ridge_regression.sg.in +++ b/examples/meta/src/regression/linear_ridge_regression.sg.in @@ -11,11 +11,11 @@ Labels labels_test = create_labels(f_labels_test) #![create_features] #![create_instance] -Machine lrr = create_machine("LinearRidgeRegression", tau=0.001, labels=labels_train) +Machine lrr = create_machine("LinearRidgeRegression", tau=0.001) #![create_instance] #![train_and_apply] -lrr.train(features_train) +lrr.train(features_train, labels_train) Labels labels_predict = lrr.apply(features_test) #![train_and_apply] @@ -25,8 +25,8 @@ RealVector w = lrr.get_real_vector("w") #[!extract_w] #[!manual_bias] -Machine lrr2 = create_machine("LinearRidgeRegression", tau=0.001, labels=labels_train, use_bias=False) -lrr2.train(features_train) +Machine lrr2 = create_machine("LinearRidgeRegression", tau=0.001, use_bias=False) +lrr2.train(features_train, labels_train) real my_bias = 0.1 lrr2.put("bias", my_bias) Labels labels_predict2 = lrr2.apply(features_test) diff --git a/examples/undocumented/python/transfer_multitask_clustered_logistic_regression.py b/examples/undocumented/python/transfer_multitask_clustered_logistic_regression.py index b202cd0782b..a84a57534d1 100644 --- a/examples/undocumented/python/transfer_multitask_clustered_logistic_regression.py +++ b/examples/undocumented/python/transfer_multitask_clustered_logistic_regression.py @@ -30,14 +30,14 @@ def transfer_multitask_clustered_logistic_regression (fm_train=traindat,fm_test= task_group.append_task(task_two) task_group.append_task(task_three) - mtlr = sg.MultitaskClusteredLogisticRegression(1.0,100.0,features,labels,task_group,2) + mtlr = sg.MultitaskClusteredLogisticRegression(1.0,100.0,task_group,2) #mtlr.io.set_loglevel(MSG_DEBUG) mtlr.set_tolerance(1e-3) # use 1e-2 tolerance mtlr.set_max_iter(100) - mtlr.train() + mtlr.train(features,labels) mtlr.set_current_task(0) #print mtlr.get_w() - out = mtlr.apply_regression().get("labels") + out = mtlr.apply_regression(features).get("labels") return out diff --git a/examples/undocumented/python/transfer_multitask_l12_logistic_regression.py b/examples/undocumented/python/transfer_multitask_l12_logistic_regression.py index 364721911be..bc1f3c774f0 100644 --- a/examples/undocumented/python/transfer_multitask_l12_logistic_regression.py +++ b/examples/undocumented/python/transfer_multitask_l12_logistic_regression.py @@ -29,12 +29,12 @@ def transfer_multitask_l12_logistic_regression (fm_train=traindat,fm_test=testda task_group.append_task(task_one) task_group.append_task(task_two) - mtlr = MultitaskL12LogisticRegression(0.1,0.1,features,labels,task_group) + mtlr = MultitaskL12LogisticRegression(0.1,0.1,task_group) mtlr.set_tolerance(1e-2) # use 1e-2 tolerance mtlr.set_max_iter(10) - mtlr.train() + mtlr.train(features,labels) mtlr.set_current_task(0) - out = mtlr.apply_regression().get_labels() + out = mtlr.apply_regression(features).get_labels() return out diff --git a/examples/undocumented/python/transfer_multitask_leastsquares_regression.py b/examples/undocumented/python/transfer_multitask_leastsquares_regression.py index ac8518f4b2f..b143eda13c8 100644 --- a/examples/undocumented/python/transfer_multitask_leastsquares_regression.py +++ b/examples/undocumented/python/transfer_multitask_leastsquares_regression.py @@ -29,12 +29,12 @@ def transfer_multitask_leastsquares_regression (fm_train=traindat,fm_test=testda task_group.append_task(task_one) task_group.append_task(task_two) - mtlsr = MultitaskLeastSquaresRegression(0.1,features,labels,task_group) + mtlsr = MultitaskLeastSquaresRegression(0.1,task_group) mtlsr.set_regularization(1) # use regularization ratio mtlsr.set_tolerance(1e-2) # use 1e-2 tolerance - mtlsr.train() + mtlsr.train(features,labels) mtlsr.set_current_task(0) - out = mtlsr.apply_regression().get_labels() + out = mtlsr.apply_regression(features).get_labels() return out if __name__=='__main__': diff --git a/examples/undocumented/python/transfer_multitask_logistic_regression.py b/examples/undocumented/python/transfer_multitask_logistic_regression.py index 24991dffbe8..b204f20ffb6 100644 --- a/examples/undocumented/python/transfer_multitask_logistic_regression.py +++ b/examples/undocumented/python/transfer_multitask_logistic_regression.py @@ -29,12 +29,12 @@ def transfer_multitask_logistic_regression (fm_train=traindat,fm_test=testdat,la task_group.append_task(task_one) task_group.append_task(task_two) - mtlr = MultitaskLogisticRegression(0.1,features,labels,task_group) + mtlr = MultitaskLogisticRegression(0.1,task_group) mtlr.set_regularization(1) # use regularization ratio mtlr.set_tolerance(1e-2) # use 1e-2 tolerance - mtlr.train() + mtlr.train(features,labels) mtlr.set_current_task(0) - out = mtlr.apply().get("labels") + out = mtlr.apply(features).get("labels") return out diff --git a/examples/undocumented/python/transfer_multitask_trace_logistic_regression.py b/examples/undocumented/python/transfer_multitask_trace_logistic_regression.py index 5f8a1c99fdb..00a296ff137 100644 --- a/examples/undocumented/python/transfer_multitask_trace_logistic_regression.py +++ b/examples/undocumented/python/transfer_multitask_trace_logistic_regression.py @@ -29,12 +29,12 @@ def transfer_multitask_trace_logistic_regression (fm_train=traindat,fm_test=test task_group.append_task(task_one) task_group.append_task(task_two) - mtlr = MultitaskTraceLogisticRegression(0.1,features,labels,task_group) + mtlr = MultitaskTraceLogisticRegression(0.1,task_group) mtlr.set_tolerance(1e-2) # use 1e-2 tolerance mtlr.set_max_iter(10) - mtlr.train() + mtlr.train(features,labels) mtlr.set_current_task(0) - out = mtlr.apply_regression().get_labels() + out = mtlr.apply_regression(features).get_labels() return out diff --git a/src/gpl b/src/gpl index 8e361a17b48..0b5edd5979d 160000 --- a/src/gpl +++ b/src/gpl @@ -1 +1 @@ -Subproject commit 8e361a17b48c17ebd6a0255a4f53a19fd84ea27f +Subproject commit 0b5edd5979d24b79e067756f2eb940ef5e403161 diff --git a/src/shogun/classifier/AveragedPerceptron.cpp b/src/shogun/classifier/AveragedPerceptron.cpp index b2fcc9bc577..ae13718c544 100644 --- a/src/shogun/classifier/AveragedPerceptron.cpp +++ b/src/shogun/classifier/AveragedPerceptron.cpp @@ -40,22 +40,9 @@ void AveragedPerceptron::init() ParameterProperties::MODEL) } -void AveragedPerceptron::init_model(const std::shared_ptr data) +void AveragedPerceptron::init_model(const std::shared_ptr& features) { - ASSERT(m_labels) - if (data) - { - if (!data->has_property(FP_DOT)) - error("Specified features are not of type CDotFeatures"); - set_features(std::static_pointer_cast(data)); - } - ASSERT(features) - - SGVector train_labels = binary_labels(m_labels)->get_int_labels(); int32_t num_feat = features->get_dim_feature_space(); - int32_t num_vec = features->get_num_vectors(); - ASSERT(num_vec == train_labels.vlen) - SGVector w(num_feat); cached_w = SGVector(num_feat); // start with uniform w, bias=0, tmp_bias=0 @@ -66,13 +53,13 @@ void AveragedPerceptron::init_model(const std::shared_ptr data) set_w(w); } -void AveragedPerceptron::iteration() +void AveragedPerceptron::iteration( + const std::shared_ptr& features, const std::shared_ptr& labs) { bool converged = true; SGVector w = get_w(); - auto labels = binary_labels(m_labels)->get_int_labels(); - + auto labels = binary_labels(labs)->get_int_labels(); int32_t num_vec = features->get_num_vectors(); // this assumes that m_current_iteration starts at 0 int32_t num_prev_weights = num_vec * m_current_iteration + 1; diff --git a/src/shogun/classifier/AveragedPerceptron.h b/src/shogun/classifier/AveragedPerceptron.h index 08d91866985..8307d9ff601 100644 --- a/src/shogun/classifier/AveragedPerceptron.h +++ b/src/shogun/classifier/AveragedPerceptron.h @@ -69,8 +69,10 @@ namespace shogun /** registers and initializes parameters */ void init(); - void init_model(std::shared_ptr data) override; - void iteration() override; + void init_model(const std::shared_ptr& data) override; + void iteration( + const std::shared_ptr& data, + const std::shared_ptr& labs) override; protected: /** learning rate */ diff --git a/src/shogun/classifier/LDA.cpp b/src/shogun/classifier/LDA.cpp index 2da9f9b940f..9a4c6b65533 100644 --- a/src/shogun/classifier/LDA.cpp +++ b/src/shogun/classifier/LDA.cpp @@ -31,19 +31,6 @@ LDA::LDA(float64_t gamma, ELDAMethod method, bool bdc_svd) m_bdc_svd = bdc_svd; } -LDA::LDA( - float64_t gamma, const std::shared_ptr>& traindat, std::shared_ptr trainlab, - ELDAMethod method, bool bdc_svd) - : DenseRealDispatch(), m_gamma(gamma) -{ - init(); - - features = traindat; - m_labels = std::move(trainlab); - m_method = method; - m_gamma = gamma; - m_bdc_svd = bdc_svd; -} void LDA::init() { @@ -63,12 +50,10 @@ void LDA::init() SG_OPTIONS(AUTO_LDA, SVD_LDA, FLD_LDA)) } -LDA::~LDA() -{ -} - template -bool LDA::train_machine_templated(const std::shared_ptr>& data) +bool LDA::train_machine_templated( + const std::shared_ptr>& data, + const std::shared_ptr& labs) { index_t num_feat = data->get_num_features(); index_t num_vec = data->get_num_vectors(); @@ -76,15 +61,17 @@ bool LDA::train_machine_templated(const std::shared_ptr>& data bool lda_more_efficient = (m_method == AUTO_LDA && num_vec <= num_feat); if (m_method == SVD_LDA || lda_more_efficient) - return solver_svd(data); + return solver_svd(data, labs); else - return solver_classic(data); + return solver_classic(data, labs); } template -bool LDA::solver_svd(std::shared_ptr> data) +bool LDA::solver_svd( + const std::shared_ptr>& data, + const std::shared_ptr& labs) { - auto labels = multiclass_labels(m_labels); + auto labels = multiclass_labels(labs); require( labels->get_num_classes() == 2, "Number of classes ({}) must be 2", labels->get_num_classes()); @@ -118,9 +105,11 @@ bool LDA::solver_svd(std::shared_ptr> data) } template -bool LDA::solver_classic(std::shared_ptr> data) +bool LDA::solver_classic( + const std::shared_ptr>& data, + const std::shared_ptr& labs) { - auto labels = multiclass_labels(m_labels); + auto labels = multiclass_labels(labs); require( labels->get_num_classes() == 2, "Number of classes ({}) must be 2", labels->get_num_classes()); diff --git a/src/shogun/classifier/LDA.h b/src/shogun/classifier/LDA.h index 1618be76cdc..073feb13385 100644 --- a/src/shogun/classifier/LDA.h +++ b/src/shogun/classifier/LDA.h @@ -112,25 +112,6 @@ class LDA : public DenseRealDispatch LDA( float64_t gamma = 0, ELDAMethod method = AUTO_LDA, bool bdc_svd = true); - - /** constructor - * - * @param gamma gamma - * @param traindat training features - * @param trainlab labels for training features - * @param method LDA using Fisher's algorithm or Singular Value - * Decomposition : ::FLD_LDA/::SVD_LDA/::AUTO_LDA[default] - * @param bdc_svd when using SVD solver switch between - * Bidiagonal Divide and Conquer algorithm (BDC-SVD) and - * Jacobi's algorithm, for the differences @see linalg::SVDAlgorithm. - * [default = BDC-SVD] - */ - LDA( - float64_t gamma, const std::shared_ptr>& traindat, - std::shared_ptr trainlab, ELDAMethod method = AUTO_LDA, - bool bdc_svd = true); - ~LDA() override; - /** get classifier type * * @return classifier type LDA @@ -152,9 +133,12 @@ class LDA : public DenseRealDispatch * * @return whether training was successful */ - template ::value>> - bool train_machine_templated(const std::shared_ptr>& data); + template < + typename ST, typename U = typename std::enable_if_t< + std::is_floating_point::value>> + bool train_machine_templated( + const std::shared_ptr>& data, + const std::shared_ptr& labs); /** * Train the machine with the svd-based solver (@see CFisherLDA). @@ -162,7 +146,9 @@ class LDA : public DenseRealDispatch * @param labels labels for training data */ template - bool solver_svd(std::shared_ptr> data); + bool solver_svd( + const std::shared_ptr>& data, + const std::shared_ptr& labs); /** * Train the machine with the classic method based on the cholesky @@ -171,7 +157,9 @@ class LDA : public DenseRealDispatch * @param labels labels for training data */ template - bool solver_classic(std::shared_ptr> data); + bool solver_classic( + const std::shared_ptr>& data, + const std::shared_ptr& labs); protected: diff --git a/src/shogun/classifier/Perceptron.cpp b/src/shogun/classifier/Perceptron.cpp index 72b0118cfd0..cd8c66340e9 100644 --- a/src/shogun/classifier/Perceptron.cpp +++ b/src/shogun/classifier/Perceptron.cpp @@ -34,15 +34,8 @@ Perceptron::~Perceptron() { } -void Perceptron::init_model(const std::shared_ptr data) +void Perceptron::init_model(const std::shared_ptr& features) { - if (data) - { - if (!data->has_property(FP_DOT)) - error("Specified features are not of type CDotFeatures"); - set_features(std::static_pointer_cast(data)); - } - int32_t num_feat = features->get_dim_feature_space(); SGVector w; @@ -57,13 +50,13 @@ void Perceptron::init_model(const std::shared_ptr data) } } -void Perceptron::iteration() +void Perceptron::iteration( + const std::shared_ptr& features, const std::shared_ptr& labs) { bool converged = true; SGVector w = get_w(); - auto labels = binary_labels(m_labels)->get_int_labels(); - + auto labels = labs->as()->get_int_labels(); for (const auto& [v, true_label] : zip_iterator(DotIterator(features), labels)) { const auto predicted_label = v.dot(w) + bias; diff --git a/src/shogun/classifier/Perceptron.h b/src/shogun/classifier/Perceptron.h index 7c74984d939..4ca077c8855 100644 --- a/src/shogun/classifier/Perceptron.h +++ b/src/shogun/classifier/Perceptron.h @@ -59,8 +59,10 @@ class Perceptron : public IterativeMachine const char* get_name() const override { return "Perceptron"; } protected: - void init_model(std::shared_ptr data) override; - void iteration() override; + void init_model(const std::shared_ptr& data) override; + void iteration( + const std::shared_ptr& data, + const std::shared_ptr& labs) override; protected: /** learning rate */ diff --git a/src/shogun/classifier/svm/LibLinear.cpp b/src/shogun/classifier/svm/LibLinear.cpp index d6793ed17f9..53648c0b964 100644 --- a/src/shogun/classifier/svm/LibLinear.cpp +++ b/src/shogun/classifier/svm/LibLinear.cpp @@ -35,16 +35,11 @@ LibLinear::LibLinear(LIBLINEAR_SOLVER_TYPE l) : RandomMixin() set_liblinear_solver_type(l); } -LibLinear::LibLinear(float64_t C, std::shared_ptr traindat, std::shared_ptr trainlab) - : RandomMixin() +LibLinear::LibLinear(float64_t C) : RandomMixin() { init(); set_C(C, C); set_bias_enabled(true); - - set_features(std::move(traindat)); - set_labels(std::move(trainlab)); - init_linear_term(); } void LibLinear::init() @@ -73,22 +68,13 @@ LibLinear::~LibLinear() { } -bool LibLinear::train_machine(std::shared_ptr data) +bool LibLinear::train_machine( + const std::shared_ptr& features, const std::shared_ptr& labs) { - ASSERT(m_labels) - init_linear_term(); - - if (data) - { - if (!data->has_property(FP_DOT)) - error("Specified features are not of type CDotFeatures"); + init_linear_term(labs); - set_features(std::static_pointer_cast(data)); - } - ASSERT(features) - - int32_t num_train_labels = m_labels->get_num_labels(); + int32_t num_train_labels = labs->get_num_labels(); int32_t num_feat = features->get_dim_feature_space(); int32_t num_vec = features->get_num_vectors(); @@ -144,7 +130,7 @@ bool LibLinear::train_machine(std::shared_ptr data) double Cp = get_C1(); double Cn = get_C2(); - auto labels = binary_labels(m_labels); + auto labels = binary_labels(labs); for (int32_t i = 0; i < prob.l; i++) { prob.y[i] = labels->get_int_label(i); @@ -1396,12 +1382,10 @@ SGVector LibLinear::get_linear_term() return m_linear_term; } -void LibLinear::init_linear_term() +void LibLinear::init_linear_term(const std::shared_ptr& labs) { - if (!m_labels) - error("Please assign labels first!"); - m_linear_term = SGVector(m_labels->get_num_labels()); + m_linear_term = SGVector(labs->get_num_labels()); SGVector::fill_vector( m_linear_term.vector, m_linear_term.vlen, -1.0); } diff --git a/src/shogun/classifier/svm/LibLinear.h b/src/shogun/classifier/svm/LibLinear.h index 12ec174aa65..4bfa01de6fb 100644 --- a/src/shogun/classifier/svm/LibLinear.h +++ b/src/shogun/classifier/svm/LibLinear.h @@ -75,10 +75,8 @@ namespace shogun /** constructor (using L2R_L1LOSS_SVC_DUAL as default) * * @param C constant C - * @param traindat training features - * @param trainlab training labels */ - LibLinear(float64_t C, std::shared_ptr traindat, std::shared_ptr trainlab); + LibLinear(float64_t C); /** destructor */ ~LibLinear() override; @@ -199,7 +197,7 @@ namespace shogun SGVector get_linear_term(); /** set the linear term for qp */ - void init_linear_term(); + void init_linear_term(const std::shared_ptr&); /** check if linear_term been inited * @return if linear_term been inited @@ -221,7 +219,9 @@ namespace shogun * * @return whether training was successful */ - bool train_machine(std::shared_ptr data = NULL) override; + bool train_machine( + const std::shared_ptr& data, + const std::shared_ptr& labs) override; private: /** set up parameters */ diff --git a/src/shogun/classifier/svm/NewtonSVM.cpp b/src/shogun/classifier/svm/NewtonSVM.cpp index bfb0c65e42a..a90dae08d9e 100644 --- a/src/shogun/classifier/svm/NewtonSVM.cpp +++ b/src/shogun/classifier/svm/NewtonSVM.cpp @@ -30,8 +30,7 @@ NewtonSVM::NewtonSVM() : IterativeMachine() t = 0; } -NewtonSVM::NewtonSVM( - float64_t c, std::shared_ptr traindat, std::shared_ptr trainlab, int32_t itr) +NewtonSVM::NewtonSVM(float64_t c, int32_t itr) : IterativeMachine() { lambda=1/c; @@ -39,8 +38,6 @@ NewtonSVM::NewtonSVM( prec=1e-6; C=c; t = 0; - set_features(std::move(traindat)); - set_labels(std::move(trainlab)); } @@ -48,18 +45,8 @@ NewtonSVM::~NewtonSVM() { } -void NewtonSVM::init_model(const std::shared_ptr data) +void NewtonSVM::init_model(const std::shared_ptr& features) { - if (data) - { - if (!data->has_property(FP_DOT)) - error("Specified features are not of type CDotFeatures"); - set_features(std::static_pointer_cast(data)); - } - - ASSERT(features) - - SGVector train_labels = binary_labels(m_labels)->get_labels(); int32_t num_feat=features->get_dim_feature_space(); int32_t num_vec=features->get_num_vectors(); @@ -67,8 +54,6 @@ void NewtonSVM::init_model(const std::shared_ptr data) x_n=num_vec; x_d=num_feat; - ASSERT(num_vec==train_labels.vlen) - SGVector weights(x_d); set_w(weights); out = SGVector(x_n); @@ -81,9 +66,10 @@ void NewtonSVM::init_model(const std::shared_ptr data) grad.set_const(0.0); } -void NewtonSVM::iteration() +void NewtonSVM::iteration( + const std::shared_ptr& features, const std::shared_ptr& labs) { - obj_fun_linear(); + obj_fun_linear(features, labs); SGVector weights = get_w(); SGVector sgv; @@ -132,7 +118,7 @@ void NewtonSVM::iteration() for (int32_t i = 0; i < x_d + 1; i++) step[i] = -s2[i]; - line_search_linear(step); + line_search_linear(step, features, labs); SGVector tmp_step(step.data(), x_d, false); linalg::add(weights, tmp_step, weights, 1.0, t); @@ -143,9 +129,11 @@ void NewtonSVM::iteration() m_complete = true; } -void NewtonSVM::line_search_linear(const SGVector& d) +void NewtonSVM::line_search_linear( + const SGVector& d, const std::shared_ptr& features, + const std::shared_ptr& labs) { - SGVector Y = binary_labels(m_labels)->get_labels(); + SGVector Y = binary_labels(labs)->get_labels(); SGVector outz(x_n); SGVector temp1(x_n); SGVector temp1forout(x_n); @@ -213,11 +201,11 @@ void NewtonSVM::line_search_linear(const SGVector& d) out = outz.clone(); } -void NewtonSVM::obj_fun_linear() +void NewtonSVM::obj_fun_linear( + const std::shared_ptr& features, const std::shared_ptr& labs) { SGVector weights = get_w(); - SGVector v = binary_labels(m_labels)->get_labels(); - + SGVector v = binary_labels(labs)->get_labels(); for (int32_t i=0; i /** constructor * @param C constant C * @param itr constant no of iterations - * @param traindat training features - * @param trainlab labels for features */ - NewtonSVM(float64_t C, std::shared_ptr traindat, std::shared_ptr trainlab, int32_t itr=20); + NewtonSVM(float64_t C, int32_t itr = 20); ~NewtonSVM() override; @@ -93,13 +91,19 @@ class NewtonSVM : public IterativeMachine const char* get_name() const override { return "NewtonSVM"; } protected: - void init_model(std::shared_ptr data) override; - void iteration() override; + void init_model(const std::shared_ptr& data) override; + virtual void iteration( + const std::shared_ptr& data, + const std::shared_ptr& labs) override; private: - void obj_fun_linear(); + void obj_fun_linear( + const std::shared_ptr& data, + const std::shared_ptr& labs); - void line_search_linear(const SGVector& d); + void line_search_linear( + const SGVector& d, const std::shared_ptr& data, + const std::shared_ptr& labs); protected: /** lambda=1/C */ diff --git a/src/shogun/classifier/svm/SGDQN.cpp b/src/shogun/classifier/svm/SGDQN.cpp index 6be3b020730..912b9f36696 100644 --- a/src/shogun/classifier/svm/SGDQN.cpp +++ b/src/shogun/classifier/svm/SGDQN.cpp @@ -31,16 +31,6 @@ SGDQN::SGDQN(float64_t C) C2=C; } -SGDQN::SGDQN(float64_t C, std::shared_ptr traindat, std::shared_ptr trainlab) -: LinearMachine() -{ - init(); - C1=C; - C2=C; - - set_features(std::move(traindat)); - set_labels(std::move(trainlab)); -} SGDQN::~SGDQN() { @@ -77,27 +67,14 @@ void SGDQN::combine_and_clip(float64_t* Bc,float64_t* B,int32_t dim,float64_t c1 } } } - -bool SGDQN::train(std::shared_ptr data) +bool SGDQN::train_machine( + const std::shared_ptr& features, const std::shared_ptr& labs) { - ASSERT(m_labels) - ASSERT(m_labels->get_label_type() == LT_BINARY) + const auto binary_labels = labs->as(); - if (data) - { - if (!data->has_property(FP_DOT)) - error("Specified features are not of type CDotFeatures"); - set_features(std::static_pointer_cast(data)); - } - - ASSERT(features) - - int32_t num_train_labels=m_labels->get_num_labels(); - int32_t num_vec=features->get_num_vectors(); - - ASSERT(num_vec==num_train_labels) - ASSERT(num_vec>0) + int32_t num_train_labels = binary_labels->get_num_labels(); + int32_t num_vec = features->get_num_vectors(); SGVector w(features->get_dim_feature_space()); w.zero(); @@ -122,7 +99,7 @@ bool SGDQN::train(std::shared_ptr data) float64_t* B=SG_MALLOC(float64_t, w.vlen); //Calibrate - calibrate(); + calibrate(features); io::info("Training on {} vectors", num_vec); @@ -131,7 +108,6 @@ bool SGDQN::train(std::shared_ptr data) if ((loss_type == L_LOGLOSS) || (loss_type == L_LOGLOSSMARGIN)) is_log_loss = true; - auto binary_labels = std::static_pointer_cast(m_labels); for (auto e : SG_PROGRESS(range(epochs))) { COMPUTATION_CONTROLLERS @@ -192,11 +168,8 @@ bool SGDQN::train(std::shared_ptr data) return true; } - - -void SGDQN::calibrate() +void SGDQN::calibrate(const std::shared_ptr& features) { - ASSERT(features) int32_t num_vec=features->get_num_vectors(); int32_t c_dim=features->get_dim_feature_space(); diff --git a/src/shogun/classifier/svm/SGDQN.h b/src/shogun/classifier/svm/SGDQN.h index 904980f999f..7e41fa99ac9 100644 --- a/src/shogun/classifier/svm/SGDQN.h +++ b/src/shogun/classifier/svm/SGDQN.h @@ -35,16 +35,6 @@ class SGDQN : public LinearMachine */ SGDQN(float64_t C); - /** constructor - * - * @param C constant C - * @param traindat training features - * @param trainlab labels for training features - */ - SGDQN( - float64_t C, std::shared_ptr traindat, - std::shared_ptr trainlab); - ~SGDQN() override; /** get classifier type @@ -52,17 +42,6 @@ class SGDQN : public LinearMachine * @return classifier type SVMSGDQN */ EMachineType get_classifier_type() override { return CT_SGDQN; } - - /** train classifier - * - * @param data training data (parameter can be avoided if distance or - * kernel-based classifiers are used and distance/kernels are - * initialized with train data) - * - * @return whether training was successful - */ - bool train(std::shared_ptr data=NULL) override; - /** set C * * @param c_neg new C constant for negatively labeled examples @@ -117,8 +96,12 @@ class SGDQN : public LinearMachine const char* get_name() const override { return "SGDQN"; } protected: + bool train_machine( + const std::shared_ptr&, + const std::shared_ptr&) override; + /** calibrate */ - void calibrate(); + void calibrate(const std::shared_ptr& features); private: void init(); diff --git a/src/shogun/classifier/svm/SVMOcas.cpp b/src/shogun/classifier/svm/SVMOcas.cpp index adf241c843d..8715d6477f5 100644 --- a/src/shogun/classifier/svm/SVMOcas.cpp +++ b/src/shogun/classifier/svm/SVMOcas.cpp @@ -33,16 +33,12 @@ SVMOcas::SVMOcas(E_SVM_TYPE type) method=type; } -SVMOcas::SVMOcas( - float64_t C, const std::shared_ptr& traindat, std::shared_ptr trainlab) -: LinearMachine() +SVMOcas::SVMOcas(float64_t C) : LinearMachine() { init(); C1=C; C2=C; - set_features(std::dynamic_pointer_cast(traindat)); - set_labels(std::move(trainlab)); } @@ -50,24 +46,16 @@ SVMOcas::~SVMOcas() { } -bool SVMOcas::train_machine(std::shared_ptr data) +bool SVMOcas::train_machine( + const std::shared_ptr& features, const std::shared_ptr& labs) { io::info("C={}, epsilon={}, bufsize={}", get_C1(), get_epsilon(), bufsize); SG_DEBUG("use_bias = {}", get_bias_enabled()) - ASSERT(m_labels) - ASSERT(m_labels->get_label_type() == LT_BINARY) - if (data) - { - if (!data->has_property(FP_DOT)) - error("Specified features are not of type CDotFeatures"); - set_features(std::static_pointer_cast(data)); - } - ASSERT(features) - + m_features = features; int32_t num_vec=features->get_num_vectors(); lab = SGVector(num_vec); - auto labels = binary_labels(m_labels); + auto labels = binary_labels(labs); for (int32_t i=0; iget_label(i); @@ -185,7 +173,7 @@ int SVMOcas::add_new_cut( uint32_t nSel, void* ptr) { auto o = (SVMOcas*)ptr; - auto f = o->features; + auto f = o->m_features; uint32_t nDim=(uint32_t) o->current_w.vlen; float64_t* y = o->lab.vector; @@ -270,7 +258,7 @@ int SVMOcas::sort(float64_t* vals, float64_t* data, uint32_t size) int SVMOcas::compute_output(float64_t *output, void* ptr) { auto o = (SVMOcas*)ptr; - auto f=o->features; + auto f = o->m_features; int32_t nData=f->get_num_vectors(); float64_t* y = o->lab.vector; diff --git a/src/shogun/classifier/svm/SVMOcas.h b/src/shogun/classifier/svm/SVMOcas.h index 8644350c8a2..8975bf9dc7a 100644 --- a/src/shogun/classifier/svm/SVMOcas.h +++ b/src/shogun/classifier/svm/SVMOcas.h @@ -47,12 +47,8 @@ class SVMOcas : public LinearMachine /** constructor * * @param C constant C - * @param traindat training features - * @param trainlab labels for training features */ - SVMOcas( - float64_t C, const std::shared_ptr& traindat, - std::shared_ptr trainlab); + SVMOcas(float64_t C); ~SVMOcas() override; /** get classifier type @@ -187,7 +183,9 @@ class SVMOcas : public LinearMachine * * @return whether training was successful */ - bool train_machine(std::shared_ptr data=NULL) override; + bool train_machine( + const std::shared_ptr& data, + const std::shared_ptr& labs) override; private: void init(); @@ -229,6 +227,8 @@ class SVMOcas : public LinearMachine /** primal objective */ float64_t primal_objective; + + std::shared_ptr m_features; }; } #endif diff --git a/src/shogun/evaluation/CrossValidation.cpp b/src/shogun/evaluation/CrossValidation.cpp index baaaf7621b3..839d859ef85 100644 --- a/src/shogun/evaluation/CrossValidation.cpp +++ b/src/shogun/evaluation/CrossValidation.cpp @@ -116,8 +116,15 @@ float64_t CrossValidation::evaluate_one_run(int64_t index) const auto evaluation_criterion = make_clone(m_evaluation_criterion); - machine->set_labels(labels_train); - machine->train(features_train); + try + { + machine->train(features_train, labels_train); + } + catch(const std::exception& e){ + machine->set_labels(labels_train); + machine->train(features_train); + } + auto result_labels = machine->apply(features_test); diff --git a/src/shogun/latent/LatentSVM.cpp b/src/shogun/latent/LatentSVM.cpp index f6f857ef111..e334b246a0b 100644 --- a/src/shogun/latent/LatentSVM.cpp +++ b/src/shogun/latent/LatentSVM.cpp @@ -59,13 +59,13 @@ std::shared_ptr LatentSVM::apply_latent() float64_t LatentSVM::do_inner_loop(float64_t cooling_eps) { auto ys = m_model->get_labels()->get_labels(); - auto feats = (m_model->get_caching() ? + std::shared_ptr dot_feats = (m_model->get_caching() ? m_model->get_cached_psi_features() : m_model->get_psi_feature_vectors()); - SVMOcas svm(m_C, feats, ys); + const auto feats = std::static_pointer_cast(dot_feats); + SVMOcas svm(m_C); svm.set_epsilon(cooling_eps); - svm.train(); - + svm.train(feats, ys); /* copy the resulting w */ set_w(svm.get_w().clone()); diff --git a/src/shogun/machine/DirectorLinearMachine.h b/src/shogun/machine/DirectorLinearMachine.h index a702e4da564..e05cb45d86c 100644 --- a/src/shogun/machine/DirectorLinearMachine.h +++ b/src/shogun/machine/DirectorLinearMachine.h @@ -36,42 +36,12 @@ IGNORE_IN_CLASSLIST class DirectorLinearMachine : public LinearMachine } - /** train machine - * - * @param data training data (parameter can be avoided if distance or - * kernel-based classifiers are used and distance/kernels are - * initialized with train data). - * - * @return whether training was successful - */ - virtual bool train(std::shared_ptr data=NULL) - { - return LinearMachine::train(data); - } - virtual bool train_function(std::shared_ptr data=NULL) { error("Train function of Director Linear Machine needs to be overridden."); return false; } - /** set features - * - * @param feat features to set - */ - virtual void set_features(std::shared_ptr feat) - { - LinearMachine::set_features(feat); - } - - /** get features - * - * @return features - */ - virtual std::shared_ptr get_features() - { - return LinearMachine::get_features(); - } /** apply machine to data * if data is not specified apply to the current features @@ -99,9 +69,9 @@ IGNORE_IN_CLASSLIST class DirectorLinearMachine : public LinearMachine /** apply machine to data in means of multiclass classification problem */ using LinearMachine::apply_multiclass; - virtual float64_t apply_one(int32_t vec_idx) + virtual float64_t apply_one(const std::shared_ptr& features, int32_t vec_idx) { - return LinearMachine::apply_one(vec_idx); + return LinearMachine::apply_one(features, vec_idx); } /** set labels @@ -143,13 +113,12 @@ IGNORE_IN_CLASSLIST class DirectorLinearMachine : public LinearMachine * kernel-based classifiers are used and distance/kernels are * initialized with train data) * - * NOT IMPLEMENTED! - * + * NOT IMPLEMENTED! * @return whether training was successful */ - virtual bool train_machine(std::shared_ptr data=NULL) + bool train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) override { - return train_function(data); + return LinearMachine::train_machine(data, labs); } }; diff --git a/src/shogun/machine/FeatureDispatchCRTP.h b/src/shogun/machine/FeatureDispatchCRTP.h index 591ee7d5459..2c2598f2c64 100644 --- a/src/shogun/machine/FeatureDispatchCRTP.h +++ b/src/shogun/machine/FeatureDispatchCRTP.h @@ -33,21 +33,27 @@ namespace shogun } protected: - bool train_dense(std::shared_ptr data) override + bool train_dense( + const std::shared_ptr& data, + const std::shared_ptr& labs) override { auto* this_casted = static_cast(this); switch (data->get_feature_type()) { case F_DREAL: return this_casted->template train_machine_templated( - std::dynamic_pointer_cast>(data)); + std::dynamic_pointer_cast>(data), + labs); case F_SHORTREAL: return this_casted->template train_machine_templated( - std::dynamic_pointer_cast>(data)); + std::dynamic_pointer_cast>(data), + labs); case F_LONGREAL: return this_casted ->template train_machine_templated( - std::dynamic_pointer_cast>(data)); + std::dynamic_pointer_cast>( + data), + labs); default: error( "Training with {} of provided type {} is not " @@ -83,20 +89,22 @@ namespace shogun } protected: - virtual bool train_string(std::shared_ptr data) + virtual bool train_string( + const std::shared_ptr& data, + const std::shared_ptr& labs) { auto this_casted = this->template as

(); switch (data->get_feature_type()) { case F_BYTE: return this_casted->template train_machine_templated( - data->as>()); + data->as>(), labs); case F_CHAR: return this_casted->template train_machine_templated( - data->as>()); + data->as>(), labs); case F_WORD: return this_casted->template train_machine_templated( - data->as>()); + data->as>(), labs); default: error( "Training with {} of provided type {} is " diff --git a/src/shogun/machine/IterativeMachine.h b/src/shogun/machine/IterativeMachine.h index 393ce7512c3..4b8f66c243a 100644 --- a/src/shogun/machine/IterativeMachine.h +++ b/src/shogun/machine/IterativeMachine.h @@ -56,7 +56,9 @@ namespace shogun return m_complete; } - bool continue_train() override + bool continue_train( + const std::shared_ptr& data, + const std::shared_ptr& labs) override { this->reset_computation_variables(); //this->put("features", m_continue_features); @@ -65,7 +67,7 @@ namespace shogun while (m_current_iteration < m_max_iterations && !m_complete) { COMPUTATION_CONTROLLERS - iteration(); + iteration(data, labs); m_current_iteration++; pb.print_progress(); } @@ -92,28 +94,27 @@ namespace shogun } protected: - bool train_machine(std::shared_ptr data = NULL) override + bool train_machine( + const std::shared_ptr& data, + const std::shared_ptr& lab) override { - if (data) - { - - - m_continue_features = data; - } + m_continue_features = data; m_current_iteration = 0; m_complete = false; init_model(data); - return continue_train(); + return continue_train(data, lab); } /** To be overloaded by sublcasses to implement custom single * iterations of training loop. */ - virtual void iteration() = 0; + virtual void iteration( + const std::shared_ptr& data, + const std::shared_ptr& labs) = 0; /** To be overloaded in subclasses to initialize the model for training */ - virtual void init_model(const std::shared_ptr data = NULL) = 0; + virtual void init_model(const std::shared_ptr& data) = 0; /** Can be overloaded in subclasses to show more information * and/or clean up states diff --git a/src/shogun/machine/LinearMachine.cpp b/src/shogun/machine/LinearMachine.cpp index e9cc65e6362..f236230864a 100644 --- a/src/shogun/machine/LinearMachine.cpp +++ b/src/shogun/machine/LinearMachine.cpp @@ -33,13 +33,8 @@ LinearMachine::LinearMachine(const std::shared_ptr& machine) : Ma void LinearMachine::init() { - bias = 0; - features = NULL; - SG_ADD(&m_w, "w", "Parameter vector w.", ParameterProperties::MODEL); SG_ADD(&bias, "bias", "Bias b.", ParameterProperties::MODEL); - SG_ADD( - (std::shared_ptr*)&features, "features", "Feature object."); } @@ -48,7 +43,8 @@ LinearMachine::~LinearMachine() } -float64_t LinearMachine::apply_one(int32_t vec_idx) +float64_t LinearMachine::apply_one( + const std::shared_ptr& features, int32_t vec_idx) { return features->dot(vec_idx, m_w) + bias; } @@ -67,20 +63,11 @@ std::shared_ptr LinearMachine::apply_binary(std::shared_ptr LinearMachine::apply_get_outputs(std::shared_ptr data) { - if (data) - { - if (!data->has_property(FP_DOT)) - error("Specified features are not of type CDotFeatures"); - - set_features(std::static_pointer_cast(data)); - } - - if (!features) - return SGVector(); - + const auto features = data->as(); int32_t num=features->get_num_vectors(); - ASSERT(num>0) - ASSERT(m_w.vlen==features->get_dim_feature_space()) + require( + m_w.vlen == features->get_dim_feature_space(), + "Fetures expected to have {} dimentions", m_w.vlen); SGVector out(num); features->dense_dot_range(out.vector, 0, num, NULL, m_w.vector, m_w.vlen, bias); return out; @@ -106,16 +93,4 @@ float64_t LinearMachine::get_bias() const return bias; } -void LinearMachine::set_features(std::shared_ptr feat) -{ - - - features=std::move(feat); -} - -std::shared_ptr LinearMachine::get_features() -{ - - return features; -} diff --git a/src/shogun/machine/LinearMachine.h b/src/shogun/machine/LinearMachine.h index 3d4c51185ea..5d903be4ede 100644 --- a/src/shogun/machine/LinearMachine.h +++ b/src/shogun/machine/LinearMachine.h @@ -15,6 +15,7 @@ #include #include #include +#include namespace shogun @@ -95,19 +96,14 @@ class LinearMachine : public Machine */ virtual float64_t get_bias() const; - /** set features - * - * @param feat features to set - */ - virtual void set_features(std::shared_ptr feat); - /** apply linear machine to data * for binary classification problem * * @param data (test)data to be classified * @return classified labels */ - std::shared_ptr apply_binary(std::shared_ptr data=NULL) override; + std::shared_ptr + apply_binary(std::shared_ptr data) override; /** apply linear machine to data * for regression problem @@ -115,16 +111,12 @@ class LinearMachine : public Machine * @param data (test)data to be classified * @return classified labels */ - std::shared_ptr apply_regression(std::shared_ptr data=NULL) override; + std::shared_ptr + apply_regression(std::shared_ptr data) override; /** applies to one vector */ - float64_t apply_one(int32_t vec_idx) override; - - /** get features - * - * @return features - */ - virtual std::shared_ptr get_features(); + float64_t apply_one( + const std::shared_ptr& features, int32_t vec_idx) override; /** Returns the name of the SGSerializable instance. It MUST BE * the CLASS NAME without the prefixed `C'. @@ -142,6 +134,17 @@ class LinearMachine : public Machine */ virtual SGVector apply_get_outputs(std::shared_ptr data); + bool train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) final + { + const auto dot_feat = data->as(); + return train_machine(dot_feat, labs); + } + + virtual bool train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) + { + not_implemented(SOURCE_LOCATION); + return false; + } private: void init(); @@ -151,10 +154,7 @@ class LinearMachine : public Machine SGVector m_w; /** bias */ - float64_t bias; - - /** features */ - std::shared_ptr features; + float64_t bias = 0.0; }; } #endif diff --git a/src/shogun/machine/LinearMulticlassMachine.h b/src/shogun/machine/LinearMulticlassMachine.h index 27ae4d36aeb..ce7ff009cb3 100644 --- a/src/shogun/machine/LinearMulticlassMachine.h +++ b/src/shogun/machine/LinearMulticlassMachine.h @@ -64,11 +64,6 @@ class LinearMulticlassMachine : public MulticlassMachine void set_features(std::shared_ptr f) { m_features = f; - for (auto m: m_machines) - { - auto machine = m->as(); - machine->set_features(f); - } } /** get features @@ -82,6 +77,42 @@ class LinearMulticlassMachine : public MulticlassMachine protected: + bool train_machine(std::shared_ptr data) override + { + m_features = data->as(); + require(m_multiclass_strategy, "Multiclass strategy not set"); + int32_t num_classes = m_labels->as()->get_num_classes(); + m_multiclass_strategy->set_num_classes(num_classes); + + m_machines.clear(); + auto train_labels = std::make_shared(get_num_rhs_vectors()); + + m_multiclass_strategy->train_start( + multiclass_labels(m_labels), train_labels); + while (m_multiclass_strategy->train_has_more()) + { + SGVector subset=m_multiclass_strategy->train_prepare_next(); + if (subset.vlen) + { + train_labels->add_subset(subset); + add_machine_subset(subset); + } + + m_machine->train(data, train_labels); + m_machines.push_back(get_machine_from_trained(m_machine)); + + if (subset.vlen) + { + train_labels->remove_subset(); + remove_machine_subset(); + } + } + + m_multiclass_strategy->train_stop(); + + + return true; + } /** init machine for train with setting features */ bool init_machine_for_train(std::shared_ptr data) override { @@ -91,8 +122,6 @@ class LinearMulticlassMachine : public MulticlassMachine if (data) set_features(data->as()); - m_machine->as()->set_features(m_features); - return true; } @@ -102,14 +131,6 @@ class LinearMulticlassMachine : public MulticlassMachine if (data) set_features(data->as()); - for (auto m: m_machines) - { - auto machine = m->as(); - ASSERT(m_features) - ASSERT(machine) - machine->set_features(m_features); - } - return true; } @@ -125,7 +146,7 @@ class LinearMulticlassMachine : public MulticlassMachine /** construct linear machine from given linear machine */ std::shared_ptr get_machine_from_trained(std::shared_ptr machine) const override { - return std::make_shared(machine->as()); + return machine->clone(ParameterProperties::MODEL)->as(); } /** get number of rhs feature vectors */ diff --git a/src/shogun/machine/Machine.cpp b/src/shogun/machine/Machine.cpp index e9267ddcaf6..a54f7940236 100644 --- a/src/shogun/machine/Machine.cpp +++ b/src/shogun/machine/Machine.cpp @@ -71,9 +71,30 @@ bool Machine::train(std::shared_ptr data) return result; } -bool Machine::train(const std::shared_ptr& data, const std::shared_ptr& lab){ - set_labels(lab); - return train(data); +bool Machine::train( + const std::shared_ptr& data, const std::shared_ptr& labs) +{ + auto sub = connect_to_signal_handler(); + bool result = false; + + if (support_feature_dispatching()) + { + if (support_dense_dispatching() && data->get_feature_class() == C_DENSE) + result = train_dense(data, labs); + else if ( + support_string_dispatching() && + data->get_feature_class() == C_STRING) + result = train_string(data, labs); + else + error("Training with {} is not implemented!", data->get_name()); + } + else + result = train_machine(data, labs); + + sub.unsubscribe(); + reset_computation_variables(); + + return result; } void Machine::set_labels(std::shared_ptr lab) diff --git a/src/shogun/machine/Machine.h b/src/shogun/machine/Machine.h index 95562b68769..91168e8b915 100644 --- a/src/shogun/machine/Machine.h +++ b/src/shogun/machine/Machine.h @@ -156,12 +156,14 @@ class Machine : public StoppableSGObject /** train machine * - * @param data training data + * @param data training data * @param lab training label * * @return whether training was successful */ - virtual bool train(const std::shared_ptr& data, const std::shared_ptr& lab); + virtual bool train( + const std::shared_ptr& data, + const std::shared_ptr& lab); /** apply machine to data * if data is not specified apply to the current features @@ -264,11 +266,38 @@ class Machine : public StoppableSGObject return false; } + virtual bool train_machine( + const std::shared_ptr& data, const std::shared_ptr& labs) + { + require(data->get_num_vectors() == labs->get_num_labels(), + "Number of training vectors ({}) does not match number of " + "labels ({})", + data->get_num_vectors(), labs->get_num_labels()); + + error("train_machine is not yet implemented for {}!", get_name()); + return false; + } + virtual bool train_dense(std::shared_ptr data) { not_implemented(SOURCE_LOCATION); return false; } + virtual bool train_dense( + const std::shared_ptr& data, + const std::shared_ptr& labs) + { + not_implemented(SOURCE_LOCATION); + return false; + } + + virtual bool train_string( + const std::shared_ptr& data, + const std::shared_ptr& labs) + { + not_implemented(SOURCE_LOCATION); + return false; + } virtual bool train_string(std::shared_ptr data) { diff --git a/src/shogun/machine/MulticlassMachine.cpp b/src/shogun/machine/MulticlassMachine.cpp index 96b9800caa9..b0e8c3b2ee4 100644 --- a/src/shogun/machine/MulticlassMachine.cpp +++ b/src/shogun/machine/MulticlassMachine.cpp @@ -57,11 +57,11 @@ void MulticlassMachine::init_strategy() m_multiclass_strategy->set_num_classes(num_classes); } -std::shared_ptr MulticlassMachine::get_submachine_outputs(int32_t i) +std::shared_ptr MulticlassMachine::get_submachine_outputs(const std::shared_ptr& data, int32_t i) { auto machine = m_machines.at(i); ASSERT(machine) - return machine->apply_binary(); + return machine->apply_binary(data); } float64_t MulticlassMachine::get_submachine_output(int32_t i, int32_t num) @@ -88,7 +88,6 @@ std::shared_ptr MulticlassMachine::apply_multiclass(std::share /* num vectors depends on whether data is provided */ int32_t num_vectors=data ? data->get_num_vectors() : get_num_rhs_vectors(); - int32_t num_machines=m_machines.size(); if (num_machines <= 0) error("num_machines = {}, did you train your machine?", num_machines); @@ -107,11 +106,9 @@ std::shared_ptr MulticlassMachine::apply_multiclass(std::share std::vector> outputs(num_machines); SGVector As(num_machines); SGVector Bs(num_machines); - for (int32_t i=0; iget_values()); @@ -122,7 +119,6 @@ std::shared_ptr MulticlassMachine::apply_multiclass(std::share if (heuris!=PROB_HEURIS_NONE && heuris!=OVA_SOFTMAX) outputs[i]->scores_to_probabilities(0,0); } - SGVector output_for_i(num_machines); SGVector r_output_for_i(num_machines); if (heuris!=PROB_HEURIS_NONE) @@ -180,10 +176,11 @@ std::shared_ptr MulticlassMachine::apply_multilabel_output(std if (is_ready()) { + /* num vectors depends on whether data is provided */ int32_t num_vectors=data ? data->get_num_vectors() : get_num_rhs_vectors(); - + int32_t num_machines=m_machines.size(); if (num_machines <= 0) error("num_machines = {}, did you train your machine?", num_machines); @@ -191,16 +188,14 @@ std::shared_ptr MulticlassMachine::apply_multilabel_output(std auto result=std::make_shared(num_vectors, n_outputs); std::vector> outputs(num_machines); - + for (int32_t i=0; i < num_machines; ++i) - outputs[i] = get_submachine_outputs(i); - + outputs[i] = get_submachine_outputs(data, i); SGVector output_for_i(num_machines); for (int32_t i=0; iget_value(i); - result->set_label(i, m_multiclass_strategy->decide_label_multiple_output(output_for_i, n_outputs)); } for (int32_t i=0; i < num_machines; ++i) diff --git a/src/shogun/machine/MulticlassMachine.h b/src/shogun/machine/MulticlassMachine.h index 2a08c39313a..1fc92670b00 100644 --- a/src/shogun/machine/MulticlassMachine.h +++ b/src/shogun/machine/MulticlassMachine.h @@ -74,10 +74,11 @@ class MulticlassMachine : public BaseMulticlassMachine } /** get outputs of i-th submachine + * @param data features to be trained * @param i number of submachine * @return outputs */ - virtual std::shared_ptr get_submachine_outputs(int32_t i); + virtual std::shared_ptr get_submachine_outputs(const std::shared_ptr& data, int32_t i); /** get output of i-th submachine for num-th vector * @param i number of submachine diff --git a/src/shogun/machine/NonParametricMachine.h b/src/shogun/machine/NonParametricMachine.h index bb698bb9949..09351d1f9d0 100644 --- a/src/shogun/machine/NonParametricMachine.h +++ b/src/shogun/machine/NonParametricMachine.h @@ -15,28 +15,39 @@ namespace shogun class NonParametricMachine : public Machine { public: - NonParametricMachine(): Machine() + NonParametricMachine() : Machine() { - //TODO : when all refactor is done, m_labels should be removed from - //Machine Class + // TODO : when all refactor is done, m_labels should be removed from + // Machine Class // SG_ADD( - // &m_labels, "labels", "labels used in train machine algorithm", - // ParameterProperties::READONLY); - SG_ADD(&m_features, "features_train", + // &m_labels, "labels", "labels used in train machine + // algorithm", ParameterProperties::READONLY); + SG_ADD( + &m_features, "features_train", "Training features of nonparametric model", ParameterProperties::READONLY); } virtual ~NonParametricMachine() { } + using Machine::train; - const char* get_name() const override{ return "NonParametricMachine"; } + bool train( + const std::shared_ptr& data, + const std::shared_ptr& lab) override + { + m_labels = lab; + return Machine::train(data); + } + const char* get_name() const override + { + return "NonParametricMachine"; + } protected: - std::shared_ptr m_features; - //TODO + // TODO // when all refactor is done, we should use this m_labels // std::shared_ptr m_labels; }; diff --git a/src/shogun/machine/Pipeline.cpp b/src/shogun/machine/Pipeline.cpp index 9dfc49c6a0e..f43737b5b1c 100644 --- a/src/shogun/machine/Pipeline.cpp +++ b/src/shogun/machine/Pipeline.cpp @@ -141,9 +141,17 @@ namespace shogun else { auto machine = shogun::get>(stage.second); - if (machine->train_require_labels()) - machine->set_labels(m_labels); - machine->train(current_data); + try + { + if (machine->train_require_labels()) + machine->set_labels(m_labels); + machine->train(current_data); + } + catch(const std::exception& e) + { + machine->train(current_data, m_labels); + } + } } return true; diff --git a/src/shogun/regression/KernelRidgeRegression.cpp b/src/shogun/regression/KernelRidgeRegression.cpp index b07216d822e..118705629bf 100644 --- a/src/shogun/regression/KernelRidgeRegression.cpp +++ b/src/shogun/regression/KernelRidgeRegression.cpp @@ -23,8 +23,9 @@ KernelRidgeRegression::KernelRidgeRegression() init(); } -KernelRidgeRegression::KernelRidgeRegression(float64_t tau, std::shared_ptr k) -: KernelMachine() +KernelRidgeRegression::KernelRidgeRegression( + float64_t tau, std::shared_ptr k) + : KernelMachine() { init(); @@ -63,10 +64,8 @@ bool KernelRidgeRegression::solve_krr_system() return true; } -bool KernelRidgeRegression::train_machine(std::shared_ptrdata) +bool KernelRidgeRegression::train_machine(std::shared_ptr data) { - require(m_labels->get_num_labels() == data->get_num_vectors(), - "Number of training vectors does not match number of labels"); require(kernel, "Kernel not set"); kernel->init(data, data); diff --git a/src/shogun/regression/LeastAngleRegression.cpp b/src/shogun/regression/LeastAngleRegression.cpp index 08cbb46a328..f75d94620aa 100644 --- a/src/shogun/regression/LeastAngleRegression.cpp +++ b/src/shogun/regression/LeastAngleRegression.cpp @@ -104,7 +104,9 @@ void LeastAngleRegression::plane_rot(ST x0, ST x1, } template -bool LeastAngleRegression::train_machine_templated(const std::shared_ptr>& data) +bool LeastAngleRegression::train_machine_templated( + const std::shared_ptr>& data, + const std::shared_ptr& labs) { std::vector> m_beta_path_t; @@ -122,7 +124,7 @@ bool LeastAngleRegression::train_machine_templated(const std::shared_ptr y = regression_labels(m_labels)->template get_labels_t(); + SGVector y = regression_labels(labs)->template get_labels_t(); typename SGVector::EigenVectorXtMap map_y(y.vector, y.size()); // transpose(X) is more convenient to work with since we care @@ -429,9 +431,15 @@ SGMatrix LeastAngleRegression::cholesky_delete(SGMatrix& R, int32_t i_ki return nR; } -template bool LeastAngleRegression::train_machine_templated(const std::shared_ptr>& data); -template bool LeastAngleRegression::train_machine_templated(const std::shared_ptr>& data); -template bool LeastAngleRegression::train_machine_templated(const std::shared_ptr>& data); +template bool LeastAngleRegression::train_machine_templated( + const std::shared_ptr>& data, + const std::shared_ptr& labs); +template bool LeastAngleRegression::train_machine_templated( + const std::shared_ptr>& data, + const std::shared_ptr& labs); +template bool LeastAngleRegression::train_machine_templated( + const std::shared_ptr>& data, + const std::shared_ptr& labs); template SGMatrix LeastAngleRegression::cholesky_insert(const SGMatrix& X, const SGMatrix& X_active, SGMatrix& R, int32_t i_max_corr, int32_t num_active); template SGMatrix LeastAngleRegression::cholesky_insert(const SGMatrix& X, const SGMatrix& X_active, SGMatrix& R, int32_t i_max_corr, int32_t num_active); template SGMatrix LeastAngleRegression::cholesky_insert(const SGMatrix& X, const SGMatrix& X_active, SGMatrix& R, int32_t i_max_corr, int32_t num_active); diff --git a/src/shogun/regression/LeastAngleRegression.h b/src/shogun/regression/LeastAngleRegression.h index 85b049b3355..96f99d7d620 100644 --- a/src/shogun/regression/LeastAngleRegression.h +++ b/src/shogun/regression/LeastAngleRegression.h @@ -171,9 +171,12 @@ class LeastAngleRegression: public DenseRealDispatch::value>> - bool train_machine_templated(const std::shared_ptr>& data); + template < + typename ST, typename U = typename std::enable_if_t< + std::is_floating_point::value>> + bool train_machine_templated( + const std::shared_ptr>& data, + const std::shared_ptr& labs); private: /** Initialize and register parameters */ diff --git a/src/shogun/regression/LeastSquaresRegression.cpp b/src/shogun/regression/LeastSquaresRegression.cpp index b1c660de11d..d0edda066ba 100644 --- a/src/shogun/regression/LeastSquaresRegression.cpp +++ b/src/shogun/regression/LeastSquaresRegression.cpp @@ -22,8 +22,5 @@ LeastSquaresRegression::LeastSquaresRegression() m_tau=0; } -LeastSquaresRegression::LeastSquaresRegression(std::shared_ptr> data, std::shared_ptr lab) -: LinearRidgeRegression(0, std::move(data), std::move(lab)) -{ -} + #endif diff --git a/src/shogun/regression/LeastSquaresRegression.h b/src/shogun/regression/LeastSquaresRegression.h index 0945c9b6035..cff982ebeff 100644 --- a/src/shogun/regression/LeastSquaresRegression.h +++ b/src/shogun/regression/LeastSquaresRegression.h @@ -31,13 +31,7 @@ namespace shogun /** default constructor */ LeastSquaresRegression(); - /** constructor - * - * @param data training data - * @param lab labels - */ - LeastSquaresRegression(std::shared_ptr> data, std::shared_ptr lab); - ~LeastSquaresRegression() override {} + ~LeastSquaresRegression() override = default; /** get classifier type * diff --git a/src/shogun/regression/LinearRidgeRegression.cpp b/src/shogun/regression/LinearRidgeRegression.cpp index bd6e61b7f01..209c8f13ad1 100644 --- a/src/shogun/regression/LinearRidgeRegression.cpp +++ b/src/shogun/regression/LinearRidgeRegression.cpp @@ -21,15 +21,12 @@ LinearRidgeRegression::LinearRidgeRegression() init(); } -LinearRidgeRegression::LinearRidgeRegression( - float64_t tau, const std::shared_ptr>& data, std::shared_ptr lab) +LinearRidgeRegression::LinearRidgeRegression(float64_t tau) : DenseRealDispatch() { init(); set_tau(tau); - set_labels(std::move(lab)); - set_features(data); } void LinearRidgeRegression::init() @@ -45,12 +42,13 @@ void LinearRidgeRegression::init() template bool LinearRidgeRegression::train_machine_templated( - const std::shared_ptr>& feats) + const std::shared_ptr>& feats, + const std::shared_ptr& labs) { auto N = feats->get_num_vectors(); auto D = feats->get_num_features(); - auto y = regression_labels(m_labels)->get_labels().as(); + auto y = regression_labels(labs)->get_labels().as(); T tau = m_tau; SGVector x_mean; diff --git a/src/shogun/regression/LinearRidgeRegression.h b/src/shogun/regression/LinearRidgeRegression.h index 7cc23894e69..ed084c43a36 100644 --- a/src/shogun/regression/LinearRidgeRegression.h +++ b/src/shogun/regression/LinearRidgeRegression.h @@ -69,8 +69,8 @@ namespace shogun * @param data training data * @param lab labels */ - LinearRidgeRegression(float64_t tau, const std::shared_ptr>& data, std::shared_ptr lab); - ~LinearRidgeRegression() override {} + LinearRidgeRegression(float64_t tau); + ~LinearRidgeRegression() override = default; /** set regularization constant * @@ -106,7 +106,9 @@ namespace shogun protected: template - bool train_machine_templated(const std::shared_ptr>& feats); + bool train_machine_templated( + const std::shared_ptr>& feats, + const std::shared_ptr& labs); private: void init(); diff --git a/src/shogun/regression/svr/LibLinearRegression.cpp b/src/shogun/regression/svr/LibLinearRegression.cpp index e33e663570e..2a29ea5a418 100644 --- a/src/shogun/regression/svr/LibLinearRegression.cpp +++ b/src/shogun/regression/svr/LibLinearRegression.cpp @@ -27,14 +27,12 @@ LibLinearRegression::LibLinearRegression() : init_defaults(); } -LibLinearRegression::LibLinearRegression(float64_t C, std::shared_ptr feats, std::shared_ptr labs) : - RandomMixin() +LibLinearRegression::LibLinearRegression(float64_t C) + : RandomMixin() { register_parameters(); init_defaults(); set_C(C); - set_features(std::move(feats)); - set_labels(std::move(labs)); } void LibLinearRegression::init_defaults() @@ -71,26 +69,14 @@ LibLinearRegression::~LibLinearRegression() { } -bool LibLinearRegression::train_machine(std::shared_ptr data) +bool LibLinearRegression::train_machine( + const std::shared_ptr& features, const std::shared_ptr& labs) { - - if (data) - set_features(data->as()); - - ASSERT(features) - ASSERT(m_labels && m_labels->get_label_type()==LT_REGRESSION) - - auto num_train_labels=m_labels->get_num_labels(); + auto labels = labs->as(); + auto num_feat=features->get_dim_feature_space(); auto num_vec=features->get_num_vectors(); - if (num_vec!=num_train_labels) - { - error("number of vectors {} does not match " - "number of training labels {}", - num_vec, num_train_labels); - } - SGVector w; auto prob = liblinear_problem(); prob.use_bias = get_use_bias(); @@ -112,7 +98,6 @@ bool LibLinearRegression::train_machine(std::shared_ptr data) } prob.l=num_vec; prob.x=features; - auto labels = regression_labels(m_labels); // store reference to vector locally in order to prevent free-ing auto lab = labels->get_labels(); diff --git a/src/shogun/regression/svr/LibLinearRegression.h b/src/shogun/regression/svr/LibLinearRegression.h index 7a70e2c4c44..131020110fe 100644 --- a/src/shogun/regression/svr/LibLinearRegression.h +++ b/src/shogun/regression/svr/LibLinearRegression.h @@ -50,10 +50,8 @@ class LibLinearRegression : public RandomMixin /** standard constructor * @param C C regularization constant value - * @param features features - * @param labs labels */ - LibLinearRegression(float64_t C, std::shared_ptr features, std::shared_ptr labs); + LibLinearRegression(float64_t C); /** destructor */ ~LibLinearRegression() override; @@ -148,7 +146,9 @@ class LibLinearRegression : public RandomMixin protected: /** train machine */ - bool train_machine(std::shared_ptr data = NULL) override; + bool train_machine( + const std::shared_ptr& data, + const std::shared_ptr& labs) override; private: /** solve svr with l1 or l2 loss */ diff --git a/src/shogun/transfer/domain_adaptation/DomainAdaptationMulticlassLibLinear.cpp b/src/shogun/transfer/domain_adaptation/DomainAdaptationMulticlassLibLinear.cpp index facc7f0263b..da95b06d027 100644 --- a/src/shogun/transfer/domain_adaptation/DomainAdaptationMulticlassLibLinear.cpp +++ b/src/shogun/transfer/domain_adaptation/DomainAdaptationMulticlassLibLinear.cpp @@ -102,10 +102,10 @@ SGMatrix DomainAdaptationMulticlassLibLinear::obtain_regularizer_matr return w0; } -std::shared_ptr DomainAdaptationMulticlassLibLinear::get_submachine_outputs(int32_t i) +std::shared_ptr DomainAdaptationMulticlassLibLinear::get_submachine_outputs(const std::shared_ptr& data, int32_t i) { - auto target_outputs = MulticlassMachine::get_submachine_outputs(i); - auto source_outputs = m_source_machine->get_submachine_outputs(i); + auto target_outputs = MulticlassMachine::get_submachine_outputs(data, i); + auto source_outputs = m_source_machine->get_submachine_outputs(data, i); int32_t n_target_outputs = target_outputs->get_num_labels(); ASSERT(n_target_outputs==source_outputs->get_num_labels()) SGVector result(n_target_outputs); diff --git a/src/shogun/transfer/domain_adaptation/DomainAdaptationMulticlassLibLinear.h b/src/shogun/transfer/domain_adaptation/DomainAdaptationMulticlassLibLinear.h index 828589035a2..f366bb2283a 100644 --- a/src/shogun/transfer/domain_adaptation/DomainAdaptationMulticlassLibLinear.h +++ b/src/shogun/transfer/domain_adaptation/DomainAdaptationMulticlassLibLinear.h @@ -36,7 +36,7 @@ class DomainAdaptationMulticlassLibLinear : public MulticlassLibLinear ~DomainAdaptationMulticlassLibLinear() override; /** get submachine outputs */ - std::shared_ptr get_submachine_outputs(int32_t) override; + std::shared_ptr get_submachine_outputs(const std::shared_ptr& data, int32_t i) override; /** get name */ const char* get_name() const override diff --git a/src/shogun/transfer/domain_adaptation/DomainAdaptationSVMLinear.cpp b/src/shogun/transfer/domain_adaptation/DomainAdaptationSVMLinear.cpp index abc1efb71b8..ba83915959f 100644 --- a/src/shogun/transfer/domain_adaptation/DomainAdaptationSVMLinear.cpp +++ b/src/shogun/transfer/domain_adaptation/DomainAdaptationSVMLinear.cpp @@ -25,7 +25,7 @@ DomainAdaptationSVMLinear::DomainAdaptationSVMLinear() : LibLinear(L2R_L1LOSS_SV } -DomainAdaptationSVMLinear::DomainAdaptationSVMLinear(float64_t C, std::shared_ptr f, std::shared_ptr lab, std::shared_ptr pre_svm, float64_t B_param) : LibLinear(C, std::move(f), std::move(lab)) +DomainAdaptationSVMLinear::DomainAdaptationSVMLinear(float64_t C, std::shared_ptr pre_svm, float64_t B_param) : LibLinear(C) { init(std::move(pre_svm), B_param); @@ -80,9 +80,6 @@ bool DomainAdaptationSVMLinear::is_presvm_sane() error("presvm bias not set to zero"); } - if (presvm->get_features()->get_feature_type() != this->get_features()->get_feature_type()) { - error("feature types do not agree"); - } } return true; @@ -90,30 +87,10 @@ bool DomainAdaptationSVMLinear::is_presvm_sane() } -bool DomainAdaptationSVMLinear::train_machine(std::shared_ptr train_data) +bool DomainAdaptationSVMLinear::train_machine(const std::shared_ptr& train_data, const std::shared_ptr& labs) { - std::shared_ptr tmp_data; - - if (m_labels->get_label_type() != LT_BINARY) - error("DomainAdaptationSVMLinear requires binary labels"); - - if (train_data) - { - if (!train_data->has_property(FP_DOT)) - error("DotFeatures expected"); - - if (m_labels->as()->get_num_labels() != train_data->get_num_vectors()) - error("Number of training vectors does not match number of labels"); - - tmp_data = train_data->as(); - } - else - { - tmp_data = features; - } - - auto labels = binary_labels(get_labels()); + auto labels = binary_labels(labs); int32_t num_training_points = labels->get_num_labels(); std::vector lin_term = std::vector(num_training_points); @@ -123,7 +100,7 @@ bool DomainAdaptationSVMLinear::train_machine(std::shared_ptr train_da ASSERT(presvm->get_bias() == 0.0) // bias of parent SVM was set to zero in constructor, already contains B - auto parent_svm_out = presvm->apply_binary(tmp_data); + auto parent_svm_out = presvm->apply_binary(train_data); SG_DEBUG("pre-computing linear term from presvm") @@ -161,20 +138,7 @@ bool DomainAdaptationSVMLinear::train_machine(std::shared_ptr train_da set_w(tmp_w_copy, w_dim); SG_FREE(tmp_w_copy); */ - - bool success = false; - - //train SVM - if (train_data) - { - success = LibLinear::train_machine(train_data); - } else { - success = LibLinear::train_machine(); - } - - //ASSERT(presvm) - - return success; + return LibLinear::train_machine(train_data, labs); } diff --git a/src/shogun/transfer/domain_adaptation/DomainAdaptationSVMLinear.h b/src/shogun/transfer/domain_adaptation/DomainAdaptationSVMLinear.h index d55e558310d..29b8c50f9fb 100644 --- a/src/shogun/transfer/domain_adaptation/DomainAdaptationSVMLinear.h +++ b/src/shogun/transfer/domain_adaptation/DomainAdaptationSVMLinear.h @@ -36,7 +36,7 @@ class DomainAdaptationSVMLinear : public LibLinear * @param presvm trained SVM to regularize against * @param B trade-off constant B */ - DomainAdaptationSVMLinear(float64_t C, std::shared_ptr f, std::shared_ptr lab, std::shared_ptr presvm, float64_t B); + DomainAdaptationSVMLinear(float64_t C, std::shared_ptr presvm, float64_t B); /** destructor */ @@ -62,7 +62,7 @@ class DomainAdaptationSVMLinear : public LibLinear * @param data (test)data to be classified * @return classified labels */ - std::shared_ptr apply_binary(std::shared_ptr data=NULL) override; + std::shared_ptr apply_binary(std::shared_ptr data) override; /** returns SVM that is used as prior information @@ -126,8 +126,7 @@ class DomainAdaptationSVMLinear : public LibLinear * * @return whether training was successful */ - bool train_machine(std::shared_ptr data=NULL) override; - + bool train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) override; protected: /** SVM to regularize against */ diff --git a/src/shogun/transfer/multitask/LibLinearMTL.cpp b/src/shogun/transfer/multitask/LibLinearMTL.cpp index 97bb869e864..c9ff4ea2d12 100644 --- a/src/shogun/transfer/multitask/LibLinearMTL.cpp +++ b/src/shogun/transfer/multitask/LibLinearMTL.cpp @@ -29,18 +29,12 @@ using namespace shogun; init(); } -LibLinearMTL::LibLinearMTL( - float64_t C, std::shared_ptr traindat, std::shared_ptr trainlab) -: RandomMixin() +LibLinearMTL::LibLinearMTL(float64_t C): RandomMixin() { init(); C1=C; C2=C; use_bias=true; - - set_features(std::move(traindat)); - set_labels(std::move(trainlab)); - } @@ -64,33 +58,17 @@ LibLinearMTL::~LibLinearMTL() { } -bool LibLinearMTL::train_machine(std::shared_ptr data) +bool LibLinearMTL::train_machine(const std::shared_ptr& features, const std::shared_ptr& labs) { - - ASSERT(m_labels) - - if (data) - { - if (!data->has_property(FP_DOT)) - error("Specified features are not of type DotFeatures"); - - set_features(data->as()); - } - ASSERT(features) - m_labels->ensure_valid(); - - + int32_t num_labels=labs->get_num_labels(); + require(num_labels==m_linear_term.vlen, "Number of labels ({}) does not match number" + " of entries ({}) in linear term ", num_labels, + m_linear_term.vlen); + labs->ensure_valid(); int32_t num_train_labels=m_labels->get_num_labels(); int32_t num_feat=features->get_dim_feature_space(); int32_t num_vec=features->get_num_vectors(); - if (num_vec!=num_train_labels) - { - error("number of vectors {} does not match " - "number of training labels {}", - num_vec, num_train_labels); - } - float64_t* training_w = NULL; if (use_bias) @@ -114,7 +92,7 @@ bool LibLinearMTL::train_machine(std::shared_ptr data) prob.y=SG_MALLOC(float64_t, prob.l); prob.use_bias=use_bias; - auto bl = binary_labels(m_labels); + auto bl = binary_labels(labs); for (int32_t i=0; iget_label(i); @@ -391,8 +369,9 @@ void LibLinearMTL::solve_l2r_l1l2_svc(const liblinear_problem *prob, double eps, } -float64_t LibLinearMTL::compute_primal_obj() +float64_t LibLinearMTL::compute_primal_obj(const std::shared_ptr& data, const std::shared_ptr& labs) { + /* python protype num_param = param.shape[0] num_dim = len(all_xt[0]) @@ -436,7 +415,7 @@ return obj io::info("DONE to compute Primal OBJ"); // calculate objective value SGMatrix W = get_W(); - + const auto features = data->as(); float64_t obj = 0; int32_t num_vec = features->get_num_vectors(); int32_t w_size = features->get_dim_feature_space(); @@ -486,7 +465,7 @@ return obj return obj; } -float64_t LibLinearMTL::compute_dual_obj() +float64_t LibLinearMTL::compute_dual_obj(const std::shared_ptr& data) { /* python prototype num_xt = len(xt) @@ -502,7 +481,7 @@ obj -= 0.5 * M[s,t] * alphas[i] * alphas[j] * lt[i] * lt[j] * np.dot(xt[i], xt[j return obj */ - + const auto features = data->as(); io::info("starting to compute DUAL OBJ"); int32_t num_vec=features->get_num_vectors(); diff --git a/src/shogun/transfer/multitask/LibLinearMTL.h b/src/shogun/transfer/multitask/LibLinearMTL.h index 2c07e52ce82..c5c78dfcd21 100644 --- a/src/shogun/transfer/multitask/LibLinearMTL.h +++ b/src/shogun/transfer/multitask/LibLinearMTL.h @@ -93,12 +93,8 @@ class LibLinearMTL : public RandomMixin /** constructor (using L2R_L1LOSS_SVC_DUAL as default) * * @param C constant C - * @param traindat training features - * @param trainlab training labels */ - LibLinearMTL( - float64_t C, std::shared_ptr traindat, - std::shared_ptr trainlab); + LibLinearMTL(float64_t C); /** destructor */ ~LibLinearMTL() override; @@ -177,18 +173,6 @@ class LibLinearMTL : public RandomMixin /** set the linear term for qp */ inline void set_linear_term(SGVector linear_term) { - if (!m_labels) - error("Please assign labels first!"); - - int32_t num_labels=m_labels->get_num_labels(); - - if (num_labels!=linear_term.vlen) - { - error("Number of labels ({}) does not match number" - " of entries ({}) in linear term ", num_labels, - linear_term.vlen); - } - m_linear_term = linear_term; } @@ -269,13 +253,13 @@ class LibLinearMTL : public RandomMixin * * @return primal objective */ - virtual float64_t compute_primal_obj(); + virtual float64_t compute_primal_obj(const std::shared_ptr& features, const std::shared_ptr& labs); /** compute dual objective * * @return dual objective */ - virtual float64_t compute_dual_obj(); + virtual float64_t compute_dual_obj(const std::shared_ptr& features); /** compute duality gap * @@ -293,7 +277,7 @@ class LibLinearMTL : public RandomMixin * * @return whether training was successful */ - bool train_machine(std::shared_ptr data=NULL) override; + bool train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) override; private: /** set up parameters */ diff --git a/tests/unit/classifier/LDA_unittest.cc b/tests/unit/classifier/LDA_unittest.cc index 7fa5d5e3474..77d033e1991 100644 --- a/tests/unit/classifier/LDA_unittest.cc +++ b/tests/unit/classifier/LDA_unittest.cc @@ -108,8 +108,7 @@ void test_with_method( std::shared_ptr labels = std::make_shared(lab); auto lda = std::make_shared(0, method); - lda->put("labels", labels); - lda->train(features); + lda->train(features, labels); auto results = lda->apply_regression(features); projection = results->get>("labels"); @@ -202,10 +201,9 @@ TEST(LDA, num_classes_in_labels_exception) std::shared_ptr labels = std::make_shared(lab); auto features = std::make_shared>(feat); auto lda = std::make_shared(0, SVD_LDA); - lda->put("labels", labels); // should throw an incorrect number of classes exception (expected value is // 2) - EXPECT_THROW(lda->train(features), ShogunException); + EXPECT_THROW(lda->train(features, labels), ShogunException); } //FLD template testing diff --git a/tests/unit/classifier/Perceptron_unittest.cc b/tests/unit/classifier/Perceptron_unittest.cc index c822e879a38..bd70e3f55cc 100644 --- a/tests/unit/classifier/Perceptron_unittest.cc +++ b/tests/unit/classifier/Perceptron_unittest.cc @@ -56,8 +56,7 @@ TEST(Perceptron, train) auto test_labels = env->get_labels_test(); auto perceptron = std::make_shared(); - perceptron->set_labels(labels); - EXPECT_TRUE(perceptron->train(features)); + EXPECT_TRUE(perceptron->train(features, labels)); auto results = perceptron->apply(test_features); auto acc = std::make_shared(); @@ -73,8 +72,7 @@ TEST(Perceptron, custom_hyperplane_initialization) auto test_labels = env->get_labels_test(); auto perceptron = std::make_shared(); - perceptron->set_labels(labels); - perceptron->train(features); + perceptron->train(features, labels); auto weights = perceptron->get_w(); @@ -82,9 +80,8 @@ TEST(Perceptron, custom_hyperplane_initialization) perceptron_initialized->set_initialize_hyperplane(false); perceptron_initialized->set_w(weights); perceptron_initialized->put("max_iterations", 1); - perceptron_initialized->set_labels(labels); - perceptron_initialized->train(features); + perceptron_initialized->train(features, labels); EXPECT_TRUE(perceptron_initialized->get_w().equals(weights)); } diff --git a/tests/unit/classifier/svm/LibLinear_unittest.cc b/tests/unit/classifier/svm/LibLinear_unittest.cc index 3e6f045552f..f1688cdc63b 100644 --- a/tests/unit/classifier/svm/LibLinear_unittest.cc +++ b/tests/unit/classifier/svm/LibLinear_unittest.cc @@ -53,11 +53,9 @@ class LibLinearFixture : public ::testing::Test ll->set_bias_enabled(biasEnable); - ll->set_features(train_feats); - ll->set_labels(ground_truth); ll->set_liblinear_solver_type(liblinear_solver_type); - ll->train(); + ll->train(train_feats, ground_truth); auto pred = ll->apply_binary(test_feats); auto liblin_accuracy = eval->evaluate(pred, ground_truth); @@ -82,13 +80,11 @@ class LibLinearFixture : public ::testing::Test ll->set_bias_enabled(biasEnable); - ll->set_features(train_feats); if (C_value) ll->set_C(0.1,0.1); //Only in the case of L2R_L1LOSS_SVC_DUAL - ll->set_labels(ground_truth); ll->set_liblinear_solver_type(liblinear_solver_type); ll->put("seed", seed); - ll->train(); + ll->train(train_feats, ground_truth); auto pred = ll->apply_binary(test_feats); diff --git a/tests/unit/classifier/svm/SVMOcas_unittest.cc b/tests/unit/classifier/svm/SVMOcas_unittest.cc index c11d37844b8..1617bcc5801 100644 --- a/tests/unit/classifier/svm/SVMOcas_unittest.cc +++ b/tests/unit/classifier/svm/SVMOcas_unittest.cc @@ -23,10 +23,10 @@ TEST(SVMOcasTest,train) auto ground_truth = std::static_pointer_cast(mockData->get_labels_test()); - auto ocas = std::make_shared(1.0, train_feats, ground_truth); + auto ocas = std::make_shared(1.0); env()->set_num_threads(1); ocas->set_epsilon(1e-5); - ocas->train(); + ocas->train(train_feats, ground_truth); float64_t objective = ocas->compute_primal_objective(); EXPECT_NEAR(objective, 0.024344632618686062, 1e-2); diff --git a/tests/unit/evaluation/CrossValidation_unittest.cc b/tests/unit/evaluation/CrossValidation_unittest.cc index e69f31133fc..34baa92c92f 100644 --- a/tests/unit/evaluation/CrossValidation_unittest.cc +++ b/tests/unit/evaluation/CrossValidation_unittest.cc @@ -168,9 +168,8 @@ class CrossValidationTests : public ::testing::Test std::shared_ptr machine; }; -typedef ::testing::Types +typedef ::testing::Types MachineTypes; TYPED_TEST_CASE(CrossValidationTests, MachineTypes); diff --git a/tests/unit/machine/FeatureDispatchCRTP_unittest.cc b/tests/unit/machine/FeatureDispatchCRTP_unittest.cc index a389cc903ba..78ce9bfe37a 100644 --- a/tests/unit/machine/FeatureDispatchCRTP_unittest.cc +++ b/tests/unit/machine/FeatureDispatchCRTP_unittest.cc @@ -22,7 +22,9 @@ class DenseRealMockMachine { } template - bool train_machine_templated(const std::shared_ptr>& data) + bool train_machine_templated( + const std::shared_ptr>& data, + const std::shared_ptr& labs) { if (data->get_feature_type() == m_expected_feature_type) return true; @@ -49,7 +51,9 @@ class StringMockMachine { } template - bool train_machine_templated(const std::shared_ptr>& data) + bool train_machine_templated( + const std::shared_ptr>& data, + const std::shared_ptr& labs) { if (data->get_feature_type() == m_expected_feature_type) return true; @@ -80,9 +84,8 @@ TYPED_TEST(DenseDispatchCRTP, train_with_dense) auto mock_machine = std::make_shared(features->get_feature_type()); - mock_machine->set_labels(std::make_shared(labels)); - - EXPECT_TRUE(mock_machine->train(features)); + auto labs = std::make_shared(labels); + EXPECT_TRUE(mock_machine->train(features, labs)); } typedef ::testing::Types SGCharTypes; @@ -103,9 +106,8 @@ TYPED_TEST(StringDispatchCRTP, train_with_string) auto labels = SGVector({1, -1}); auto mock_machine = std::make_shared(features->get_feature_type()); - mock_machine->set_labels(std::make_shared(labels)); - - EXPECT_TRUE(mock_machine->train(features)); + auto labs = std::make_shared(labels); + EXPECT_TRUE(mock_machine->train(features, labs)); } TEST(TrainDense, train_dense_with_wrong_feature_type) @@ -117,9 +119,8 @@ TEST(TrainDense, train_dense_with_wrong_feature_type) auto mock_machine = std::make_shared(features->get_feature_type()); - mock_machine->set_labels(std::make_shared(labels)); - - EXPECT_THROW(mock_machine->train(features), ShogunException); + auto labs = std::make_shared(labels); + EXPECT_THROW(mock_machine->train(features, labs), ShogunException); } TEST(TrainDense, train_dense_with_wrong_feature_class) @@ -132,6 +133,6 @@ TEST(TrainDense, train_dense_with_wrong_feature_class) auto mock_machine = std::make_shared(features->get_feature_type()); - mock_machine->set_labels(std::make_shared(labels)); - EXPECT_THROW(mock_machine->train(features), ShogunException); + auto labs = std::make_shared(labels); + EXPECT_THROW(mock_machine->train(features, labs), ShogunException); } diff --git a/tests/unit/multiclass/MulticlassLibLinear_unittest.cc b/tests/unit/multiclass/MulticlassLibLinear_unittest.cc index 4e853d67ab3..024f31bdbb6 100644 --- a/tests/unit/multiclass/MulticlassLibLinear_unittest.cc +++ b/tests/unit/multiclass/MulticlassLibLinear_unittest.cc @@ -52,11 +52,10 @@ TEST(MulticlassLibLinearTest,train_and_apply) float64_t C=1.0; - auto mocas=std::make_shared(C, features, - labels); + auto mocas=std::make_shared(C, features, labels); env()->set_num_threads(1); mocas->set_epsilon(1e-5); - mocas->train(); + mocas->train(features); auto pred=mocas->apply(features_test)->as(); for (int i=0; iget_num_vectors(); ++i) diff --git a/tests/unit/regression/LibLinearRegression_unittest.cc b/tests/unit/regression/LibLinearRegression_unittest.cc index 043f93d5695..2a57e6c3728 100644 --- a/tests/unit/regression/LibLinearRegression_unittest.cc +++ b/tests/unit/regression/LibLinearRegression_unittest.cc @@ -30,12 +30,11 @@ TEST(LibLinearRegression, lr_with_bias) auto labels_test = mockData->get_labels_test(); auto labels_train = mockData->get_labels_train(); - auto lr = - std::make_shared(1., train_feats, labels_train); + auto lr = std::make_shared(1.); lr->set_use_bias(use_bias); lr->set_epsilon(epsilon); lr->set_tube_epsilon(epsilon); - lr->train(); + lr->train(train_feats, labels_train); auto predicted_labels = lr->apply(test_feats)->as(); @@ -66,12 +65,11 @@ TEST(LibLinearRegression, lr_without_bias) auto labels_test = mockData->get_labels_test(); auto labels_train = mockData->get_labels_train(); - auto lr = - std::make_shared(1., train_feats, labels_train); + auto lr = std::make_shared(1.); lr->set_use_bias(use_bias); lr->set_epsilon(epsilon); lr->set_tube_epsilon(epsilon); - lr->train(); + lr->train(train_feats, labels_train); auto predicted_labels = lr->apply(test_feats)->as(); diff --git a/tests/unit/regression/lars_unittest.cc b/tests/unit/regression/lars_unittest.cc index 2a0745f4361..ba90598a808 100644 --- a/tests/unit/regression/lars_unittest.cc +++ b/tests/unit/regression/lars_unittest.cc @@ -106,8 +106,7 @@ TEST(LeastAngleRegression, lasso_n_greater_than_d) auto labels=std::make_shared(lab); auto lars=std::make_shared(); - lars->set_labels(labels); - lars->train(features); + lars->train(features, labels); SGVector active3=SGVector(lars->get_w_for_var(3)); SGVector active2=SGVector(lars->get_w_for_var(2)); @@ -138,8 +137,7 @@ TEST(LeastAngleRegression, lasso_n_less_than_d) auto labels=std::make_shared(lab); auto lars=std::make_shared(); - lars->set_labels(labels); - lars->train(features); + lars->train(features, labels); SGVector active2=SGVector(lars->get_w_for_var(2)); SGVector active1=SGVector(lars->get_w_for_var(1)); @@ -169,8 +167,7 @@ TEST(LeastAngleRegression, lars_n_greater_than_d) auto labels=std::make_shared(lab); auto lars=std::make_shared(false); - lars->set_labels(labels); - lars->train(features); + lars->train(features, labels); SGVector active3=SGVector(lars->get_w_for_var(3)); SGVector active2=SGVector(lars->get_w_for_var(2)); @@ -201,8 +198,7 @@ TEST(LeastAngleRegression, lars_n_less_than_d) auto labels=std::make_shared(lab); auto lars=std::make_shared(false); - lars->set_labels(labels); - lars->train(features); + lars->train(features, labels); SGVector active2=SGVector(lars->get_w_for_var(2)); SGVector active1=SGVector(lars->get_w_for_var(1)); @@ -240,12 +236,11 @@ void lars_n_less_than_d_feature_test_templated() auto lars=std::make_shared(false); - lars->set_labels(labels); //Catch exceptions thrown when training, clean up try { - lars->train(features); + lars->train(features, labels); } catch(...) { @@ -427,8 +422,7 @@ TEST(LeastAngleRegression, ols_equivalence) auto labels = std::make_shared(lab); auto lars = std::make_shared(false); - lars->set_labels(labels); - lars->train(features); + lars->train(features, labels); // Full LAR model SGVector w=lars->get_w(); Map map_w(w.vector, w.size()); @@ -456,10 +450,9 @@ TEST(LeastAngleRegression, early_stop_l1_norm) auto labels=std::make_shared(lab); auto lars=std::make_shared(false); - lars->set_labels(labels); // set max l1 norm lars->put("max_l1_norm", 1.0); - lars->train(features); + lars->train(features, labels); SGVector active2=SGVector(lars->get_w_for_var(2)); SGVector active1=SGVector(lars->get_w_for_var(1)); diff --git a/tests/unit/transfer/MALSAR_unittest.cc b/tests/unit/transfer/MALSAR_unittest.cc index 302ee93a8d3..f7ef9f591c0 100644 --- a/tests/unit/transfer/MALSAR_unittest.cc +++ b/tests/unit/transfer/MALSAR_unittest.cc @@ -60,11 +60,10 @@ TEST(MalsarL12Test, train) auto task = std::make_shared(0, data.second->get_num_labels()); task_group->append_task(task); - auto mtlr = std::make_shared(0.1,0.1,data.first.first,data.second,task_group); - mtlr->train(); - mtlr->set_features(data.first.second); + auto mtlr = std::make_shared(0.1,0.1, task_group); + mtlr->train(data.first.first, data.second); mtlr->set_current_task(0); - auto output = mtlr->apply(); + auto output = mtlr->apply(data.first.second); } @@ -77,11 +76,10 @@ TEST(MalsarClusteredTest, train) auto task = std::make_shared(0, data.second->get_num_labels()); task_group->append_task(task); - auto mtlr = std::make_shared(0.1,0.1,data.first.first,data.second,task_group,1); - mtlr->train(); - mtlr->set_features(data.first.second); + auto mtlr = std::make_shared(0.1,0.1, task_group,1); + mtlr->train(data.first.first, data.second); mtlr->set_current_task(0); - auto output = mtlr->apply(); + auto output = mtlr->apply(data.first.second); } @@ -94,11 +92,10 @@ TEST(MalsarTraceTest, train) auto task = std::make_shared(0, data.second->get_num_labels()); task_group->append_task(task); - auto mtlr = std::make_shared(0.1,data.first.first,data.second,task_group); - mtlr->train(); - mtlr->set_features(data.first.second); + auto mtlr = std::make_shared(0.1, task_group); + mtlr->train(data.first.first, data.second); mtlr->set_current_task(0); - auto output = mtlr->apply(); + auto output = mtlr->apply(data.first.second); } From bf39050a4991b4c2d82d52c249c865c210f621da Mon Sep 17 00:00:00 2001 From: LiuYuhui Date: Tue, 28 Jul 2020 13:38:34 +0800 Subject: [PATCH 5/9] Refactor MulticlassMachine (#5101) * Refactor Multiclass Machine --- .../classification/Classification.ipynb | 16 ++-- .../classification/MKL.ipynb | 24 +++--- .../SupportVectorMachines.ipynb | 6 +- doc/ipython-notebooks/multiclass/KNN.ipynb | 4 +- .../multiclass/multiclass_reduction.ipynb | 27 +++---- .../multiclass/naive_bayes.ipynb | 6 +- .../src/evaluation/accuracy_multiclass.sg.in | 4 +- .../meta/src/evaluation/multiclass_ovr.sg.in | 4 +- .../src/multiclass/ecoc_random_dense_hd.sg.in | 6 +- .../src/multiclass/gaussian_naive_bayes.sg.in | 4 +- examples/meta/src/multiclass/gmnpsvm.sg.in | 4 +- examples/meta/src/multiclass/linear.sg.in | 4 +- .../linear_discriminant_analysis.sg.in | 4 +- .../src/multiclass/logistic_regression.sg.in | 4 +- .../src/multiclass/multiclassliblinear.sg.in | 4 +- .../meta/src/multiclass/one_versus_rest.sg.in | 4 +- .../quadratic_discriminant_analysis.sg.in | 4 +- examples/meta/src/multiclass/shareboost.sg.in | 4 +- .../multiclass/support_vector_machine.sg.in | 4 +- .../python/classifier_multiclassocas.py | 4 +- .../classifier_multilabeloutputliblinear.py | 4 +- .../undocumented/python/mkl_multiclass.py | 4 +- src/shogun/classifier/mkl/MKLMulticlass.cpp | 56 +++++++------- src/shogun/classifier/mkl/MKLMulticlass.h | 13 ++-- src/shogun/classifier/svm/LibLinear.cpp | 5 ++ src/shogun/classifier/svm/LibLinear.h | 2 + src/shogun/classifier/svm/SVM.cpp | 8 ++ src/shogun/classifier/svm/SVM.h | 2 + src/shogun/machine/DirectorLinearMachine.h | 18 ----- .../machine/KernelMulticlassMachine.cpp | 4 +- src/shogun/machine/KernelMulticlassMachine.h | 2 +- src/shogun/machine/LinearMulticlassMachine.h | 75 ++++++------------- src/shogun/machine/Machine.cpp | 4 + src/shogun/machine/Machine.h | 5 -- src/shogun/machine/MulticlassMachine.cpp | 21 ++---- src/shogun/machine/MulticlassMachine.h | 16 ++-- src/shogun/mathematics/Seedable.h | 3 +- src/shogun/multiclass/GMNPSVM.cpp | 20 ++--- src/shogun/multiclass/GMNPSVM.h | 4 +- src/shogun/multiclass/GaussianNaiveBayes.cpp | 6 +- src/shogun/multiclass/GaussianNaiveBayes.h | 2 +- src/shogun/multiclass/MCLDA.cpp | 11 ++- src/shogun/multiclass/MCLDA.h | 4 +- src/shogun/multiclass/MulticlassLibLinear.cpp | 56 ++++++-------- src/shogun/multiclass/MulticlassLibLinear.h | 4 +- src/shogun/multiclass/MulticlassLibSVM.cpp | 18 ++--- src/shogun/multiclass/MulticlassLibSVM.h | 4 +- src/shogun/multiclass/MulticlassOCAS.cpp | 24 +++--- src/shogun/multiclass/MulticlassOCAS.h | 4 +- src/shogun/multiclass/MulticlassSVM.cpp | 6 +- src/shogun/multiclass/MulticlassSVM.h | 2 +- src/shogun/multiclass/QDA.cpp | 20 ++--- src/shogun/multiclass/QDA.h | 10 +-- src/shogun/multiclass/ScatterSVM.cpp | 46 ++++++------ src/shogun/multiclass/ScatterSVM.h | 12 +-- src/shogun/multiclass/ShareBoost.cpp | 47 +++++------- src/shogun/multiclass/ShareBoost.h | 15 ++-- src/shogun/multiclass/ShareBoostOptimizer.cpp | 4 +- src/shogun/multiclass/ShareBoostOptimizer.h | 2 +- .../multiclass/tree/RelaxedTreeUtil.cpp | 3 +- .../DomainAdaptationMulticlassLibLinear.cpp | 9 +-- .../DomainAdaptationMulticlassLibLinear.h | 4 +- tests/unit/multiclass/MCLDA_unittest.cc | 6 +- .../MulticlassLibLinear_unittest.cc | 4 +- .../multiclass/MulticlassOCAS_unittest.cc | 4 +- tests/unit/multiclass/QDA_unittest.cc | 6 +- 66 files changed, 326 insertions(+), 414 deletions(-) diff --git a/doc/ipython-notebooks/classification/Classification.ipynb b/doc/ipython-notebooks/classification/Classification.ipynb index 5ddbfc3afb4..a24fa498f63 100644 --- a/doc/ipython-notebooks/classification/Classification.ipynb +++ b/doc/ipython-notebooks/classification/Classification.ipynb @@ -403,9 +403,7 @@ "shogun_multiclass_labels_non_linear = sg.MulticlassLabels(multiclass_labels_non_linear)\n", "\n", "naive_bayes_linear = sg.create_machine(\"GaussianNaiveBayes\")\n", - "naive_bayes_linear.put('features', shogun_feats_linear)\n", - "naive_bayes_linear.put('labels', shogun_multiclass_labels_linear)\n", - "naive_bayes_linear.train()\n", + "naive_bayes_linear.train(shogun_feats_linear, shogun_multiclass_labels_linear)\n", "classifiers_linear.append(naive_bayes_linear)\n", "classifiers_names.append(\"Naive Bayes\")\n", "fadings.append(False)\n", @@ -416,9 +414,7 @@ "plot_model(plt,naive_bayes_linear,feats_linear,labels_linear,fading=False)\n", "\n", "naive_bayes_non_linear = sg.create_machine(\"GaussianNaiveBayes\")\n", - "naive_bayes_non_linear.put('features', shogun_feats_non_linear)\n", - "naive_bayes_non_linear.put('labels', shogun_multiclass_labels_non_linear)\n", - "naive_bayes_non_linear.train()\n", + "naive_bayes_non_linear.train(shogun_feats_non_linear, shogun_multiclass_labels_non_linear)\n", "classifiers_non_linear.append(naive_bayes_non_linear)\n", "\n", "plt.subplot(122)\n", @@ -515,8 +511,8 @@ "metadata": {}, "outputs": [], "source": [ - "qda_linear = sg.create_machine(\"QDA\", labels=shogun_multiclass_labels_linear)\n", - "qda_linear.train(shogun_feats_linear)\n", + "qda_linear = sg.create_machine(\"QDA\")\n", + "qda_linear.train(shogun_feats_linear, shogun_multiclass_labels_linear)\n", "classifiers_linear.append(qda_linear)\n", "classifiers_names.append(\"QDA\")\n", "fadings.append(False)\n", @@ -526,8 +522,8 @@ "plt.title(\"QDA - Linear Features\")\n", "plot_model(plt,qda_linear,feats_linear,labels_linear,fading=False)\n", "\n", - "qda_non_linear = sg.create_machine(\"QDA\", labels=shogun_multiclass_labels_non_linear)\n", - "qda_non_linear.train(shogun_feats_non_linear)\n", + "qda_non_linear = sg.create_machine(\"QDA\")\n", + "qda_non_linear.train(shogun_feats_non_linear, shogun_multiclass_labels_non_linear)\n", "classifiers_non_linear.append(qda_non_linear)\n", "\n", "plt.subplot(122)\n", diff --git a/doc/ipython-notebooks/classification/MKL.ipynb b/doc/ipython-notebooks/classification/MKL.ipynb index cda6f40a2e3..25ae1941792 100644 --- a/doc/ipython-notebooks/classification/MKL.ipynb +++ b/doc/ipython-notebooks/classification/MKL.ipynb @@ -253,10 +253,10 @@ "kernel.add(\"kernel_array\", kernel1)\n", "kernel.init(feats_train, feats_train)\n", "\n", - "mkl = sg.create_machine(\"MKLClassification\", mkl_norm=1, C1=1, C2=1, kernel=kernel, labels=labels)\n", + "mkl = sg.create_machine(\"MKLClassification\", mkl_norm=1, C1=1, C2=1, kernel=kernel)\n", "\n", "#train to get weights\n", - "mkl.train() \n", + "mkl.train(feats_train, labels) \n", "\n", "w=kernel.get_subkernel_weights()\n", "print(w)" @@ -490,9 +490,9 @@ " kernel.add(\"kernel_array\", kernel3)\n", " \n", " kernel.init(feats_tr, feats_tr)\n", - " mkl = sg.create_machine(\"MKLClassification\", mkl_norm=1, C1=1, C2=2, kernel=kernel, labels=lab)\n", + " mkl = sg.create_machine(\"MKLClassification\", mkl_norm=1, C1=1, C2=2, kernel=kernel)\n", " \n", - " mkl.train()\n", + " mkl.train(feats_tr, lab)\n", " \n", " w=kernel.get_subkernel_weights()\n", " return w, mkl\n", @@ -704,17 +704,17 @@ "kernel.init(feats_train, feats_train)\n", "\n", "mkl = sg.create_machine(\"MKLMulticlass\", C=1.2, kernel=kernel, \n", - " labels=labels, mkl_eps=0.001, mkl_norm=1)\n", + " mkl_eps=0.001, mkl_norm=1)\n", "\n", "# set epsilon of SVM\n", "mkl.get(\"machine\").put(\"epsilon\", 1e-2)\n", "\n", - "mkl.train()\n", + "mkl.train(feats_train, labels)\n", "\n", "#initialize with test features\n", "kernel.init(feats_train, feats_test) \n", "\n", - "out = mkl.apply()\n", + "out = mkl.apply(feats_test)\n", "evaluator = sg.create_evaluation(\"MulticlassAccuracy\")\n", "accuracy = evaluator.evaluate(out, labels_rem)\n", "print(\"Accuracy = %2.2f%%\" % (100*accuracy))\n", @@ -748,8 +748,8 @@ "\n", "pk = sg.create_kernel('PolyKernel', degree=10, c=2) \n", "\n", - "svm = sg.create_machine(\"GMNPSVM\", C=C, kernel=pk, labels=labels)\n", - "_=svm.train(feats)\n", + "svm = sg.create_machine(\"GMNPSVM\", C=C, kernel=pk)\n", + "_=svm.train(feats, labels)\n", "out=svm.apply(feats_rem)\n", "evaluator = sg.create_evaluation(\"MulticlassAccuracy\")\n", "accuracy = evaluator.evaluate(out, labels_rem)\n", @@ -776,8 +776,8 @@ "\n", "gk=sg.create_kernel(\"GaussianKernel\", width=width)\n", "\n", - "svm=sg.create_machine(\"GMNPSVM\", C=C, kernel=gk, labels=labels)\n", - "_=svm.train(feats)\n", + "svm=sg.create_machine(\"GMNPSVM\", C=C, kernel=gk)\n", + "_=svm.train(feats, labels)\n", "out=svm.apply(feats_rem)\n", "evaluator = sg.create_evaluation(\"MulticlassAccuracy\")\n", "accuracy = evaluator.evaluate(out, labels_rem)\n", @@ -984,7 +984,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/doc/ipython-notebooks/classification/SupportVectorMachines.ipynb b/doc/ipython-notebooks/classification/SupportVectorMachines.ipynb index e39e2c4f925..286de351266 100644 --- a/doc/ipython-notebooks/classification/SupportVectorMachines.ipynb +++ b/doc/ipython-notebooks/classification/SupportVectorMachines.ipynb @@ -932,8 +932,8 @@ "metadata": {}, "outputs": [], "source": [ - "svm=sg.create_machine(\"GMNPSVM\", C=1, kernel=gaussian_kernel, labels=labels)\n", - "_=svm.train(feats_train)\n", + "svm=sg.create_machine(\"GMNPSVM\", C=1, kernel=gaussian_kernel)\n", + "_=svm.train(feats_train, labels)\n", "\n", "size=100\n", "x1=np.linspace(-6, 6, size)\n", @@ -947,7 +947,7 @@ " plt.subplot(1,len(kernels),i+1)\n", " plt.title(kernels[i].get_name())\n", " svm.put(\"kernel\", kernels[i])\n", - " svm.train(feats_train)\n", + " svm.train(feats_train, labels)\n", " grid_out=svm.apply(grid)\n", " z=grid_out.get(\"labels\").reshape((size, size))\n", " plt.pcolor(x, y, z)\n", diff --git a/doc/ipython-notebooks/multiclass/KNN.ipynb b/doc/ipython-notebooks/multiclass/KNN.ipynb index ade4a8fc10d..0587182462f 100644 --- a/doc/ipython-notebooks/multiclass/KNN.ipynb +++ b/doc/ipython-notebooks/multiclass/KNN.ipynb @@ -408,8 +408,8 @@ "\n", "gk=sg.create_kernel(\"GaussianKernel\", width=width)\n", "\n", - "svm=sg.create_machine(\"GMNPSVM\", C=C, kernel=gk, labels=labels)\n", - "_=svm.train(feats)" + "svm=sg.create_machine(\"GMNPSVM\", C=C, kernel=gk)\n", + "_=svm.train(feats, labels)" ] }, { diff --git a/doc/ipython-notebooks/multiclass/multiclass_reduction.ipynb b/doc/ipython-notebooks/multiclass/multiclass_reduction.ipynb index 8d86f6a5bbd..7152032694c 100644 --- a/doc/ipython-notebooks/multiclass/multiclass_reduction.ipynb +++ b/doc/ipython-notebooks/multiclass/multiclass_reduction.ipynb @@ -205,11 +205,10 @@ "\n", " mc_machine = sg.create_machine(\"LinearMulticlassMachine\",\n", " multiclass_strategy=strategy, \n", - " machine=bin_machine, \n", - " labels=lab_train)\n", + " machine=bin_machine)\n", "\n", " t_begin = time.process_time()\n", - " mc_machine.train(feats_train)\n", + " mc_machine.train(feats_train, lab_train)\n", " t_train = time.process_time() - t_begin\n", "\n", " t_begin = time.process_time()\n", @@ -259,11 +258,10 @@ "metadata": {}, "outputs": [], "source": [ - "mcsvm = sg.create_machine(\"MulticlassLibLinear\", C=5.0, \n", - " labels=lab_train, use_bias=True)\n", + "mcsvm = sg.create_machine(\"MulticlassLibLinear\", C=5.0, use_bias=True)\n", "\n", "t_begin = time.process_time()\n", - "mcsvm.train(feats_train)\n", + "mcsvm.train(feats_train, lab_train)\n", "t_train = time.process_time() - t_begin\n", "\n", "t_begin = time.process_time()\n", @@ -472,11 +470,10 @@ " mc_machine = sg.create_machine(\"KernelMulticlassMachine\",\n", " multiclass_strategy=strategy, \n", " kernel=kernel, \n", - " machine=classifier,\n", - " labels=lab_train)\n", + " machine=classifier)\n", "\n", " t_begin = time.process_time()\n", - " mc_machine.train()\n", + " mc_machine.train(feats_train, lab_train)\n", " t_train = time.process_time() - t_begin\n", "\n", " t_begin = time.process_time()\n", @@ -609,10 +606,9 @@ "mc_machine=sg.create_machine(\"KernelMulticlassMachine\",\n", " multiclass_strategy=sg.create_multiclass_strategy(\"MulticlassOneVsRestStrategy\"),\n", " kernel=kernel, \n", - " machine=classifier, \n", - " labels=labels)\n", + " machine=classifier)\n", "\n", - "mc_machine.train()\n", + "mc_machine.train(feats_tr, labels)\n", "\n", "size=100\n", "x1=linspace(-10, 10, size)\n", @@ -668,9 +664,8 @@ "\n", "mc_machine1 = sg.create_machine(\"LinearMulticlassMachine\",\n", " multiclass_strategy=sg.create_multiclass_strategy(\"MulticlassOneVsOneStrategy\"),\n", - " machine=bin_machine, \n", - " labels=labels)\n", - "mc_machine1.train(feats_tr)\n", + " machine=bin_machine)\n", + "mc_machine1.train(feats_tr, labels)\n", "\n", "out1=mc_machine1.apply_multiclass(grid) #main output\n", "z1=out1.get_labels().reshape((size, size))\n", @@ -728,7 +723,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/doc/ipython-notebooks/multiclass/naive_bayes.ipynb b/doc/ipython-notebooks/multiclass/naive_bayes.ipynb index e9d5a4abeb1..4ed06769292 100644 --- a/doc/ipython-notebooks/multiclass/naive_bayes.ipynb +++ b/doc/ipython-notebooks/multiclass/naive_bayes.ipynb @@ -135,9 +135,9 @@ "source": [ "X_train, Y_train = gen_samples(n_train)\n", "\n", - "machine = sg.create_machine(\"GaussianNaiveBayes\", labels=sg.create_labels(Y_train))\n", + "machine = sg.create_machine(\"GaussianNaiveBayes\")\n", "\n", - "machine.train(sg.create_features(X_train))" + "machine.train(sg.create_features(X_train), sg.create_labels(Y_train))" ] }, { @@ -283,7 +283,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/examples/meta/src/evaluation/accuracy_multiclass.sg.in b/examples/meta/src/evaluation/accuracy_multiclass.sg.in index d57830112ea..76a7dd4ff55 100644 --- a/examples/meta/src/evaluation/accuracy_multiclass.sg.in +++ b/examples/meta/src/evaluation/accuracy_multiclass.sg.in @@ -11,11 +11,11 @@ Labels labels_test = create_labels(f_labels_test) #![create_features] #![create_classifier] -Machine svm= create_machine("MulticlassLibLinear", C=1.0, labels=labels_train) +Machine svm= create_machine("MulticlassLibLinear", C=1.0) #![create_classifier] #![train_and_apply] -svm.train(feats_train) +svm.train(feats_train, labels_train) Labels predicted_labels = svm.apply(feats_test) #![train_and_apply] diff --git a/examples/meta/src/evaluation/multiclass_ovr.sg.in b/examples/meta/src/evaluation/multiclass_ovr.sg.in index ec953373a9c..e072e244ed5 100644 --- a/examples/meta/src/evaluation/multiclass_ovr.sg.in +++ b/examples/meta/src/evaluation/multiclass_ovr.sg.in @@ -7,11 +7,11 @@ Labels labels_train = create_labels(f_labels_train) #![create_features] #![create_classifier] -Machine svm= create_machine("MulticlassLibLinear", C=1.0, labels=labels_train) +Machine svm= create_machine("MulticlassLibLinear", C=1.0) #![create_classifier] #![train_and_apply] -svm.train(feats_train) +svm.train(feats_train, labels_train) Labels labels_predicted = svm.apply(feats_train) RealVector labels = labels_predicted.get_real_vector("labels") #![train_and_apply] diff --git a/examples/meta/src/multiclass/ecoc_random_dense_hd.sg.in b/examples/meta/src/multiclass/ecoc_random_dense_hd.sg.in index 945cb5390a2..2061ef0eab1 100644 --- a/examples/meta/src/multiclass/ecoc_random_dense_hd.sg.in +++ b/examples/meta/src/multiclass/ecoc_random_dense_hd.sg.in @@ -22,12 +22,12 @@ MulticlassStrategy rnd_dense_strategy=create_multiclass_strategy("ECOCStrategy", #![choose_strategy] #![create_instance] -Machine mc_classifier=create_machine("LinearMulticlassMachine", multiclass_strategy=rnd_dense_strategy, machine=classifier, labels=labels_train) +Machine mc_classifier=create_machine("LinearMulticlassMachine", multiclass_strategy=rnd_dense_strategy, machine=classifier) #![create_instance] #![train_and_apply] -mc_classifier.train(features_train) -Labels labels_predict = mc_classifier.apply(features_test) +mc_classifier.train(features_train, labels_train) +MulticlassLabels labels_predict = mc_classifier.apply_multiclass(features_test) #![train_and_apply] #![evaluate_accuracy] diff --git a/examples/meta/src/multiclass/gaussian_naive_bayes.sg.in b/examples/meta/src/multiclass/gaussian_naive_bayes.sg.in index 47c50613df0..9709698d1c1 100644 --- a/examples/meta/src/multiclass/gaussian_naive_bayes.sg.in +++ b/examples/meta/src/multiclass/gaussian_naive_bayes.sg.in @@ -10,11 +10,11 @@ Labels labels_train = create_labels(f_labels_train) #![create_instance] -Machine gnb = create_machine("GaussianNaiveBayes", features=features_train, labels=labels_train) +Machine gnb = create_machine("GaussianNaiveBayes") #![create_instance] #![train_and_apply] -gnb.train() +gnb.train(features_train, labels_train) Labels labels_predict = gnb.apply(features_test) RealVector labels = labels_predict.get_real_vector("labels") #![train_and_apply] diff --git a/examples/meta/src/multiclass/gmnpsvm.sg.in b/examples/meta/src/multiclass/gmnpsvm.sg.in index d29a1a4dc27..5e28fb1ab0b 100644 --- a/examples/meta/src/multiclass/gmnpsvm.sg.in +++ b/examples/meta/src/multiclass/gmnpsvm.sg.in @@ -10,11 +10,11 @@ Labels labels_train = create_labels(f_labels_train) #![create_machine] Kernel gaussian_kernel = create_kernel("GaussianKernel", width=2.1) -Machine gmnpsvm = create_machine("GMNPSVM", C=1.0, kernel=gaussian_kernel, labels=labels_train) +Machine gmnpsvm = create_machine("GMNPSVM", C=1.0, kernel=gaussian_kernel) #![create_machine] #![train_and_apply] -gmnpsvm.train(feats_train) +gmnpsvm.train(feats_train, labels_train) Labels test_labels = gmnpsvm.apply(feats_test) RealVector test_labels_vector = test_labels.get_real_vector("labels") #![train_and_apply] diff --git a/examples/meta/src/multiclass/linear.sg.in b/examples/meta/src/multiclass/linear.sg.in index 2a291f9f28f..18e6328f7e6 100644 --- a/examples/meta/src/multiclass/linear.sg.in +++ b/examples/meta/src/multiclass/linear.sg.in @@ -19,11 +19,11 @@ MulticlassStrategy strategy=create_multiclass_strategy("MulticlassOneVsOneStrate #![choose_strategy] #![create_instance] -Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier, labels = labels_train) +Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier) #![create_instance] #![train_and_apply] -mc_classifier.train(features_train) +mc_classifier.train(features_train, labels_train) MulticlassLabels labels_predict = mc_classifier.apply_multiclass(features_test) #![train_and_apply] diff --git a/examples/meta/src/multiclass/linear_discriminant_analysis.sg.in b/examples/meta/src/multiclass/linear_discriminant_analysis.sg.in index 6b2c00d6fed..b25e95eb6f2 100644 --- a/examples/meta/src/multiclass/linear_discriminant_analysis.sg.in +++ b/examples/meta/src/multiclass/linear_discriminant_analysis.sg.in @@ -11,11 +11,11 @@ Labels labels_test = create_labels(f_labels_test) #![create_features] #![create_instance] -Machine mc_lda = create_machine("MCLDA", labels=labels_train, m_tolerance=0.0001, m_store_cov=True) +Machine mc_lda = create_machine("MCLDA", m_tolerance=0.0001, m_store_cov=True) #![create_instance] #![train_and_apply] -mc_lda.train(features_train) +mc_lda.train(features_train, labels_train) MulticlassLabels labels_predict = mc_lda.apply_multiclass(features_test) #![train_and_apply] diff --git a/examples/meta/src/multiclass/logistic_regression.sg.in b/examples/meta/src/multiclass/logistic_regression.sg.in index 618d8b75d7d..1e3a9915458 100644 --- a/examples/meta/src/multiclass/logistic_regression.sg.in +++ b/examples/meta/src/multiclass/logistic_regression.sg.in @@ -12,11 +12,11 @@ Labels labels_test = create_labels(f_labels_test) #![create_instance] -Machine classifier = create_machine("MulticlassLogisticRegression", m_z=1.0, labels = labels_train) +Machine classifier = create_machine("MulticlassLogisticRegression", m_z=1.0) #![create_instance] #![train_and_apply] -classifier.train(features_train) +classifier.train(features_train, labels_train) MulticlassLabels labels_predict = classifier.apply_multiclass(features_test) #![train_and_apply] diff --git a/examples/meta/src/multiclass/multiclassliblinear.sg.in b/examples/meta/src/multiclass/multiclassliblinear.sg.in index 55c9c19adb4..204510c0a6d 100644 --- a/examples/meta/src/multiclass/multiclassliblinear.sg.in +++ b/examples/meta/src/multiclass/multiclassliblinear.sg.in @@ -9,11 +9,11 @@ Labels labels_train = create_labels(label_train_multiclass) #![create_features] #![create_machine] -Machine classifier = create_machine("MulticlassLibLinear", C=1.0, labels = labels_train) +Machine classifier = create_machine("MulticlassLibLinear", C=1.0) #![create_machine] #![train_and_apply] -classifier.train(feats_train) +classifier.train(feats_train, labels_train) Labels labels_train_predict = classifier.apply(feats_train) Labels labels_test_predict = classifier.apply(feats_test) #![train_and_apply] diff --git a/examples/meta/src/multiclass/one_versus_rest.sg.in b/examples/meta/src/multiclass/one_versus_rest.sg.in index 9913830aab9..8a358b4fc78 100644 --- a/examples/meta/src/multiclass/one_versus_rest.sg.in +++ b/examples/meta/src/multiclass/one_versus_rest.sg.in @@ -21,11 +21,11 @@ Machine classifier = create_machine("LibSVM") #![create_classifier] #![create_machine] -Machine multiclass_machine = create_machine("KernelMulticlassMachine", multiclass_strategy=one_versus_rest, kernel=k, machine=classifier, labels=labels_train) +Machine multiclass_machine = create_machine("KernelMulticlassMachine", multiclass_strategy=one_versus_rest, kernel=k, machine=classifier) #![create_machine] #![train_and_apply] -multiclass_machine.train(features_train) +multiclass_machine.train(features_train, labels_train) Labels labels_predict = multiclass_machine.apply(features_test) #![train_and_apply] diff --git a/examples/meta/src/multiclass/quadratic_discriminant_analysis.sg.in b/examples/meta/src/multiclass/quadratic_discriminant_analysis.sg.in index 5a8152d0561..fb885b3162d 100644 --- a/examples/meta/src/multiclass/quadratic_discriminant_analysis.sg.in +++ b/examples/meta/src/multiclass/quadratic_discriminant_analysis.sg.in @@ -11,11 +11,11 @@ Labels labels_test = create_labels(f_labels_test) #![create_features] #![create_instance] -Machine qda = create_machine("QDA", labels=labels_train, m_tolerance=0.0001, m_store_covs=True) +Machine qda = create_machine("QDA", m_tolerance=0.0001, m_store_covs=True) #![create_instance] #![train_and_apply] -qda.train(features_train) +qda.train(features_train, labels_train) MulticlassLabels labels_predict = qda.apply_multiclass(features_test) #![train_and_apply] diff --git a/examples/meta/src/multiclass/shareboost.sg.in b/examples/meta/src/multiclass/shareboost.sg.in index 9c6bb7065cf..9ba14445974 100644 --- a/examples/meta/src/multiclass/shareboost.sg.in +++ b/examples/meta/src/multiclass/shareboost.sg.in @@ -11,11 +11,11 @@ Labels labels_test = create_labels(f_labels_test) #![create_features] #![create_instance] -Machine shareboost = create_machine("ShareBoost", labels=labels_train, nonzero_feas=2) +Machine shareboost = create_machine("ShareBoost", nonzero_feas=2) #![create_instance] #![train_and_apply] -shareboost.train(features_train) +shareboost.train(features_train, labels_train) Features features_test_sub = create_subset_features(features_test, shareboost.get_int_vector("active_set")) MulticlassLabels labels_predict = shareboost.apply_multiclass(features_test_sub) #![train_and_apply] diff --git a/examples/meta/src/multiclass/support_vector_machine.sg.in b/examples/meta/src/multiclass/support_vector_machine.sg.in index 1ca37f9ebfb..27a38640588 100644 --- a/examples/meta/src/multiclass/support_vector_machine.sg.in +++ b/examples/meta/src/multiclass/support_vector_machine.sg.in @@ -16,11 +16,11 @@ Kernel gauss_kernel = create_kernel("GaussianKernel", width=1.0) #![set_parameters] #![create_instance] -Machine svm = create_machine("MulticlassLibSVM", C=C, kernel=gauss_kernel, labels=labels_train) +Machine svm = create_machine("MulticlassLibSVM", C=C, kernel=gauss_kernel) #![create_instance] #![train_and_apply] -svm.train(features_train) +svm.train(features_train, labels_train) Labels labels_predict = svm.apply(features_test) #![train_and_apply] diff --git a/examples/undocumented/python/classifier_multiclassocas.py b/examples/undocumented/python/classifier_multiclassocas.py index 8febc57ec6a..1746833a475 100644 --- a/examples/undocumented/python/classifier_multiclassocas.py +++ b/examples/undocumented/python/classifier_multiclassocas.py @@ -28,8 +28,8 @@ def classifier_multiclassocas (num_vec=10,num_class=3,distance=15,width=2.1,C=1, labels=sg.create_labels(label_train) - classifier = sg.create_machine("MulticlassOCAS", labels=labels, C=C) - classifier.train(feats_train) + classifier = sg.create_machine("MulticlassOCAS", C=C) + classifier.train(feats_train, labels) out = classifier.apply(feats_test).get("labels") #print label_test diff --git a/examples/undocumented/python/classifier_multilabeloutputliblinear.py b/examples/undocumented/python/classifier_multilabeloutputliblinear.py index 1bd417d8c19..11aae702253 100644 --- a/examples/undocumented/python/classifier_multilabeloutputliblinear.py +++ b/examples/undocumented/python/classifier_multilabeloutputliblinear.py @@ -14,8 +14,8 @@ def classifier_multilabeloutputliblinear (fm_train_real=traindat,fm_test_real=te labels=MulticlassLabels(label_train_multiclass) - classifier = sg.create_machine("MulticlassLibLinear", C=C, labels=labels) - classifier.train(feats_train) + classifier = sg.create_machine("MulticlassLibLinear", C=C) + classifier.train(feats_train, labels) # TODO: figure out the new style API for the below call, disabling for now #label_pred = classifier.apply_multilabel_output(feats_test,2) diff --git a/examples/undocumented/python/mkl_multiclass.py b/examples/undocumented/python/mkl_multiclass.py index a3d583fd41e..5990c245eb6 100644 --- a/examples/undocumented/python/mkl_multiclass.py +++ b/examples/undocumented/python/mkl_multiclass.py @@ -44,14 +44,14 @@ def mkl_multiclass (fm_train_real, fm_test_real, label_train_multiclass, labels = MulticlassLabels(label_train_multiclass) - mkl = sg.create_machine("MKLMulticlass", C=C, kernel=kernel, labels=labels, + mkl = sg.create_machine("MKLMulticlass", C=C, kernel=kernel, mkl_eps=mkl_epsilon, mkl_norm=mkl_norm) mkl.get("machine").put("epsilon", epsilon) mkl.get_global_parallel().set_num_threads(num_threads) - mkl.train() + mkl.train(feats_train, labels) kernel.init(feats_train, feats_test) diff --git a/src/shogun/classifier/mkl/MKLMulticlass.cpp b/src/shogun/classifier/mkl/MKLMulticlass.cpp index b50876f1459..7e5ac207c7c 100644 --- a/src/shogun/classifier/mkl/MKLMulticlass.cpp +++ b/src/shogun/classifier/mkl/MKLMulticlass.cpp @@ -26,8 +26,8 @@ MKLMulticlass::MKLMulticlass() init(); } -MKLMulticlass::MKLMulticlass(float64_t C, std::shared_ptr k, std::shared_ptr lab) -: MulticlassSVM(std::make_shared(), C, std::move(k), std::move(lab)) +MKLMulticlass::MKLMulticlass(float64_t C, std::shared_ptr k ) +: MulticlassSVM(std::make_shared(), C, std::move(k) ) { svm=NULL; lpw=NULL; @@ -72,9 +72,9 @@ MKLMulticlass MKLMulticlass::operator=( const MKLMulticlass & cm) } -void MKLMulticlass::initsvm() +void MKLMulticlass::initsvm( const std::shared_ptr& labs) { - if (!m_labels) + if (!labs) { error("MKLMulticlass::initsvm(): the set labels is NULL"); } @@ -84,13 +84,13 @@ void MKLMulticlass::initsvm() svm->set_C(get_C()); svm->set_epsilon(get_epsilon()); - if (m_labels->get_num_labels()<=0) + if (labs->get_num_labels()<=0) { error("MKLMulticlass::initsvm(): the number of labels is " "nonpositive, do not know how to handle this!\n"); } - svm->set_labels(m_labels); + svm->set_labels(labs); } void MKLMulticlass::initlpsolver() @@ -210,8 +210,8 @@ bool MKLMulticlass::evaluatefinishcriterion(const int32_t return false; } -void MKLMulticlass::addingweightsstep( const std::vector & - curweights) +void MKLMulticlass::addingweightsstep( const std::vector & curweights, + const std::shared_ptr& data, const std::shared_ptr& labs) { if (weightshistory.size()>2) @@ -228,12 +228,12 @@ void MKLMulticlass::addingweightsstep( const std::vector & //delete[] weights; //weights=NULL; - initsvm(); + initsvm(labs); svm->set_kernel(m_kernel); - svm->train(); + svm->train(data, labs); - float64_t sumofsignfreealphas=getsumofsignfreealphas(); + float64_t sumofsignfreealphas=getsumofsignfreealphas(labs); curalphaterm=sumofsignfreealphas; int32_t numkernels= @@ -243,23 +243,23 @@ void MKLMulticlass::addingweightsstep( const std::vector & normweightssquared.resize(numkernels); for (int32_t ind=0; ind < numkernels; ++ind ) { - normweightssquared[ind]=getsquarenormofprimalcoefficients( ind ); + normweightssquared[ind]=getsquarenormofprimalcoefficients(ind, labs); } lpw->addconstraint(normweightssquared,sumofsignfreealphas); } -float64_t MKLMulticlass::getsumofsignfreealphas() +float64_t MKLMulticlass::getsumofsignfreealphas( const std::shared_ptr& labs) { - std::vector trainlabels2(m_labels->get_num_labels()); - SGVector lab=(std::static_pointer_cast(m_labels))->get_int_labels(); + std::vector trainlabels2(labs->get_num_labels()); + SGVector lab=(std::static_pointer_cast(labs))->get_int_labels(); std::copy(lab.vector,lab.vector+lab.vlen, trainlabels2.begin()); ASSERT (trainlabels2.size()>0) float64_t sum=0; - for (int32_t nc=0; nc< (std::static_pointer_cast(m_labels))->get_num_classes();++nc) + for (int32_t nc=0; nc< (std::static_pointer_cast(labs))->get_num_classes();++nc) { auto sm=svm->get_svm(nc); @@ -275,7 +275,7 @@ float64_t MKLMulticlass::getsumofsignfreealphas() for (size_t lb=0; lb< trainlabels2.size();++lb) { - for (int32_t nc=0; nc< (std::static_pointer_cast(m_labels))->get_num_classes();++nc) + for (int32_t nc=0; nc< (std::static_pointer_cast(labs))->get_num_classes();++nc) { auto sm=svm->get_svm(nc); @@ -297,13 +297,13 @@ float64_t MKLMulticlass::getsumofsignfreealphas() } float64_t MKLMulticlass::getsquarenormofprimalcoefficients( - const int32_t ind) + const int32_t ind, const std::shared_ptr& labs) { auto ker=std::dynamic_pointer_cast(m_kernel)->get_kernel(ind); float64_t tmp=0; - for (int32_t classindex=0; classindex< (std::static_pointer_cast(m_labels))->get_num_classes(); + for (int32_t classindex=0; classindex< (std::static_pointer_cast(labs))->get_num_classes(); ++classindex) { auto sm=svm->get_svm(classindex); @@ -332,22 +332,22 @@ float64_t MKLMulticlass::getsquarenormofprimalcoefficients( } -bool MKLMulticlass::train_machine(std::shared_ptr data) +bool MKLMulticlass::train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) { ASSERT(m_kernel) - ASSERT(m_labels && m_labels->get_num_labels()) - ASSERT(m_labels->get_label_type() == LT_MULTICLASS) - init_strategy(); + ASSERT(labs && labs->get_num_labels()) + ASSERT(labs->get_label_type() == LT_MULTICLASS) + init_strategy(labs); - int numcl=(std::static_pointer_cast(m_labels))->get_num_classes(); + int numcl=(std::static_pointer_cast(labs))->get_num_classes(); if (data) { - if (m_labels->get_num_labels() != data->get_num_vectors()) + if (labs->get_num_labels() != data->get_num_vectors()) { error("{}::train_machine(): Number of training vectors ({}) does" " not match number of labels ({})\n", get_name(), - data->get_num_vectors(), m_labels->get_num_labels()); + data->get_num_vectors(), labs->get_num_labels()); } m_kernel->init(data, data); } @@ -362,7 +362,7 @@ bool MKLMulticlass::train_machine(std::shared_ptr data) ::std::vector curweights(numkernels,1.0/numkernels); weightshistory.push_back(curweights); - addingweightsstep(curweights); + addingweightsstep(curweights, data, labs); oldalphaterm=curalphaterm; oldnormweightssquared=normweightssquared; @@ -377,7 +377,7 @@ bool MKLMulticlass::train_machine(std::shared_ptr data) lpw->computeweights(curweights); weightshistory.push_back(curweights); - addingweightsstep(curweights); + addingweightsstep(curweights, data, labs); //new weights new biasterm diff --git a/src/shogun/classifier/mkl/MKLMulticlass.h b/src/shogun/classifier/mkl/MKLMulticlass.h index 652c7b553ed..566a6643aee 100644 --- a/src/shogun/classifier/mkl/MKLMulticlass.h +++ b/src/shogun/classifier/mkl/MKLMulticlass.h @@ -42,7 +42,7 @@ class MKLMulticlass : public MulticlassSVM * @param k kernel * @param lab labels */ - MKLMulticlass(float64_t C, std::shared_ptr k, std::shared_ptr lab); + MKLMulticlass(float64_t C, std::shared_ptr k ); /** Class default Destructor */ ~MKLMulticlass() override; @@ -109,7 +109,7 @@ class MKLMulticlass : public MulticlassSVM /** inits the underlying Multiclass SVM * */ - void initsvm(); + void initsvm( const std::shared_ptr& labs); /** checks MKL for convergence @@ -130,13 +130,14 @@ class MKLMulticlass : public MulticlassSVM * and * float64_t getsumofsignfreealphas(); */ - void addingweightsstep( const std::vector & curweights); + void addingweightsstep( const std::vector & curweights, + const std::shared_ptr& data, const std::shared_ptr& labs); /** computes the first svm-dependent part used for generating MKL constraints * it is * \f$ \sum_y b_y^2-\sum_i \sum_{ y | y \neq y_i} \alpha_{iy}(b_{y_i}-b_y-1) \f$ */ - float64_t getsumofsignfreealphas(); + float64_t getsumofsignfreealphas( const std::shared_ptr& labs); /** computes the second svm-dependent part used for generating MKL * constraints @@ -145,7 +146,7 @@ class MKLMulticlass : public MulticlassSVM * to compute \f$ \|w \|^2 \f$ */ float64_t getsquarenormofprimalcoefficients( - const int32_t ind); + const int32_t ind, const std::shared_ptr& labs); /** train Multiclass MKL classifier * @@ -155,7 +156,7 @@ class MKLMulticlass : public MulticlassSVM * * @return whether training was successful */ - bool train_machine(std::shared_ptr data=NULL) override; + bool train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) override; /** @return object name */ const char* get_name() const override { return "MKLMulticlass"; } diff --git a/src/shogun/classifier/svm/LibLinear.cpp b/src/shogun/classifier/svm/LibLinear.cpp index 53648c0b964..f94cfc5ac90 100644 --- a/src/shogun/classifier/svm/LibLinear.cpp +++ b/src/shogun/classifier/svm/LibLinear.cpp @@ -68,6 +68,11 @@ LibLinear::~LibLinear() { } +bool LibLinear::train(const std::shared_ptr& data, const std::shared_ptr& labs) +{ + return train_machine(data->as(), labs); +} + bool LibLinear::train_machine( const std::shared_ptr& features, const std::shared_ptr& labs) { diff --git a/src/shogun/classifier/svm/LibLinear.h b/src/shogun/classifier/svm/LibLinear.h index 4bfa01de6fb..fd4fd89e8bf 100644 --- a/src/shogun/classifier/svm/LibLinear.h +++ b/src/shogun/classifier/svm/LibLinear.h @@ -210,6 +210,8 @@ namespace shogun return true; } + bool train(const std::shared_ptr& data, const std::shared_ptr& labs) override; + protected: /** train linear SVM classifier * diff --git a/src/shogun/classifier/svm/SVM.cpp b/src/shogun/classifier/svm/SVM.cpp index c8cdb086592..2b1c8f296fd 100644 --- a/src/shogun/classifier/svm/SVM.cpp +++ b/src/shogun/classifier/svm/SVM.cpp @@ -34,6 +34,14 @@ SVM::SVM(float64_t C, std::shared_ptr k, std::shared_ptr lab) set_kernel(std::move(k)); } +SVM::SVM(float64_t C, std::shared_ptr k) +: KernelMachine() +{ + set_defaults(); + set_C(C,C); + set_kernel(std::move(k)); +} + SVM::~SVM() { diff --git a/src/shogun/classifier/svm/SVM.h b/src/shogun/classifier/svm/SVM.h index 648f2b806bf..57720acec01 100644 --- a/src/shogun/classifier/svm/SVM.h +++ b/src/shogun/classifier/svm/SVM.h @@ -64,6 +64,8 @@ class SVM : public KernelMachine */ SVM(float64_t C, std::shared_ptr k, std::shared_ptr lab); + SVM(float64_t C, std::shared_ptr k); + ~SVM() override; /** set default values for members a SVM object diff --git a/src/shogun/machine/DirectorLinearMachine.h b/src/shogun/machine/DirectorLinearMachine.h index e05cb45d86c..d969f11a2a6 100644 --- a/src/shogun/machine/DirectorLinearMachine.h +++ b/src/shogun/machine/DirectorLinearMachine.h @@ -74,24 +74,6 @@ IGNORE_IN_CLASSLIST class DirectorLinearMachine : public LinearMachine return LinearMachine::apply_one(features, vec_idx); } - /** set labels - * - * @param lab labels - */ - virtual void set_labels(std::shared_ptr lab) - { - LinearMachine::set_labels(lab); - } - - /** get labels - * - * @return labels - */ - virtual std::shared_ptr get_labels() - { - return LinearMachine::get_labels(); - } - /** get classifier type * * @return classifier type NONE diff --git a/src/shogun/machine/KernelMulticlassMachine.cpp b/src/shogun/machine/KernelMulticlassMachine.cpp index e25f3966393..08ebc0bfb90 100644 --- a/src/shogun/machine/KernelMulticlassMachine.cpp +++ b/src/shogun/machine/KernelMulticlassMachine.cpp @@ -84,8 +84,8 @@ KernelMulticlassMachine::KernelMulticlassMachine() : MulticlassMachine(), m_kern * @param machine kernel machine * @param labs labels */ -KernelMulticlassMachine::KernelMulticlassMachine(std::shared_ptrstrategy, std::shared_ptr kernel, std::shared_ptr machine, std::shared_ptr labs) : - MulticlassMachine(std::move(strategy),std::move(machine),std::move(labs)), m_kernel(NULL) +KernelMulticlassMachine::KernelMulticlassMachine(std::shared_ptrstrategy, std::shared_ptr kernel, std::shared_ptr machine ) : + MulticlassMachine(std::move(strategy),std::move(machine)), m_kernel(NULL) { set_kernel(std::move(kernel)); SG_ADD(&m_kernel,"kernel", "The kernel to be used", ParameterProperties::HYPER); diff --git a/src/shogun/machine/KernelMulticlassMachine.h b/src/shogun/machine/KernelMulticlassMachine.h index ac50837495a..448d32e3103 100644 --- a/src/shogun/machine/KernelMulticlassMachine.h +++ b/src/shogun/machine/KernelMulticlassMachine.h @@ -32,7 +32,7 @@ class KernelMulticlassMachine : public MulticlassMachine * @param machine kernel machine * @param labs labels */ - KernelMulticlassMachine(std::shared_ptrstrategy, std::shared_ptr kernel, std::shared_ptr machine, std::shared_ptr labs); + KernelMulticlassMachine(std::shared_ptrstrategy, std::shared_ptr kernel, std::shared_ptr machine ); /** destructor */ ~KernelMulticlassMachine() override; diff --git a/src/shogun/machine/LinearMulticlassMachine.h b/src/shogun/machine/LinearMulticlassMachine.h index ce7ff009cb3..6c9c3938748 100644 --- a/src/shogun/machine/LinearMulticlassMachine.h +++ b/src/shogun/machine/LinearMulticlassMachine.h @@ -30,20 +30,16 @@ class LinearMulticlassMachine : public MulticlassMachine /** default constructor */ LinearMulticlassMachine() : MulticlassMachine() { - SG_ADD(&m_features, "m_features", "Feature object."); + } /** standard constructor * @param strategy multiclass strategy - * @param features features * @param machine linear machine - * @param labs labels */ - LinearMulticlassMachine(std::shared_ptr strategy, std::shared_ptr features, std::shared_ptr machine, std::shared_ptr labs) : - MulticlassMachine(strategy, machine,labs) + LinearMulticlassMachine(std::shared_ptr strategy, std::shared_ptr machine ) : + MulticlassMachine(strategy, machine) { - set_features(features->as()); - SG_ADD(&m_features, "m_features", "Feature object."); } /** destructor */ @@ -57,54 +53,45 @@ class LinearMulticlassMachine : public MulticlassMachine return "LinearMulticlassMachine"; } - /** set features - * - * @param f features - */ - void set_features(std::shared_ptr f) - { - m_features = f; + virtual int32_t get_num_classes() const { + return m_num_classes; } - /** get features - * - * @return features - */ - std::shared_ptr get_features() const - { - return m_features; + virtual int32_t get_dim_feature_space() const{ + return m_dim_feature_space; } protected: - bool train_machine(std::shared_ptr data) override + bool train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) override { - m_features = data->as(); + m_num_vectors = data->get_num_vectors(); + m_num_classes = multiclass_labels(labs)->get_num_classes(); + m_dim_feature_space = data->as()->get_dim_feature_space(); + require(m_multiclass_strategy, "Multiclass strategy not set"); - int32_t num_classes = m_labels->as()->get_num_classes(); + int32_t num_classes = labs->as()->get_num_classes(); m_multiclass_strategy->set_num_classes(num_classes); m_machines.clear(); auto train_labels = std::make_shared(get_num_rhs_vectors()); - m_multiclass_strategy->train_start( - multiclass_labels(m_labels), train_labels); + multiclass_labels(labs), train_labels); while (m_multiclass_strategy->train_has_more()) { SGVector subset=m_multiclass_strategy->train_prepare_next(); if (subset.vlen) { train_labels->add_subset(subset); - add_machine_subset(subset); + data->add_subset(subset); } - m_machine->train(data, train_labels); m_machines.push_back(get_machine_from_trained(m_machine)); if (subset.vlen) { train_labels->remove_subset(); - remove_machine_subset(); + data->remove_subset(); } } @@ -116,31 +103,21 @@ class LinearMulticlassMachine : public MulticlassMachine /** init machine for train with setting features */ bool init_machine_for_train(std::shared_ptr data) override { - if (!m_machine) - error("No machine given in Multiclass constructor"); - - if (data) - set_features(data->as()); - + require(m_machine, "No machine given in Multiclass constructor"); return true; } /** init machines for applying with setting features */ bool init_machines_for_apply(std::shared_ptr data) override { - if (data) - set_features(data->as()); - return true; } /** check features availability */ bool is_ready() override { - if (m_features) - return true; + return true; - return false; } /** construct linear machine from given linear machine */ @@ -152,7 +129,7 @@ class LinearMulticlassMachine : public MulticlassMachine /** get number of rhs feature vectors */ int32_t get_num_rhs_vectors() const override { - return m_features->get_num_vectors(); + return m_num_vectors; } /** set subset to the features of the machine, deletes old one @@ -161,23 +138,19 @@ class LinearMulticlassMachine : public MulticlassMachine */ void add_machine_subset(SGVector subset) override { - /* changing the subset structure to use subset stacks. This might - * have to be revised. Heiko Strathmann */ - m_features->add_subset(subset); + } /** deletes any subset set to the features of the machine */ void remove_machine_subset() override { - /* changing the subset structure to use subset stacks. This might - * have to be revised. Heiko Strathmann */ - m_features->remove_subset(); + } protected: - - /** features */ - std::shared_ptr m_features; + int32_t m_num_vectors; + int32_t m_dim_feature_space; + int32_t m_num_classes; }; } #endif diff --git a/src/shogun/machine/Machine.cpp b/src/shogun/machine/Machine.cpp index a54f7940236..cc07f2e294f 100644 --- a/src/shogun/machine/Machine.cpp +++ b/src/shogun/machine/Machine.cpp @@ -74,6 +74,10 @@ bool Machine::train(std::shared_ptr data) bool Machine::train( const std::shared_ptr& data, const std::shared_ptr& labs) { + require(data->get_num_vectors() == labs->get_num_labels(), + "Number of training vectors ({}) does not match number of " + "labels ({})", + data->get_num_vectors(), labs->get_num_labels()); auto sub = connect_to_signal_handler(); bool result = false; diff --git a/src/shogun/machine/Machine.h b/src/shogun/machine/Machine.h index 91168e8b915..682baaeabd7 100644 --- a/src/shogun/machine/Machine.h +++ b/src/shogun/machine/Machine.h @@ -269,11 +269,6 @@ class Machine : public StoppableSGObject virtual bool train_machine( const std::shared_ptr& data, const std::shared_ptr& labs) { - require(data->get_num_vectors() == labs->get_num_labels(), - "Number of training vectors ({}) does not match number of " - "labels ({})", - data->get_num_vectors(), labs->get_num_labels()); - error("train_machine is not yet implemented for {}!", get_name()); return false; } diff --git a/src/shogun/machine/MulticlassMachine.cpp b/src/shogun/machine/MulticlassMachine.cpp index b0e8c3b2ee4..a54088af470 100644 --- a/src/shogun/machine/MulticlassMachine.cpp +++ b/src/shogun/machine/MulticlassMachine.cpp @@ -27,11 +27,9 @@ MulticlassMachine::MulticlassMachine() MulticlassMachine::MulticlassMachine( std::shared_ptrstrategy, - std::shared_ptr machine, std::shared_ptr labs) + std::shared_ptr machine ) : BaseMulticlassMachine(), m_multiclass_strategy(std::move(strategy)) { - set_labels(std::move(labs)); - m_machine = std::move(machine); register_parameters(); } @@ -40,20 +38,15 @@ MulticlassMachine::~MulticlassMachine() { } -void MulticlassMachine::set_labels(std::shared_ptr lab) -{ - Machine::set_labels(lab); -} - void MulticlassMachine::register_parameters() { SG_ADD(&m_multiclass_strategy,"multiclass_strategy", "Multiclass strategy"); SG_ADD(&m_machine, "machine", "The base machine"); } -void MulticlassMachine::init_strategy() +void MulticlassMachine::init_strategy( const std::shared_ptr& labs) { - int32_t num_classes = m_labels->as()->get_num_classes(); + int32_t num_classes = labs->as()->get_num_classes(); m_multiclass_strategy->set_num_classes(num_classes); } @@ -76,7 +69,7 @@ std::shared_ptr MulticlassMachine::apply_multiclass(std::share SG_TRACE("entering {}::apply_multiclass({} at {})", get_name(), data ? data->get_name() : "NULL", fmt::ptr(data.get())); - std::shared_ptr return_labels=NULL; + std::shared_ptr return_labels; if (data) init_machines_for_apply(data); @@ -209,10 +202,10 @@ std::shared_ptr MulticlassMachine::apply_multilabel_output(std return return_labels; } -bool MulticlassMachine::train_machine(std::shared_ptr data) +bool MulticlassMachine::train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) { ASSERT(m_multiclass_strategy) - init_strategy(); + init_strategy(labs); if ( !data && !is_ready() ) error("Please provide training data."); @@ -225,7 +218,7 @@ bool MulticlassMachine::train_machine(std::shared_ptr data) m_machine->set_labels(train_labels); m_multiclass_strategy->train_start( - multiclass_labels(m_labels), train_labels); + multiclass_labels(labs), train_labels); while (m_multiclass_strategy->train_has_more()) { SGVector subset=m_multiclass_strategy->train_prepare_next(); diff --git a/src/shogun/machine/MulticlassMachine.h b/src/shogun/machine/MulticlassMachine.h index 1fc92670b00..6b11735a54c 100644 --- a/src/shogun/machine/MulticlassMachine.h +++ b/src/shogun/machine/MulticlassMachine.h @@ -36,17 +36,11 @@ class MulticlassMachine : public BaseMulticlassMachine * @param machine machine * @param labels labels */ - MulticlassMachine(std::shared_ptr strategy, std::shared_ptr machine, std::shared_ptr labels); + MulticlassMachine(std::shared_ptr strategy, std::shared_ptr machine ); /** destructor */ ~MulticlassMachine() override; - /** set labels - * - * @param lab labels - */ - void set_labels(std::shared_ptr lab) override; - /** set machine * * @param num index of machine @@ -91,13 +85,13 @@ class MulticlassMachine : public BaseMulticlassMachine * * @return resulting labels */ - std::shared_ptr apply_multiclass(std::shared_ptr data=NULL) override; + std::shared_ptr apply_multiclass(std::shared_ptr data) override; /** classify all examples with multiple output * * @return resulting labels */ - virtual std::shared_ptr apply_multilabel_output(std::shared_ptr data=NULL, int32_t n_outputs=5); + virtual std::shared_ptr apply_multilabel_output(std::shared_ptr data, int32_t n_outputs=5); /** classify one example * @param vec_idx @@ -155,13 +149,13 @@ class MulticlassMachine : public BaseMulticlassMachine protected: /** init strategy */ - void init_strategy(); + void init_strategy( const std::shared_ptr& labs); /** clear machines */ void clear_machines(); /** train machine */ - bool train_machine(std::shared_ptr data = NULL) override; + bool train_machine(const std::shared_ptr&, const std::shared_ptr& labs) override; /** abstract init machine for training method */ virtual bool init_machine_for_train(std::shared_ptr data) = 0; diff --git a/src/shogun/mathematics/Seedable.h b/src/shogun/mathematics/Seedable.h index 1bd02e7c990..d6fd90b294e 100644 --- a/src/shogun/mathematics/Seedable.h +++ b/src/shogun/mathematics/Seedable.h @@ -13,7 +13,7 @@ namespace shogun #ifndef SWIG static constexpr std::string_view kSetRandomSeed = "set_random_seed"; static constexpr std::string_view kSeed = "seed"; -#endif // SWIG +#endif // SWIG /** Seeds an SGObject using a specific seed */ template < @@ -46,7 +46,6 @@ namespace shogun { return "Seedable"; } - protected: /** Seeds an SGObject using the current object seed * This is intended to seed non-parameter SGObjects created inside diff --git a/src/shogun/multiclass/GMNPSVM.cpp b/src/shogun/multiclass/GMNPSVM.cpp index eec13580a2c..f6878f08e36 100644 --- a/src/shogun/multiclass/GMNPSVM.cpp +++ b/src/shogun/multiclass/GMNPSVM.cpp @@ -26,8 +26,8 @@ GMNPSVM::GMNPSVM() init(); } -GMNPSVM::GMNPSVM(float64_t C, std::shared_ptr k, std::shared_ptr lab) -: MulticlassSVM(std::make_shared(), C, std::move(k), std::move(lab)) +GMNPSVM::GMNPSVM(float64_t C, std::shared_ptr k ) +: MulticlassSVM(std::make_shared(), C, std::move(k) ) { init(); } @@ -50,32 +50,32 @@ GMNPSVM::init() m_basealphas = NULL, m_basealphas_y = 0, m_basealphas_x = 0; } -bool GMNPSVM::train_machine(std::shared_ptr data) +bool GMNPSVM::train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) { ASSERT(m_kernel) - ASSERT(m_labels && m_labels->get_num_labels()) - ASSERT(m_labels->get_label_type() == LT_MULTICLASS) - init_strategy(); + ASSERT(labs && labs->get_num_labels()) + ASSERT(labs->get_label_type() == LT_MULTICLASS) + init_strategy(labs); if (data) { - if (m_labels->get_num_labels() != data->get_num_vectors()) + if (labs->get_num_labels() != data->get_num_vectors()) { error("{}::train_machine(): Number of training vectors ({}) does" " not match number of labels ({})", get_name(), - data->get_num_vectors(), m_labels->get_num_labels()); + data->get_num_vectors(), labs->get_num_labels()); } m_kernel->init(data, data); } - int32_t num_data = m_labels->get_num_labels(); + int32_t num_data = labs->get_num_labels(); int32_t num_classes = m_multiclass_strategy->get_num_classes(); int32_t num_virtual_data= num_data*(num_classes-1); io::info("{} trainlabels, {} classes", num_data, num_classes); float64_t* vector_y = SG_MALLOC(float64_t, num_data); - auto mc = multiclass_labels(m_labels); + auto mc = multiclass_labels(labs); for (int32_t i=0; iget_label(i)+1; diff --git a/src/shogun/multiclass/GMNPSVM.h b/src/shogun/multiclass/GMNPSVM.h index 645027b9593..60f3d4ef401 100644 --- a/src/shogun/multiclass/GMNPSVM.h +++ b/src/shogun/multiclass/GMNPSVM.h @@ -33,7 +33,7 @@ class GMNPSVM : public MulticlassSVM * @param k kernel * @param lab labels */ - GMNPSVM(float64_t C, std::shared_ptr k, std::shared_ptr lab); + GMNPSVM(float64_t C, std::shared_ptr k ); /** default destructor */ ~GMNPSVM() override; @@ -67,7 +67,7 @@ class GMNPSVM : public MulticlassSVM * * @return whether training was successful */ - bool train_machine(std::shared_ptr data=NULL) override; + bool train_machine(const std::shared_ptr&, const std::shared_ptr& labs) override; protected: /** required for MKLMulticlass diff --git a/src/shogun/multiclass/GaussianNaiveBayes.cpp b/src/shogun/multiclass/GaussianNaiveBayes.cpp index 61881d3e851..94ba8303c42 100644 --- a/src/shogun/multiclass/GaussianNaiveBayes.cpp +++ b/src/shogun/multiclass/GaussianNaiveBayes.cpp @@ -59,7 +59,7 @@ void GaussianNaiveBayes::set_features(std::shared_ptr features) m_features = features->as(); } -bool GaussianNaiveBayes::train_machine(std::shared_ptr data) +bool GaussianNaiveBayes::train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) { // init features with data if necessary and assure type is correct if (data) @@ -70,9 +70,9 @@ bool GaussianNaiveBayes::train_machine(std::shared_ptr data) } // get int labels to train_labels and check length equality - ASSERT(m_labels) + ASSERT(labs) SGVector train_labels = - multiclass_labels(m_labels)->get_int_labels(); + multiclass_labels(labs)->get_int_labels(); ASSERT(m_features->get_num_vectors()==train_labels.vlen) // find minimal and maximal label diff --git a/src/shogun/multiclass/GaussianNaiveBayes.h b/src/shogun/multiclass/GaussianNaiveBayes.h index d2fab8c5d7a..fb3280942e2 100644 --- a/src/shogun/multiclass/GaussianNaiveBayes.h +++ b/src/shogun/multiclass/GaussianNaiveBayes.h @@ -91,7 +91,7 @@ class GaussianNaiveBayes : public NativeMulticlassMachine * @param data train examples * @return true if successful */ - bool train_machine(std::shared_ptr data=NULL) override; + bool train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) override; private: void init(); diff --git a/src/shogun/multiclass/MCLDA.cpp b/src/shogun/multiclass/MCLDA.cpp index bc7de2ee8cc..2105534dccd 100644 --- a/src/shogun/multiclass/MCLDA.cpp +++ b/src/shogun/multiclass/MCLDA.cpp @@ -31,7 +31,7 @@ MCLDA::MCLDA(float64_t tolerance, bool store_cov) } -MCLDA::MCLDA(const std::shared_ptr>& traindat, std::shared_ptr trainlab, float64_t tolerance, bool store_cov) +MCLDA::MCLDA(const std::shared_ptr>& traindat, float64_t tolerance, bool store_cov) : NativeMulticlassMachine() { init(); @@ -40,7 +40,6 @@ MCLDA::MCLDA(const std::shared_ptr>& traindat, std::sha m_store_cov=store_cov; set_features(traindat); - set_labels(std::move(trainlab)); } MCLDA::~MCLDA() @@ -149,9 +148,9 @@ std::shared_ptr MCLDA::apply_multiclass(std::shared_ptr data) +bool MCLDA::train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) { - if (!m_labels) + if (!labs) error("No labels allocated in MCLDA training"); if (data) @@ -165,14 +164,14 @@ bool MCLDA::train_machine(std::shared_ptr data) if (!m_features) error("No features allocated in MCLDA training"); - SGVector< int32_t > train_labels = multiclass_labels(m_labels)->get_int_labels(); + SGVector< int32_t > train_labels = multiclass_labels(labs)->get_int_labels(); if (!train_labels.vector) error("No train_labels allocated in MCLDA training"); cleanup(); - m_num_classes = multiclass_labels(m_labels)->get_num_classes(); + m_num_classes = multiclass_labels(labs)->get_num_classes(); m_dim = m_features->get_dim_feature_space(); int32_t num_vec = m_features->get_num_vectors(); diff --git a/src/shogun/multiclass/MCLDA.h b/src/shogun/multiclass/MCLDA.h index 6ca77fc0011..5dd67a69eba 100644 --- a/src/shogun/multiclass/MCLDA.h +++ b/src/shogun/multiclass/MCLDA.h @@ -48,7 +48,7 @@ class MCLDA : public NativeMulticlassMachine * @param tolerance tolerance used in training * @param store_cov whether to store the within class covariances */ - MCLDA(const std::shared_ptr>& traindat, std::shared_ptr trainlab, float64_t tolerance = 1e-4, bool store_cov = false); + MCLDA(const std::shared_ptr>& traindat, float64_t tolerance = 1e-4, bool store_cov = false); ~MCLDA() override; @@ -131,7 +131,7 @@ class MCLDA : public NativeMulticlassMachine * * @return whether training was successful */ - bool train_machine(std::shared_ptr data = NULL) override; + bool train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) override; private: void init(); diff --git a/src/shogun/multiclass/MulticlassLibLinear.cpp b/src/shogun/multiclass/MulticlassLibLinear.cpp index bf976ff4fd2..9295571adaf 100644 --- a/src/shogun/multiclass/MulticlassLibLinear.cpp +++ b/src/shogun/multiclass/MulticlassLibLinear.cpp @@ -24,8 +24,8 @@ MulticlassLibLinear::MulticlassLibLinear() : init_defaults(); } -MulticlassLibLinear::MulticlassLibLinear(float64_t C, std::shared_ptr features, std::shared_ptr labs) : - RandomMixin(std::make_shared(),std::move(features),nullptr,std::move(labs)) +MulticlassLibLinear::MulticlassLibLinear(float64_t C) : + RandomMixin(std::make_shared(), nullptr) { register_parameters(); init_defaults(); @@ -60,19 +60,14 @@ SGVector MulticlassLibLinear::get_support_vectors() const if (!m_train_state) error("Please enable save_train_state option and train machine."); - ASSERT(m_labels && m_labels->get_label_type() == LT_MULTICLASS) - - int32_t num_vectors = m_features->get_num_vectors(); - int32_t num_classes = multiclass_labels(m_labels)->get_num_classes(); - v_array nz_idxs; - nz_idxs.reserve(num_vectors); + nz_idxs.reserve(m_num_vectors); - for (int32_t i=0; ialpha[i*num_classes+y])>1e-6) + if (Math::abs(m_train_state->alpha[i*m_num_classes+y])>1e-6) { nz_idxs.push(i); break; @@ -89,28 +84,23 @@ SGMatrix MulticlassLibLinear::obtain_regularizer_matrix() const return SGMatrix(); } -bool MulticlassLibLinear::train_machine(std::shared_ptr data) +bool MulticlassLibLinear::train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) { - if (data) - set_features(data->as()); - - ASSERT(m_features) - ASSERT(m_labels && m_labels->get_label_type()==LT_MULTICLASS) - ASSERT(m_multiclass_strategy) - init_strategy(); - - int32_t num_vectors = m_features->get_num_vectors(); - int32_t num_classes = multiclass_labels(m_labels)->get_num_classes(); + require(m_multiclass_strategy, "Multiclass strategy not set"); + init_strategy(labs); + auto feats = data->as(); + m_num_vectors = data->get_num_vectors(); + m_num_classes = multiclass_labels(labs)->get_num_classes(); int32_t bias_n = m_use_bias ? 1 : 0; liblinear_problem mc_problem; - mc_problem.l = num_vectors; - mc_problem.n = m_features->get_dim_feature_space() + bias_n; + mc_problem.l = m_num_vectors; + mc_problem.n = feats->get_dim_feature_space() + bias_n; mc_problem.y = SG_MALLOC(float64_t, mc_problem.l); - for (int32_t i=0; iget_int_label(i); + for (int32_t i=0; iget_int_label(i); - mc_problem.x = m_features; + mc_problem.x = feats; mc_problem.use_bias = m_use_bias; SGMatrix w0 = obtain_regularizer_matrix(); @@ -118,27 +108,27 @@ bool MulticlassLibLinear::train_machine(std::shared_ptr data) if (!m_train_state) m_train_state = new mcsvm_state(); - float64_t* C = SG_MALLOC(float64_t, num_vectors); - for (int32_t i=0; i(); SGVector cw(mc_problem.n-bias_n); for (int32_t j=0; jw[j*num_classes+i]; + cw[j] = m_train_state->w[j*m_num_classes+i]; machine->set_w(cw); if (m_use_bias) - machine->set_bias(m_train_state->w[(mc_problem.n-bias_n)*num_classes+i]); + machine->set_bias(m_train_state->w[(mc_problem.n-bias_n)*m_num_classes+i]); m_machines.push_back(machine); } diff --git a/src/shogun/multiclass/MulticlassLibLinear.h b/src/shogun/multiclass/MulticlassLibLinear.h index c60128059c7..b59c51c3291 100644 --- a/src/shogun/multiclass/MulticlassLibLinear.h +++ b/src/shogun/multiclass/MulticlassLibLinear.h @@ -45,7 +45,7 @@ class MulticlassLibLinear : public RandomMixin * @param features features * @param labs labels */ - MulticlassLibLinear(float64_t C, std::shared_ptr features, std::shared_ptr labs); + MulticlassLibLinear(float64_t C); /** destructor */ ~MulticlassLibLinear() override; @@ -143,7 +143,7 @@ class MulticlassLibLinear : public RandomMixin protected: /** train machine */ - bool train_machine(std::shared_ptr data = NULL) override; + bool train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) override; /** obtain regularizer (w0) matrix */ virtual SGMatrix obtain_regularizer_matrix() const; diff --git a/src/shogun/multiclass/MulticlassLibSVM.cpp b/src/shogun/multiclass/MulticlassLibSVM.cpp index 57bb16cc304..7a543a3db49 100644 --- a/src/shogun/multiclass/MulticlassLibSVM.cpp +++ b/src/shogun/multiclass/MulticlassLibSVM.cpp @@ -19,8 +19,8 @@ MulticlassLibSVM::MulticlassLibSVM(LIBSVM_SOLVER_TYPE st) { } -MulticlassLibSVM::MulticlassLibSVM(float64_t C, std::shared_ptr k, std::shared_ptr lab) -: MulticlassSVM(std::make_shared(), C, std::move(k), std::move(lab)), solver_type(LIBSVM_C_SVC) +MulticlassLibSVM::MulticlassLibSVM(float64_t C, std::shared_ptr k ) +: MulticlassSVM(std::make_shared(), C, std::move(k) ), solver_type(LIBSVM_C_SVC) { } @@ -36,7 +36,7 @@ void MulticlassLibSVM::register_params() SG_OPTIONS(LIBSVM_C_SVC, LIBSVM_NU_SVC)); } -bool MulticlassLibSVM::train_machine(std::shared_ptr data) +bool MulticlassLibSVM::train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) { svm_problem problem; svm_parameter param; @@ -46,17 +46,17 @@ bool MulticlassLibSVM::train_machine(std::shared_ptr data) problem = svm_problem(); - ASSERT(m_labels && m_labels->get_num_labels()) - ASSERT(m_labels->get_label_type() == LT_MULTICLASS) - init_strategy(); + ASSERT(labs && labs->get_num_labels()) + ASSERT(labs->get_label_type() == LT_MULTICLASS) + init_strategy(labs); int32_t num_classes = m_multiclass_strategy->get_num_classes(); - problem.l=m_labels->get_num_labels(); + problem.l=labs->get_num_labels(); io::info("{} trainlabels, {} classes", problem.l, num_classes); if (data) { - if (m_labels->get_num_labels() != data->get_num_vectors()) + if (labs->get_num_labels() != data->get_num_vectors()) { error("Number of training vectors does not match number of " "labels"); @@ -74,7 +74,7 @@ bool MulticlassLibSVM::train_machine(std::shared_ptr data) for (int32_t i=0; iget_label(i); + problem.y[i]=multiclass_labels(labs)->get_label(i); problem.x[i]=&x_space[2*i]; x_space[2*i].index=i; x_space[2*i+1].index=-1; diff --git a/src/shogun/multiclass/MulticlassLibSVM.h b/src/shogun/multiclass/MulticlassLibSVM.h index 095c561eaf3..1bca4776b75 100644 --- a/src/shogun/multiclass/MulticlassLibSVM.h +++ b/src/shogun/multiclass/MulticlassLibSVM.h @@ -30,7 +30,7 @@ class MulticlassLibSVM : public MulticlassSVM * @param k kernel * @param lab labels */ - MulticlassLibSVM(float64_t C, std::shared_ptr k, std::shared_ptr lab); + MulticlassLibSVM(float64_t C, std::shared_ptr k ); /** destructor */ ~MulticlassLibSVM() override; @@ -53,7 +53,7 @@ class MulticlassLibSVM : public MulticlassSVM * * @return whether training was successful */ - bool train_machine(std::shared_ptr data=NULL) override; + bool train_machine(const std::shared_ptr&, const std::shared_ptr& labs) override; private: void register_params(); diff --git a/src/shogun/multiclass/MulticlassOCAS.cpp b/src/shogun/multiclass/MulticlassOCAS.cpp index d492deda9b2..b519c0b57e5 100644 --- a/src/shogun/multiclass/MulticlassOCAS.cpp +++ b/src/shogun/multiclass/MulticlassOCAS.cpp @@ -40,8 +40,8 @@ MulticlassOCAS::MulticlassOCAS() : set_buf_size(5000); } -MulticlassOCAS::MulticlassOCAS(float64_t C, const std::shared_ptr& train_features, std::shared_ptr train_labels) : - LinearMulticlassMachine(std::make_shared(), train_features->as(), NULL, std::move(train_labels)), m_C(C) +MulticlassOCAS::MulticlassOCAS(float64_t C) : + LinearMulticlassMachine(std::make_shared(), NULL ), m_C(C) { register_parameters(); set_epsilon(1e-2); @@ -65,22 +65,18 @@ MulticlassOCAS::~MulticlassOCAS() { } -bool MulticlassOCAS::train_machine(std::shared_ptr data) +bool MulticlassOCAS::train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) { - if (data) - set_features(data->as()); - ASSERT(m_features) - ASSERT(m_labels) - ASSERT(m_multiclass_strategy) - init_strategy(); - - int32_t num_vectors = m_features->get_num_vectors(); + require(m_multiclass_strategy, "Multiclass strategy not set"); + init_strategy(labs); + auto feats = data->as(); + int32_t num_vectors = feats->get_num_vectors(); int32_t num_classes = m_multiclass_strategy->get_num_classes(); - int32_t num_features = m_features->get_dim_feature_space(); + int32_t num_features = feats->get_dim_feature_space(); float64_t C = m_C; - SGVector labels = multiclass_labels(m_labels)->get_labels(); + SGVector labels = multiclass_labels(labs)->get_labels(); uint32_t nY = num_classes; uint32_t nData = num_vectors; float64_t TolRel = m_epsilon; @@ -91,7 +87,7 @@ bool MulticlassOCAS::train_machine(std::shared_ptr data) uint8_t Method = m_method; mocas_data user_data; - user_data.features = m_features; + user_data.features = feats; user_data.W = SG_CALLOC(float64_t, (int64_t)num_features*num_classes); user_data.oldW = SG_CALLOC(float64_t, (int64_t)num_features*num_classes); user_data.new_a = SG_CALLOC(float64_t, (int64_t)num_features*num_classes); diff --git a/src/shogun/multiclass/MulticlassOCAS.h b/src/shogun/multiclass/MulticlassOCAS.h index 20b63da9e60..21a1e0dfb34 100644 --- a/src/shogun/multiclass/MulticlassOCAS.h +++ b/src/shogun/multiclass/MulticlassOCAS.h @@ -30,7 +30,7 @@ class MulticlassOCAS : public LinearMulticlassMachine * @param features features * @param labs labels */ - MulticlassOCAS(float64_t C, const std::shared_ptr& features, std::shared_ptr labs); + MulticlassOCAS(float64_t C); /** destructor */ ~MulticlassOCAS() override; @@ -109,7 +109,7 @@ class MulticlassOCAS : public LinearMulticlassMachine protected: /** train machine */ - bool train_machine(std::shared_ptr data = NULL) override; + bool train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) override; /** update W */ static float64_t msvm_update_W(float64_t t, void* user_data); diff --git a/src/shogun/multiclass/MulticlassSVM.cpp b/src/shogun/multiclass/MulticlassSVM.cpp index 31cfb63501c..dab92bc0a44 100644 --- a/src/shogun/multiclass/MulticlassSVM.cpp +++ b/src/shogun/multiclass/MulticlassSVM.cpp @@ -19,14 +19,14 @@ MulticlassSVM::MulticlassSVM() } MulticlassSVM::MulticlassSVM(std::shared_ptrstrategy) - :KernelMulticlassMachine(std::move(strategy), NULL, std::make_shared(0), NULL) + :KernelMulticlassMachine(std::move(strategy), NULL, std::make_shared(0) ) { init(); } MulticlassSVM::MulticlassSVM( - std::shared_ptrstrategy, float64_t C, std::shared_ptr k, std::shared_ptr lab) - : KernelMulticlassMachine(std::move(strategy), k, std::make_shared(C, k, lab), lab) + std::shared_ptrstrategy, float64_t C, std::shared_ptr k) + : KernelMulticlassMachine(std::move(strategy), k, std::make_shared(C, k)) { init(); m_C=C; diff --git a/src/shogun/multiclass/MulticlassSVM.h b/src/shogun/multiclass/MulticlassSVM.h index e0053e98e5e..cf2ab2bf357 100644 --- a/src/shogun/multiclass/MulticlassSVM.h +++ b/src/shogun/multiclass/MulticlassSVM.h @@ -44,7 +44,7 @@ class MulticlassSVM : public KernelMulticlassMachine * @param lab labels */ MulticlassSVM( - std::shared_ptrstrategy, float64_t C, std::shared_ptr k, std::shared_ptr lab); + std::shared_ptrstrategy, float64_t C, std::shared_ptr k ); ~MulticlassSVM() override; /** create multiclass SVM. Appends the appropriate number of svm pointer diff --git a/src/shogun/multiclass/QDA.cpp b/src/shogun/multiclass/QDA.cpp index 7b7f672bc2b..ce59a3b6f3d 100644 --- a/src/shogun/multiclass/QDA.cpp +++ b/src/shogun/multiclass/QDA.cpp @@ -36,40 +36,36 @@ QDA::QDA(float64_t tolerance, bool store_covs) m_store_covs = store_covs; } -QDA::QDA(const std::shared_ptr>& traindat, std::shared_ptr trainlab) +QDA::QDA(const std::shared_ptr>& traindat ) : NativeMulticlassMachine(), m_num_classes(0), m_dim(0) { init(); set_features(traindat); - set_labels(std::move(trainlab)); } -QDA::QDA(const std::shared_ptr>& traindat, std::shared_ptr trainlab, float64_t tolerance) +QDA::QDA(const std::shared_ptr>& traindat, float64_t tolerance) : NativeMulticlassMachine(), m_num_classes(0), m_dim(0) { init(); set_features(traindat); - set_labels(std::move(trainlab)); m_tolerance = tolerance; } -QDA::QDA(const std::shared_ptr>& traindat, std::shared_ptr trainlab, bool store_covs) +QDA::QDA(const std::shared_ptr>& traindat, bool store_covs) : NativeMulticlassMachine(), m_num_classes(0), m_dim(0) { init(); set_features(traindat); - set_labels(std::move(trainlab)); m_store_covs = store_covs; } -QDA::QDA(const std::shared_ptr>& traindat, std::shared_ptr trainlab, float64_t tolerance, bool store_covs) +QDA::QDA(const std::shared_ptr>& traindat, float64_t tolerance, bool store_covs) : NativeMulticlassMachine(), m_num_classes(0), m_dim(0) { init(); set_features(traindat); - set_labels(std::move(trainlab)); m_tolerance = tolerance; m_store_covs = store_covs; } @@ -170,9 +166,9 @@ std::shared_ptr QDA::apply_multiclass(std::shared_ptr data) +bool QDA::train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) { - if (!m_labels) + if (!labs) error("No labels allocated in QDA training"); if ( data ) @@ -186,14 +182,14 @@ bool QDA::train_machine(std::shared_ptr data) if (!m_features) error("No features allocated in QDA training"); - SGVector< int32_t > train_labels = multiclass_labels(m_labels)->get_int_labels(); + SGVector< int32_t > train_labels = multiclass_labels(labs)->get_int_labels(); if (!train_labels.vector) error("No train_labels allocated in QDA training"); cleanup(); - m_num_classes = multiclass_labels(m_labels)->get_num_classes(); + m_num_classes = multiclass_labels(labs)->get_num_classes(); m_dim = m_features->get_dim_feature_space(); int32_t num_vec = m_features->get_num_vectors(); diff --git a/src/shogun/multiclass/QDA.h b/src/shogun/multiclass/QDA.h index dcebea2c523..400c2eeb24a 100644 --- a/src/shogun/multiclass/QDA.h +++ b/src/shogun/multiclass/QDA.h @@ -49,7 +49,7 @@ class QDA : public NativeMulticlassMachine * @param traindat training features * @param trainlab labels for training features */ - QDA(const std::shared_ptr>& traindat, std::shared_ptr trainlab); + QDA(const std::shared_ptr>& traindat ); /** constructor * @@ -57,7 +57,7 @@ class QDA : public NativeMulticlassMachine * @param trainlab labels for training features * @param tolerance tolerance used in training */ - QDA(const std::shared_ptr>& traindat, std::shared_ptr trainlab, float64_t tolerance); + QDA(const std::shared_ptr>& traindat, float64_t tolerance); /** constructor * @@ -65,7 +65,7 @@ class QDA : public NativeMulticlassMachine * @param trainlab labels for training features * @param store_covs whether to store the within class covariances */ - QDA(const std::shared_ptr>& traindat, std::shared_ptr trainlab, bool store_covs); + QDA(const std::shared_ptr>& traindat, bool store_covs); /** constructor * @@ -74,7 +74,7 @@ class QDA : public NativeMulticlassMachine * @param tolerance tolerance used in training * @param store_covs whether to store the within class covariances */ - QDA(const std::shared_ptr>& traindat, std::shared_ptr trainlab, float64_t tolerance, bool store_covs); + QDA(const std::shared_ptr>& traindat, float64_t tolerance, bool store_covs); ~QDA() override; @@ -173,7 +173,7 @@ class QDA : public NativeMulticlassMachine * * @return whether training was successful */ - bool train_machine(std::shared_ptr data = NULL) override; + bool train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) override; private: void init(); diff --git a/src/shogun/multiclass/ScatterSVM.cpp b/src/shogun/multiclass/ScatterSVM.cpp index 4a7beb1d916..5183c11401d 100644 --- a/src/shogun/multiclass/ScatterSVM.cpp +++ b/src/shogun/multiclass/ScatterSVM.cpp @@ -32,8 +32,8 @@ ScatterSVM::ScatterSVM(SCATTER_TYPE type) { } -ScatterSVM::ScatterSVM(float64_t C, std::shared_ptr k, std::shared_ptr lab) -: MulticlassSVM(std::make_shared(), C, std::move(k), std::move(lab)), scatter_type(NO_BIAS_LIBSVM), +ScatterSVM::ScatterSVM(float64_t C, std::shared_ptr k ) +: MulticlassSVM(std::make_shared(), C, std::move(k) ), scatter_type(NO_BIAS_LIBSVM), norm_wc(NULL), norm_wc_len(0), norm_wcw(NULL), norm_wcw_len(0), rho(0), m_num_classes(0) { } @@ -69,18 +69,18 @@ void ScatterSVM::register_params() #endif // USE_SVMLIGHT } -bool ScatterSVM::train_machine(std::shared_ptr data) +bool ScatterSVM::train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) { - ASSERT(m_labels && m_labels->get_num_labels()) - ASSERT(m_labels->get_label_type() == LT_MULTICLASS) - init_strategy(); + ASSERT(labs && labs->get_num_labels()) + ASSERT(labs->get_label_type() == LT_MULTICLASS) + init_strategy(labs); m_num_classes = m_multiclass_strategy->get_num_classes(); - int32_t num_vectors = m_labels->get_num_labels(); + int32_t num_vectors = labs->get_num_labels(); if (data) { - if (m_labels->get_num_labels() != data->get_num_vectors()) + if (labs->get_num_labels() != data->get_num_vectors()) error("Number of training vectors does not match number of labels"); m_kernel->init(data, data); } @@ -88,7 +88,7 @@ bool ScatterSVM::train_machine(std::shared_ptr data) int32_t* numc=SG_MALLOC(int32_t, m_num_classes); SGVector::fill_vector(numc, m_num_classes, 0); - auto mc = multiclass_labels(m_labels); + auto mc = multiclass_labels(labs); for (int32_t i=0; iget_int_label(i)]++; @@ -110,12 +110,12 @@ bool ScatterSVM::train_machine(std::shared_ptr data) if (scatter_type==NO_BIAS_LIBSVM) { - result=train_no_bias_libsvm(); + result=train_no_bias_libsvm(labs); } #ifdef USE_SVMLIGHT else if (scatter_type==NO_BIAS_SVMLIGHT) { - result=train_no_bias_svmlight(); + result=train_no_bias_svmlight(labs); } #endif //USE_SVMLIGHT else if (scatter_type==TEST_RULE1 || scatter_type==TEST_RULE2) @@ -128,7 +128,7 @@ bool ScatterSVM::train_machine(std::shared_ptr data) if (get_nu()nu_max) error("nu out of valid range [{} ... {}]", nu_min, nu_max); - result=train_testrule12(); + result=train_testrule12(labs); } else error("Unknown Scatter type"); @@ -136,7 +136,7 @@ bool ScatterSVM::train_machine(std::shared_ptr data) return result; } -bool ScatterSVM::train_no_bias_libsvm() +bool ScatterSVM::train_no_bias_libsvm( const std::shared_ptr& labs) { svm_problem problem; svm_parameter param; @@ -144,7 +144,7 @@ bool ScatterSVM::train_no_bias_libsvm() struct svm_node* x_space; - problem.l=m_labels->get_num_labels(); + problem.l=labs->get_num_labels(); io::info("{} trainlabels", problem.l); problem.y=SG_MALLOC(float64_t, problem.l); @@ -173,7 +173,7 @@ bool ScatterSVM::train_no_bias_libsvm() param.nu = get_nu(); // Nu auto prev_normalizer=m_kernel->get_normalizer(); m_kernel->set_normalizer(std::make_shared( - m_num_classes-1, -1, m_labels, prev_normalizer)); + m_num_classes-1, -1, labs, prev_normalizer)); param.kernel=m_kernel.get(); param.cache_size = m_kernel->get_cache_size(); param.C = 0; @@ -246,11 +246,11 @@ bool ScatterSVM::train_no_bias_libsvm() } #ifdef USE_SVMLIGHT -bool ScatterSVM::train_no_bias_svmlight() +bool ScatterSVM::train_no_bias_svmlight( const std::shared_ptr& labs) { auto prev_normalizer=m_kernel->get_normalizer(); auto n=std::make_shared( - m_num_classes-1, -1, m_labels, prev_normalizer); + m_num_classes-1, -1, labs, prev_normalizer); m_kernel->set_normalizer(n); m_kernel->init_normalizer(); @@ -276,21 +276,21 @@ bool ScatterSVM::train_no_bias_svmlight() } #endif //USE_SVMLIGHT -bool ScatterSVM::train_testrule12() +bool ScatterSVM::train_testrule12( const std::shared_ptr& labs) { svm_problem problem; svm_parameter param; struct svm_model* model = nullptr; struct svm_node* x_space; - problem.l=m_labels->get_num_labels(); + problem.l=labs->get_num_labels(); io::info("{} trainlabels", problem.l); problem.y=SG_MALLOC(float64_t, problem.l); problem.x=SG_MALLOC(struct svm_node*, problem.l); x_space=SG_MALLOC(struct svm_node, 2*problem.l); - auto mc = multiclass_labels(m_labels); + auto mc = multiclass_labels(labs); for (int32_t i=0; iget_label(i); @@ -406,7 +406,7 @@ void ScatterSVM::compute_norm_wc() norm_wc[i] = std::sqrt(norm_wc[i]); } -std::shared_ptr ScatterSVM::classify_one_vs_rest() +std::shared_ptr ScatterSVM::classify_one_vs_rest( const std::shared_ptr& labs) { if (!m_kernel) { @@ -434,7 +434,7 @@ std::shared_ptr ScatterSVM::classify_one_vs_rest() float64_t* outputs=SG_MALLOC(float64_t, num_vectors*m_num_classes); SGVector::fill_vector(outputs,num_vectors*m_num_classes,0.0); - auto mc = multiclass_labels(m_labels); + auto mc = multiclass_labels(labs); for (int32_t i=0; iget_num_support_vectors(); j++) @@ -483,7 +483,7 @@ std::shared_ptr ScatterSVM::classify_one_vs_rest() auto svm = get_svm(i); ASSERT(svm) svm->set_kernel(m_kernel); - svm->set_labels(m_labels); + svm->set_labels(labs); outputs[i]=svm->apply(); } diff --git a/src/shogun/multiclass/ScatterSVM.h b/src/shogun/multiclass/ScatterSVM.h index 0777d2726f1..4e00c1b716c 100644 --- a/src/shogun/multiclass/ScatterSVM.h +++ b/src/shogun/multiclass/ScatterSVM.h @@ -60,7 +60,7 @@ class ScatterSVM : public MulticlassSVM * @param k kernel * @param lab labels */ - ScatterSVM(float64_t C, std::shared_ptr k, std::shared_ptr lab); + ScatterSVM(float64_t C, std::shared_ptr k ); /** default destructor */ ~ScatterSVM() override; @@ -82,7 +82,7 @@ class ScatterSVM : public MulticlassSVM * * @return resulting labels */ - virtual std::shared_ptr classify_one_vs_rest(); + virtual std::shared_ptr classify_one_vs_rest( const std::shared_ptr& labs); /** @return object name */ const char* get_name() const override { return "ScatterSVM"; } @@ -96,15 +96,15 @@ class ScatterSVM : public MulticlassSVM * * @return whether training was successful */ - bool train_machine(std::shared_ptr data=NULL) override; + bool train_machine(const std::shared_ptr&, const std::shared_ptr& labs) override; private: void compute_norm_wc(); - virtual bool train_no_bias_libsvm(); + virtual bool train_no_bias_libsvm( const std::shared_ptr& labs); #ifdef USE_SVMLIGHT - virtual bool train_no_bias_svmlight(); + virtual bool train_no_bias_svmlight( const std::shared_ptr& labs); #endif //USE_SVMLIGHT - virtual bool train_testrule12(); + virtual bool train_testrule12( const std::shared_ptr& labs); void register_params(); diff --git a/src/shogun/multiclass/ShareBoost.cpp b/src/shogun/multiclass/ShareBoost.cpp index ddcab5ada7d..20bba85af30 100644 --- a/src/shogun/multiclass/ShareBoost.cpp +++ b/src/shogun/multiclass/ShareBoost.cpp @@ -23,8 +23,8 @@ ShareBoost::ShareBoost() init_sb_params(); } -ShareBoost::ShareBoost(const std::shared_ptr >&features, const std::shared_ptr&labs, int32_t num_nonzero_feas) - :LinearMulticlassMachine(std::make_shared(), features, NULL, labs), m_nonzero_feas(num_nonzero_feas) +ShareBoost::ShareBoost(const std::shared_ptr&labs, int32_t num_nonzero_feas) + :LinearMulticlassMachine(std::make_shared(), NULL ), m_nonzero_feas(num_nonzero_feas) { init_sb_params(); } @@ -40,18 +40,12 @@ SGVector ShareBoost::get_activeset() return m_activeset; } -bool ShareBoost::train_machine(std::shared_ptr data) +bool ShareBoost::train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) { - if (data) - set_features(data); - auto fea = m_features->as>(); - - if (m_features == NULL) - error("No features given for training"); - if (m_labels == NULL) - error("No labels given for training"); - - init_strategy(); + m_share_boost_labels = labs; + auto fea = data->as>(); + m_features = fea; + init_strategy(labs); if (m_nonzero_feas <= 0) error("Set a valid (> 0) number of non-zero features to seek before training"); @@ -78,13 +72,13 @@ bool ShareBoost::train_machine(std::shared_ptr data) for (auto t : SG_PROGRESS(range(m_nonzero_feas))) { timer->start(); - compute_rho(); - int32_t i_fea = choose_feature(); + compute_rho(labs); + int32_t i_fea = choose_feature(labs); m_activeset.vector[m_activeset.vlen] = i_fea; m_activeset.vlen += 1; float64_t t_choose_feature = timer->cur_time_diff(); timer->start(); - optimize_coefficients(); + optimize_coefficients(labs); float64_t t_optimize = timer->cur_time_diff(); SG_DEBUG(" SB[round {:03d}]: ({:8.4f} + {:8.4f}) sec.", t, @@ -108,8 +102,7 @@ bool ShareBoost::train_machine(std::shared_ptr data) void ShareBoost::compute_pred() { - auto fea = m_features->as>(); - auto subset_fea = std::make_shared>(fea, m_activeset); + auto subset_fea = std::make_shared>(m_features, m_activeset); for (int32_t i=0; i < m_multiclass_strategy->get_num_classes(); ++i) { auto machine = m_machines.at(i)->as(); @@ -136,9 +129,9 @@ void ShareBoost::compute_pred(const float64_t *W) compute_pred(); } -void ShareBoost::compute_rho() +void ShareBoost::compute_rho( const std::shared_ptr& labs) { - auto lab = multiclass_labels(m_labels); + auto lab = multiclass_labels(labs); for (int32_t i=0; i < m_rho.num_rows; ++i) { // i loop classes @@ -160,10 +153,10 @@ void ShareBoost::compute_rho() } } -int32_t ShareBoost::choose_feature() +int32_t ShareBoost::choose_feature( const std::shared_ptr& labs) { SGVector l1norm(m_fea.num_rows); - auto lab = multiclass_labels(m_labels); + auto lab = multiclass_labels(labs); for (int32_t j=0; j < m_fea.num_rows; ++j) { if (std::find(&m_activeset[0], &m_activeset[m_activeset.vlen], j) != @@ -191,16 +184,10 @@ int32_t ShareBoost::choose_feature() return Math::arg_max(l1norm.vector, 1, l1norm.vlen); } -void ShareBoost::optimize_coefficients() +void ShareBoost::optimize_coefficients(const std::shared_ptr& labs) { ShareBoostOptimizer optimizer(shared_from_this()->as(), false); optimizer.optimize(); } -void ShareBoost::set_features(const std::shared_ptr&f) -{ - auto fea = f->as>(); - if (fea == NULL) - error("Require DenseFeatures"); - LinearMulticlassMachine::set_features(fea); -} + diff --git a/src/shogun/multiclass/ShareBoost.h b/src/shogun/multiclass/ShareBoost.h index b202e7e13d8..9b09b787056 100644 --- a/src/shogun/multiclass/ShareBoost.h +++ b/src/shogun/multiclass/ShareBoost.h @@ -32,7 +32,7 @@ class ShareBoost: public LinearMulticlassMachine ShareBoost(); /** constructor */ - ShareBoost(const std::shared_ptr >&features, const std::shared_ptr&labs, int32_t num_nonzero_feas); + ShareBoost(const std::shared_ptr&labs, int32_t num_nonzero_feas); /** destructor */ ~ShareBoost() override {} @@ -46,9 +46,6 @@ class ShareBoost: public LinearMulticlassMachine /** get number of non-zero features the algorithm should seek */ int32_t get_num_nonzero_feas() const { return m_nonzero_feas; } - /** assign features */ - void set_features(const std::shared_ptr&f); - /** get active set */ SGVector get_activeset(); @@ -56,14 +53,14 @@ class ShareBoost: public LinearMulticlassMachine protected: /** train machine */ - bool train_machine(std::shared_ptr data = NULL) override; + bool train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) override; private: void init_sb_params(); ///< init machine parameters - void compute_rho(); ///< compute the rho matrix - int32_t choose_feature(); ///< choose next feature greedily - void optimize_coefficients(); ///< optimize coefficients with gradient descent + void compute_rho( const std::shared_ptr& labs); ///< compute the rho matrix + int32_t choose_feature( const std::shared_ptr& labs); ///< choose next feature greedily + void optimize_coefficients(const std::shared_ptr& labs); ///< optimize coefficients with gradient descent void compute_pred(); ///< compute predictions on training data, according to W in m_machines void compute_pred(const float64_t *W); ///< compute predictions on training data, according to given W @@ -74,6 +71,8 @@ class ShareBoost: public LinearMulticlassMachine SGMatrix m_rho; ///< cache_matrix for rho SGVector m_rho_norm; ///< column sum of m_rho SGMatrix m_pred; ///< predictions, used in training + std::shared_ptr> m_features; + std::shared_ptr m_share_boost_labels; }; } /* shogun */ diff --git a/src/shogun/multiclass/ShareBoostOptimizer.cpp b/src/shogun/multiclass/ShareBoostOptimizer.cpp index 144a1e800f9..9607055b80e 100644 --- a/src/shogun/multiclass/ShareBoostOptimizer.cpp +++ b/src/shogun/multiclass/ShareBoostOptimizer.cpp @@ -45,13 +45,13 @@ float64_t ShareBoostOptimizer::lbfgs_evaluate(void *userdata, const float64_t *W ShareBoostOptimizer *optimizer = static_cast(userdata); optimizer->m_sb->compute_pred(W); - optimizer->m_sb->compute_rho(); + optimizer->m_sb->compute_rho(optimizer->m_sb->m_share_boost_labels); int32_t m = optimizer->m_sb->m_activeset.vlen; int32_t k = optimizer->m_sb->m_multiclass_strategy->get_num_classes(); SGMatrix fea = optimizer->m_sb->m_fea; - auto lab = multiclass_labels(optimizer->m_sb->m_labels); + auto lab = multiclass_labels(optimizer->m_sb->m_share_boost_labels); // compute gradient for (int32_t i=0; i < m; ++i) diff --git a/src/shogun/multiclass/ShareBoostOptimizer.h b/src/shogun/multiclass/ShareBoostOptimizer.h index 29b09e68a8f..ab8bf1b4f2b 100644 --- a/src/shogun/multiclass/ShareBoostOptimizer.h +++ b/src/shogun/multiclass/ShareBoostOptimizer.h @@ -20,7 +20,7 @@ class ShareBoostOptimizer public: /** constructor */ ShareBoostOptimizer(std::shared_ptrsb, bool verbose=false) - :m_sb(sb), m_verbose(verbose) { } + :m_sb(sb), m_verbose(verbose){} /** destructor */ ~ShareBoostOptimizer() { } diff --git a/src/shogun/multiclass/tree/RelaxedTreeUtil.cpp b/src/shogun/multiclass/tree/RelaxedTreeUtil.cpp index 3335926ca43..d53875393da 100644 --- a/src/shogun/multiclass/tree/RelaxedTreeUtil.cpp +++ b/src/shogun/multiclass/tree/RelaxedTreeUtil.cpp @@ -24,8 +24,7 @@ SGMatrix RelaxedTreeUtil::estimate_confusion_matrix(const std::shared { // subset for training SGVector inverse_subset_indices = split->generate_subset_inverse(i); - machine->set_labels(view(Y, inverse_subset_indices)); - machine->train(view(X, inverse_subset_indices)); + machine->train(view(X, inverse_subset_indices), view(Y, inverse_subset_indices)); // subset for predicting SGVector subset_indices = split->generate_subset_indices(i); diff --git a/src/shogun/transfer/domain_adaptation/DomainAdaptationMulticlassLibLinear.cpp b/src/shogun/transfer/domain_adaptation/DomainAdaptationMulticlassLibLinear.cpp index da95b06d027..9fd7f36e6ba 100644 --- a/src/shogun/transfer/domain_adaptation/DomainAdaptationMulticlassLibLinear.cpp +++ b/src/shogun/transfer/domain_adaptation/DomainAdaptationMulticlassLibLinear.cpp @@ -20,9 +20,8 @@ DomainAdaptationMulticlassLibLinear::DomainAdaptationMulticlassLibLinear() : } DomainAdaptationMulticlassLibLinear::DomainAdaptationMulticlassLibLinear( - float64_t target_C, std::shared_ptr target_features, std::shared_ptr target_labels, - std::shared_ptr source_machine) : - MulticlassLibLinear(target_C,std::move(target_features),std::move(target_labels)) + float64_t target_C, std::shared_ptr source_machine) : + MulticlassLibLinear(target_C) { init_defaults(); @@ -88,8 +87,8 @@ DomainAdaptationMulticlassLibLinear::~DomainAdaptationMulticlassLibLinear() SGMatrix DomainAdaptationMulticlassLibLinear::obtain_regularizer_matrix() const { ASSERT(get_use_bias()==false) - int32_t n_classes = m_source_machine->get_labels()->as()->get_num_classes(); - int32_t n_features = m_source_machine->get_features()->as()->get_dim_feature_space(); + int32_t n_classes = m_source_machine->get_num_classes(); + int32_t n_features = m_source_machine->get_dim_feature_space(); SGMatrix w0(n_classes,n_features); for (int32_t i=0; i target_features, std::shared_ptr target_labels, - std::shared_ptr source_machine); + DomainAdaptationMulticlassLibLinear(float64_t target_C, std::shared_ptr source_machine); /** destructor */ ~DomainAdaptationMulticlassLibLinear() override; diff --git a/tests/unit/multiclass/MCLDA_unittest.cc b/tests/unit/multiclass/MCLDA_unittest.cc index 4a515536b42..f4599393022 100644 --- a/tests/unit/multiclass/MCLDA_unittest.cc +++ b/tests/unit/multiclass/MCLDA_unittest.cc @@ -31,11 +31,11 @@ TEST(MCLDA, train_and_apply) auto labels = std::make_shared(lab); auto features = std::make_shared>(feat); - auto lda = std::make_shared(features, labels); + auto lda = std::make_shared(); - lda->train(); + lda->train(features, labels); - auto output = lda->apply()->as(); + auto output = lda->apply(features)->as(); // Test for ( index_t i = 0; i < CLASSES*NUM; ++i ) EXPECT_EQ(output->get_label(i), labels->get_label(i)); diff --git a/tests/unit/multiclass/MulticlassLibLinear_unittest.cc b/tests/unit/multiclass/MulticlassLibLinear_unittest.cc index 024f31bdbb6..e08ab920110 100644 --- a/tests/unit/multiclass/MulticlassLibLinear_unittest.cc +++ b/tests/unit/multiclass/MulticlassLibLinear_unittest.cc @@ -52,10 +52,10 @@ TEST(MulticlassLibLinearTest,train_and_apply) float64_t C=1.0; - auto mocas=std::make_shared(C, features, labels); + auto mocas=std::make_shared(C); env()->set_num_threads(1); mocas->set_epsilon(1e-5); - mocas->train(features); + mocas->train(features, labels); auto pred=mocas->apply(features_test)->as(); for (int i=0; iget_num_vectors(); ++i) diff --git a/tests/unit/multiclass/MulticlassOCAS_unittest.cc b/tests/unit/multiclass/MulticlassOCAS_unittest.cc index c6409f8a54d..907992dde9a 100644 --- a/tests/unit/multiclass/MulticlassOCAS_unittest.cc +++ b/tests/unit/multiclass/MulticlassOCAS_unittest.cc @@ -21,10 +21,10 @@ TEST(MulticlassOCASTest,train) auto test_feats = mockData->get_features_test(); auto ground_truth = std::static_pointer_cast(mockData->get_labels_test()); - auto mocas = std::make_shared(C, train_feats, ground_truth); + auto mocas = std::make_shared(C); env()->set_num_threads(1); mocas->set_epsilon(1e-5); - mocas->train(); + mocas->train(train_feats, ground_truth); auto pred = mocas->apply(test_feats)->as(); diff --git a/tests/unit/multiclass/QDA_unittest.cc b/tests/unit/multiclass/QDA_unittest.cc index a52b6d379bf..d47f1628f1b 100644 --- a/tests/unit/multiclass/QDA_unittest.cc +++ b/tests/unit/multiclass/QDA_unittest.cc @@ -31,11 +31,11 @@ TEST(QDA, train_and_apply) auto labels = std::make_shared(lab); auto features = std::make_shared>(feat); - auto qda = std::make_shared(features, labels); + auto qda = std::make_shared(); - qda->train(); + qda->train(features, labels); - auto output = qda->apply()->as(); + auto output = qda->apply(features)->as(); // Test for ( index_t i = 0; i < CLASSES*NUM; ++i ) EXPECT_EQ(output->get_label(i), labels->get_label(i)); From 314b50e9f9e04c3fd05bed444e73db810354242d Mon Sep 17 00:00:00 2001 From: gf712 Date: Wed, 29 Jul 2020 08:04:02 +0100 Subject: [PATCH 6/9] fix GPL --- examples/meta/src/multiclass/ecoc_discriminant_aed.sg.in | 4 ++-- examples/meta/src/multiclass/ecoc_discriminant_ed.sg.in | 4 ++-- examples/meta/src/multiclass/ecoc_discriminant_hd.sg.in | 4 ++-- examples/meta/src/multiclass/ecoc_discriminant_ihd.sg.in | 4 ++-- examples/meta/src/multiclass/ecoc_discriminant_llb.sg.in | 4 ++-- examples/meta/src/multiclass/ecoc_forest_aed.sg.in | 4 ++-- examples/meta/src/multiclass/ecoc_forest_ed.sg.in | 4 ++-- examples/meta/src/multiclass/ecoc_forest_hd.sg.in | 4 ++-- examples/meta/src/multiclass/ecoc_forest_ihd.sg.in | 4 ++-- examples/meta/src/multiclass/ecoc_forest_llb.sg.in | 4 ++-- examples/meta/src/multiclass/ecoc_ovo_aed.sg.in | 4 ++-- examples/meta/src/multiclass/ecoc_ovo_ed.sg.in | 4 ++-- examples/meta/src/multiclass/ecoc_ovo_hd.sg.in | 4 ++-- examples/meta/src/multiclass/ecoc_ovo_ihd.sg.in | 4 ++-- examples/meta/src/multiclass/ecoc_ovo_llb.sg.in | 4 ++-- examples/meta/src/multiclass/ecoc_ovr_aed.sg.in | 4 ++-- examples/meta/src/multiclass/ecoc_ovr_ed.sg.in | 4 ++-- examples/meta/src/multiclass/ecoc_ovr_hd.sg.in | 4 ++-- examples/meta/src/multiclass/ecoc_ovr_ihd.sg.in | 4 ++-- examples/meta/src/multiclass/ecoc_ovr_llb.sg.in | 4 ++-- examples/meta/src/multiclass/ecoc_random_dense_aed.sg.in | 4 ++-- examples/meta/src/multiclass/ecoc_random_dense_ed.sg.in | 4 ++-- examples/meta/src/multiclass/ecoc_random_dense_ihd.sg.in | 4 ++-- examples/meta/src/multiclass/ecoc_random_dense_llb.sg.in | 4 ++-- .../meta/src/multiclass/ecoc_random_sparse_aed.sg.in | 4 ++-- examples/meta/src/multiclass/ecoc_random_sparse_ed.sg.in | 4 ++-- examples/meta/src/multiclass/ecoc_random_sparse_hd.sg.in | 4 ++-- .../meta/src/multiclass/ecoc_random_sparse_ihd.sg.in | 4 ++-- .../meta/src/multiclass/ecoc_random_sparse_llb.sg.in | 4 ++-- src/shogun/machine/IterativeMachine.h | 9 +++------ src/shogun/machine/LinearMachine.h | 4 ++-- 31 files changed, 63 insertions(+), 66 deletions(-) diff --git a/examples/meta/src/multiclass/ecoc_discriminant_aed.sg.in b/examples/meta/src/multiclass/ecoc_discriminant_aed.sg.in index b1804b4fa54..4dd87e8fadd 100644 --- a/examples/meta/src/multiclass/ecoc_discriminant_aed.sg.in +++ b/examples/meta/src/multiclass/ecoc_discriminant_aed.sg.in @@ -23,11 +23,11 @@ MulticlassStrategy strategy = create_multiclass_strategy("ECOCStrategy", encoder #![choose_strategy] #![create_instance] -Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier, labels=labels_train) +Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier) #![create_instance] #![train_and_apply] -mc_classifier.train(features_train) +mc_classifier.train(features_train, labels_train) Labels labels_predict = mc_classifier.apply(features_test) #![train_and_apply] diff --git a/examples/meta/src/multiclass/ecoc_discriminant_ed.sg.in b/examples/meta/src/multiclass/ecoc_discriminant_ed.sg.in index 9d70fe306dc..b44f265cbbf 100644 --- a/examples/meta/src/multiclass/ecoc_discriminant_ed.sg.in +++ b/examples/meta/src/multiclass/ecoc_discriminant_ed.sg.in @@ -23,11 +23,11 @@ MulticlassStrategy strategy = create_multiclass_strategy("ECOCStrategy", encoder #![choose_strategy] #![create_instance] -Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier, labels=labels_train) +Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier) #![create_instance] #![train_and_apply] -mc_classifier.train(features_train) +mc_classifier.train(features_train, labels_train) Labels labels_predict = mc_classifier.apply(features_test) #![train_and_apply] diff --git a/examples/meta/src/multiclass/ecoc_discriminant_hd.sg.in b/examples/meta/src/multiclass/ecoc_discriminant_hd.sg.in index 66729334f5d..6e8186cfc1e 100644 --- a/examples/meta/src/multiclass/ecoc_discriminant_hd.sg.in +++ b/examples/meta/src/multiclass/ecoc_discriminant_hd.sg.in @@ -23,11 +23,11 @@ MulticlassStrategy strategy = create_multiclass_strategy("ECOCStrategy", encoder #![choose_strategy] #![create_instance] -Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier, labels=labels_train) +Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier) #![create_instance] #![train_and_apply] -mc_classifier.train(features_train) +mc_classifier.train(features_train, labels_train) Labels labels_predict = mc_classifier.apply(features_test) #![train_and_apply] diff --git a/examples/meta/src/multiclass/ecoc_discriminant_ihd.sg.in b/examples/meta/src/multiclass/ecoc_discriminant_ihd.sg.in index ea28f9d2aeb..a2215264934 100644 --- a/examples/meta/src/multiclass/ecoc_discriminant_ihd.sg.in +++ b/examples/meta/src/multiclass/ecoc_discriminant_ihd.sg.in @@ -23,11 +23,11 @@ MulticlassStrategy strategy = create_multiclass_strategy("ECOCStrategy", encoder #![choose_strategy] #![create_instance] -Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier, labels=labels_train) +Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier) #![create_instance] #![train_and_apply] -mc_classifier.train(features_train) +mc_classifier.train(features_train, labels_train) Labels labels_predict = mc_classifier.apply(features_test) #![train_and_apply] diff --git a/examples/meta/src/multiclass/ecoc_discriminant_llb.sg.in b/examples/meta/src/multiclass/ecoc_discriminant_llb.sg.in index 3ee82d36705..cb7287ab646 100644 --- a/examples/meta/src/multiclass/ecoc_discriminant_llb.sg.in +++ b/examples/meta/src/multiclass/ecoc_discriminant_llb.sg.in @@ -23,11 +23,11 @@ MulticlassStrategy strategy = create_multiclass_strategy("ECOCStrategy", encoder #![choose_strategy] #![create_instance] -Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier, labels=labels_train) +Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier) #![create_instance] #![train_and_apply] -mc_classifier.train(features_train) +mc_classifier.train(features_train, labels_train) Labels labels_predict = mc_classifier.apply(features_test) #![train_and_apply] diff --git a/examples/meta/src/multiclass/ecoc_forest_aed.sg.in b/examples/meta/src/multiclass/ecoc_forest_aed.sg.in index 009c9e80592..7938cec73e2 100644 --- a/examples/meta/src/multiclass/ecoc_forest_aed.sg.in +++ b/examples/meta/src/multiclass/ecoc_forest_aed.sg.in @@ -23,11 +23,11 @@ MulticlassStrategy strategy = create_multiclass_strategy("ECOCStrategy", encoder #![choose_strategy] #![create_instance] -Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier, labels=labels_train) +Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier) #![create_instance] #![train_and_apply] -mc_classifier.train(features_train) +mc_classifier.train(features_train, labels_train) Labels labels_predict = mc_classifier.apply(features_test) #![train_and_apply] diff --git a/examples/meta/src/multiclass/ecoc_forest_ed.sg.in b/examples/meta/src/multiclass/ecoc_forest_ed.sg.in index a90e1a83b05..c5fa5c93c48 100644 --- a/examples/meta/src/multiclass/ecoc_forest_ed.sg.in +++ b/examples/meta/src/multiclass/ecoc_forest_ed.sg.in @@ -23,11 +23,11 @@ MulticlassStrategy strategy = create_multiclass_strategy("ECOCStrategy", encoder #![choose_strategy] #![create_instance] -Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier, labels=labels_train) +Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier) #![create_instance] #![train_and_apply] -mc_classifier.train(features_train) +mc_classifier.train(features_train, labels_train) Labels labels_predict = mc_classifier.apply(features_test) #![train_and_apply] diff --git a/examples/meta/src/multiclass/ecoc_forest_hd.sg.in b/examples/meta/src/multiclass/ecoc_forest_hd.sg.in index 7b869c1ad4e..26fa6d3fb70 100644 --- a/examples/meta/src/multiclass/ecoc_forest_hd.sg.in +++ b/examples/meta/src/multiclass/ecoc_forest_hd.sg.in @@ -23,11 +23,11 @@ MulticlassStrategy strategy = create_multiclass_strategy("ECOCStrategy", encoder #![choose_strategy] #![create_instance] -Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier, labels=labels_train) +Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier) #![create_instance] #![train_and_apply] -mc_classifier.train(features_train) +mc_classifier.train(features_train, labels_train) Labels labels_predict = mc_classifier.apply(features_test) #![train_and_apply] diff --git a/examples/meta/src/multiclass/ecoc_forest_ihd.sg.in b/examples/meta/src/multiclass/ecoc_forest_ihd.sg.in index ded419c6d92..ab6e5ccbbcb 100644 --- a/examples/meta/src/multiclass/ecoc_forest_ihd.sg.in +++ b/examples/meta/src/multiclass/ecoc_forest_ihd.sg.in @@ -23,11 +23,11 @@ MulticlassStrategy strategy = create_multiclass_strategy("ECOCStrategy", encoder #![choose_strategy] #![create_instance] -Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier, labels=labels_train) +Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier) #![create_instance] #![train_and_apply] -mc_classifier.train(features_train) +mc_classifier.train(features_train, labels_train) Labels labels_predict = mc_classifier.apply(features_test) #![train_and_apply] diff --git a/examples/meta/src/multiclass/ecoc_forest_llb.sg.in b/examples/meta/src/multiclass/ecoc_forest_llb.sg.in index 50711a9eeb0..0bd0893aa7c 100644 --- a/examples/meta/src/multiclass/ecoc_forest_llb.sg.in +++ b/examples/meta/src/multiclass/ecoc_forest_llb.sg.in @@ -23,11 +23,11 @@ MulticlassStrategy strategy = create_multiclass_strategy("ECOCStrategy", encoder #![choose_strategy] #![create_instance] -Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier, labels=labels_train) +Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier) #![create_instance] #![train_and_apply] -mc_classifier.train(features_train) +mc_classifier.train(features_train, labels_train) Labels labels_predict = mc_classifier.apply(features_test) #![train_and_apply] diff --git a/examples/meta/src/multiclass/ecoc_ovo_aed.sg.in b/examples/meta/src/multiclass/ecoc_ovo_aed.sg.in index 60a1a05d306..f2902224260 100644 --- a/examples/meta/src/multiclass/ecoc_ovo_aed.sg.in +++ b/examples/meta/src/multiclass/ecoc_ovo_aed.sg.in @@ -21,11 +21,11 @@ MulticlassStrategy strategy = create_multiclass_strategy("ECOCStrategy", encoder #![choose_strategy] #![create_instance] -Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier, labels=labels_train) +Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier) #![create_instance] #![train_and_apply] -mc_classifier.train(features_train) +mc_classifier.train(features_train, labels_train) Labels labels_predict = mc_classifier.apply(features_test) #![train_and_apply] diff --git a/examples/meta/src/multiclass/ecoc_ovo_ed.sg.in b/examples/meta/src/multiclass/ecoc_ovo_ed.sg.in index 46301177202..99e21b96a8b 100644 --- a/examples/meta/src/multiclass/ecoc_ovo_ed.sg.in +++ b/examples/meta/src/multiclass/ecoc_ovo_ed.sg.in @@ -21,11 +21,11 @@ MulticlassStrategy strategy = create_multiclass_strategy("ECOCStrategy", encoder #![choose_strategy] #![create_instance] -Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier, labels=labels_train) +Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier) #![create_instance] #![train_and_apply] -mc_classifier.train(features_train) +mc_classifier.train(features_train, labels_train) Labels labels_predict = mc_classifier.apply(features_test) #![train_and_apply] diff --git a/examples/meta/src/multiclass/ecoc_ovo_hd.sg.in b/examples/meta/src/multiclass/ecoc_ovo_hd.sg.in index 8fe8d4f40cc..428b3fc4edc 100644 --- a/examples/meta/src/multiclass/ecoc_ovo_hd.sg.in +++ b/examples/meta/src/multiclass/ecoc_ovo_hd.sg.in @@ -21,11 +21,11 @@ MulticlassStrategy strategy = create_multiclass_strategy("ECOCStrategy", encoder #![choose_strategy] #![create_instance] -Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier, labels=labels_train) +Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier) #![create_instance] #![train_and_apply] -mc_classifier.train(features_train) +mc_classifier.train(features_train, labels_train) Labels labels_predict = mc_classifier.apply(features_test) #![train_and_apply] diff --git a/examples/meta/src/multiclass/ecoc_ovo_ihd.sg.in b/examples/meta/src/multiclass/ecoc_ovo_ihd.sg.in index ca6fb711422..b688533d3bc 100644 --- a/examples/meta/src/multiclass/ecoc_ovo_ihd.sg.in +++ b/examples/meta/src/multiclass/ecoc_ovo_ihd.sg.in @@ -21,11 +21,11 @@ MulticlassStrategy strategy = create_multiclass_strategy("ECOCStrategy", encoder #![choose_strategy] #![create_instance] -Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier, labels=labels_train) +Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier) #![create_instance] #![train_and_apply] -mc_classifier.train(features_train) +mc_classifier.train(features_train, labels_train) Labels labels_predict = mc_classifier.apply(features_test) #![train_and_apply] diff --git a/examples/meta/src/multiclass/ecoc_ovo_llb.sg.in b/examples/meta/src/multiclass/ecoc_ovo_llb.sg.in index 4baefb7b9a6..b62ed72f0dd 100644 --- a/examples/meta/src/multiclass/ecoc_ovo_llb.sg.in +++ b/examples/meta/src/multiclass/ecoc_ovo_llb.sg.in @@ -21,11 +21,11 @@ MulticlassStrategy strategy = create_multiclass_strategy("ECOCStrategy", encoder #![choose_strategy] #![create_instance] -Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier, labels=labels_train) +Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier) #![create_instance] #![train_and_apply] -mc_classifier.train(features_train) +mc_classifier.train(features_train, labels_train) Labels labels_predict = mc_classifier.apply(features_test) #![train_and_apply] diff --git a/examples/meta/src/multiclass/ecoc_ovr_aed.sg.in b/examples/meta/src/multiclass/ecoc_ovr_aed.sg.in index 46e30f98353..bfc23865b9a 100644 --- a/examples/meta/src/multiclass/ecoc_ovr_aed.sg.in +++ b/examples/meta/src/multiclass/ecoc_ovr_aed.sg.in @@ -21,11 +21,11 @@ MulticlassStrategy strategy = create_multiclass_strategy("ECOCStrategy", encoder #![choose_strategy] #![create_instance] -Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier, labels=labels_train) +Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier) #![create_instance] #![train_and_apply] -mc_classifier.train(features_train) +mc_classifier.train(features_train, labels_train) Labels labels_predict = mc_classifier.apply(features_test) #![train_and_apply] diff --git a/examples/meta/src/multiclass/ecoc_ovr_ed.sg.in b/examples/meta/src/multiclass/ecoc_ovr_ed.sg.in index 2bb4f1cd1c5..3592c33fdef 100644 --- a/examples/meta/src/multiclass/ecoc_ovr_ed.sg.in +++ b/examples/meta/src/multiclass/ecoc_ovr_ed.sg.in @@ -21,11 +21,11 @@ MulticlassStrategy strategy = create_multiclass_strategy("ECOCStrategy", encoder #![choose_strategy] #![create_instance] -Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier, labels=labels_train) +Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier) #![create_instance] #![train_and_apply] -mc_classifier.train(features_train) +mc_classifier.train(features_train, labels_train) Labels labels_predict = mc_classifier.apply(features_test) #![train_and_apply] diff --git a/examples/meta/src/multiclass/ecoc_ovr_hd.sg.in b/examples/meta/src/multiclass/ecoc_ovr_hd.sg.in index 878372b9a7b..3319cf4a22e 100644 --- a/examples/meta/src/multiclass/ecoc_ovr_hd.sg.in +++ b/examples/meta/src/multiclass/ecoc_ovr_hd.sg.in @@ -21,11 +21,11 @@ MulticlassStrategy strategy = create_multiclass_strategy("ECOCStrategy", encoder #![choose_strategy] #![create_instance] -Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier, labels=labels_train) +Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier) #![create_instance] #![train_and_apply] -mc_classifier.train(features_train) +mc_classifier.train(features_train, labels_train) Labels labels_predict = mc_classifier.apply(features_test) #![train_and_apply] diff --git a/examples/meta/src/multiclass/ecoc_ovr_ihd.sg.in b/examples/meta/src/multiclass/ecoc_ovr_ihd.sg.in index a600caf88ac..14a510db114 100644 --- a/examples/meta/src/multiclass/ecoc_ovr_ihd.sg.in +++ b/examples/meta/src/multiclass/ecoc_ovr_ihd.sg.in @@ -21,11 +21,11 @@ MulticlassStrategy strategy = create_multiclass_strategy("ECOCStrategy", encoder #![choose_strategy] #![create_instance] -Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier, labels=labels_train) +Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier) #![create_instance] #![train_and_apply] -mc_classifier.train(features_train) +mc_classifier.train(features_train, labels_train) Labels labels_predict = mc_classifier.apply(features_test) #![train_and_apply] diff --git a/examples/meta/src/multiclass/ecoc_ovr_llb.sg.in b/examples/meta/src/multiclass/ecoc_ovr_llb.sg.in index 29f3ae28400..2b5f8bde0ac 100644 --- a/examples/meta/src/multiclass/ecoc_ovr_llb.sg.in +++ b/examples/meta/src/multiclass/ecoc_ovr_llb.sg.in @@ -21,11 +21,11 @@ MulticlassStrategy strategy = create_multiclass_strategy("ECOCStrategy", encoder #![choose_strategy] #![create_instance] -Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier, labels=labels_train) +Machine mc_classifier = create_machine("LinearMulticlassMachine", multiclass_strategy=strategy, machine=classifier) #![create_instance] #![train_and_apply] -mc_classifier.train(features_train) +mc_classifier.train(features_train, labels_train) Labels labels_predict = mc_classifier.apply(features_test) #![train_and_apply] diff --git a/examples/meta/src/multiclass/ecoc_random_dense_aed.sg.in b/examples/meta/src/multiclass/ecoc_random_dense_aed.sg.in index ace60664712..d3c98ffcb38 100644 --- a/examples/meta/src/multiclass/ecoc_random_dense_aed.sg.in +++ b/examples/meta/src/multiclass/ecoc_random_dense_aed.sg.in @@ -22,11 +22,11 @@ MulticlassStrategy rnd_dense_strategy=create_multiclass_strategy("ECOCStrategy", #![choose_strategy] #![create_instance] -Machine mc_classifier=create_machine("LinearMulticlassMachine", multiclass_strategy=rnd_dense_strategy, machine=classifier, labels=labels_train) +Machine mc_classifier=create_machine("LinearMulticlassMachine", multiclass_strategy=rnd_dense_strategy, machine=classifier) #![create_instance] #![train_and_apply] -mc_classifier.train(features_train) +mc_classifier.train(features_train, labels_train) Labels labels_predict = mc_classifier.apply(features_test) #![train_and_apply] diff --git a/examples/meta/src/multiclass/ecoc_random_dense_ed.sg.in b/examples/meta/src/multiclass/ecoc_random_dense_ed.sg.in index 0db262c1f26..0f47968be09 100644 --- a/examples/meta/src/multiclass/ecoc_random_dense_ed.sg.in +++ b/examples/meta/src/multiclass/ecoc_random_dense_ed.sg.in @@ -22,11 +22,11 @@ MulticlassStrategy rnd_dense_strategy=create_multiclass_strategy("ECOCStrategy", #![choose_strategy] #![create_instance] -Machine mc_classifier=create_machine("LinearMulticlassMachine", multiclass_strategy=rnd_dense_strategy, machine=classifier, labels=labels_train) +Machine mc_classifier=create_machine("LinearMulticlassMachine", multiclass_strategy=rnd_dense_strategy, machine=classifier) #![create_instance] #![train_and_apply] -mc_classifier.train(features_train) +mc_classifier.train(features_train, labels_train) Labels labels_predict = mc_classifier.apply(features_test) #![train_and_apply] diff --git a/examples/meta/src/multiclass/ecoc_random_dense_ihd.sg.in b/examples/meta/src/multiclass/ecoc_random_dense_ihd.sg.in index 6f914c05f23..7d67bfcf27c 100644 --- a/examples/meta/src/multiclass/ecoc_random_dense_ihd.sg.in +++ b/examples/meta/src/multiclass/ecoc_random_dense_ihd.sg.in @@ -22,11 +22,11 @@ MulticlassStrategy rnd_dense_strategy=create_multiclass_strategy("ECOCStrategy", #![choose_strategy] #![create_instance] -Machine mc_classifier=create_machine("LinearMulticlassMachine", multiclass_strategy=rnd_dense_strategy, machine=classifier, labels=labels_train) +Machine mc_classifier=create_machine("LinearMulticlassMachine", multiclass_strategy=rnd_dense_strategy, machine=classifier) #![create_instance] #![train_and_apply] -mc_classifier.train(features_train) +mc_classifier.train(features_train, labels_train) Labels labels_predict = mc_classifier.apply(features_test) #![train_and_apply] diff --git a/examples/meta/src/multiclass/ecoc_random_dense_llb.sg.in b/examples/meta/src/multiclass/ecoc_random_dense_llb.sg.in index 521af659c05..aa68ddeee44 100644 --- a/examples/meta/src/multiclass/ecoc_random_dense_llb.sg.in +++ b/examples/meta/src/multiclass/ecoc_random_dense_llb.sg.in @@ -22,11 +22,11 @@ MulticlassStrategy rnd_dense_strategy=create_multiclass_strategy("ECOCStrategy", #![choose_strategy] #![create_instance] -Machine mc_classifier=create_machine("LinearMulticlassMachine", multiclass_strategy=rnd_dense_strategy, machine=classifier, labels=labels_train) +Machine mc_classifier=create_machine("LinearMulticlassMachine", multiclass_strategy=rnd_dense_strategy, machine=classifier) #![create_instance] #![train_and_apply] -mc_classifier.train(features_train) +mc_classifier.train(features_train, labels_train) Labels labels_predict = mc_classifier.apply(features_test) #![train_and_apply] diff --git a/examples/meta/src/multiclass/ecoc_random_sparse_aed.sg.in b/examples/meta/src/multiclass/ecoc_random_sparse_aed.sg.in index 3eb830c9cf3..373b3635c35 100644 --- a/examples/meta/src/multiclass/ecoc_random_sparse_aed.sg.in +++ b/examples/meta/src/multiclass/ecoc_random_sparse_aed.sg.in @@ -22,11 +22,11 @@ MulticlassStrategy rnd_dense_strategy=create_multiclass_strategy("ECOCStrategy", #![choose_strategy] #![create_instance] -Machine mc_classifier=create_machine("LinearMulticlassMachine", multiclass_strategy=rnd_dense_strategy, machine=classifier, labels=labels_train) +Machine mc_classifier=create_machine("LinearMulticlassMachine", multiclass_strategy=rnd_dense_strategy, machine=classifier) #![create_instance] #![train_and_apply] -mc_classifier.train(features_train) +mc_classifier.train(features_train, labels_train) Labels labels_predict = mc_classifier.apply(features_test) #![train_and_apply] diff --git a/examples/meta/src/multiclass/ecoc_random_sparse_ed.sg.in b/examples/meta/src/multiclass/ecoc_random_sparse_ed.sg.in index 9d40f6f296b..6b9c9ffec9a 100644 --- a/examples/meta/src/multiclass/ecoc_random_sparse_ed.sg.in +++ b/examples/meta/src/multiclass/ecoc_random_sparse_ed.sg.in @@ -22,11 +22,11 @@ MulticlassStrategy rnd_dense_strategy=create_multiclass_strategy("ECOCStrategy", #![choose_strategy] #![create_instance] -Machine mc_classifier=create_machine("LinearMulticlassMachine", multiclass_strategy=rnd_dense_strategy, machine=classifier, labels=labels_train) +Machine mc_classifier=create_machine("LinearMulticlassMachine", multiclass_strategy=rnd_dense_strategy, machine=classifier) #![create_instance] #![train_and_apply] -mc_classifier.train(features_train) +mc_classifier.train(features_train, labels_train) Labels labels_predict = mc_classifier.apply(features_test) #![train_and_apply] diff --git a/examples/meta/src/multiclass/ecoc_random_sparse_hd.sg.in b/examples/meta/src/multiclass/ecoc_random_sparse_hd.sg.in index ea5c2cee788..0dd38f099db 100644 --- a/examples/meta/src/multiclass/ecoc_random_sparse_hd.sg.in +++ b/examples/meta/src/multiclass/ecoc_random_sparse_hd.sg.in @@ -22,11 +22,11 @@ MulticlassStrategy rnd_dense_strategy=create_multiclass_strategy("ECOCStrategy", #![choose_strategy] #![create_instance] -Machine mc_classifier=create_machine("LinearMulticlassMachine", multiclass_strategy=rnd_dense_strategy, machine=classifier, labels=labels_train) +Machine mc_classifier=create_machine("LinearMulticlassMachine", multiclass_strategy=rnd_dense_strategy, machine=classifier) #![create_instance] #![train_and_apply] -mc_classifier.train(features_train) +mc_classifier.train(features_train, labels_train) Labels labels_predict = mc_classifier.apply(features_test) #![train_and_apply] diff --git a/examples/meta/src/multiclass/ecoc_random_sparse_ihd.sg.in b/examples/meta/src/multiclass/ecoc_random_sparse_ihd.sg.in index c7fbca0933d..817d5f7ed14 100644 --- a/examples/meta/src/multiclass/ecoc_random_sparse_ihd.sg.in +++ b/examples/meta/src/multiclass/ecoc_random_sparse_ihd.sg.in @@ -22,11 +22,11 @@ MulticlassStrategy rnd_dense_strategy=create_multiclass_strategy("ECOCStrategy", #![choose_strategy] #![create_instance] -Machine mc_classifier=create_machine("LinearMulticlassMachine", multiclass_strategy=rnd_dense_strategy, machine=classifier, labels=labels_train) +Machine mc_classifier=create_machine("LinearMulticlassMachine", multiclass_strategy=rnd_dense_strategy, machine=classifier) #![create_instance] #![train_and_apply] -mc_classifier.train(features_train) +mc_classifier.train(features_train, labels_train) Labels labels_predict = mc_classifier.apply(features_test) #![train_and_apply] diff --git a/examples/meta/src/multiclass/ecoc_random_sparse_llb.sg.in b/examples/meta/src/multiclass/ecoc_random_sparse_llb.sg.in index 5738b69eb98..8d3c0ad8efe 100644 --- a/examples/meta/src/multiclass/ecoc_random_sparse_llb.sg.in +++ b/examples/meta/src/multiclass/ecoc_random_sparse_llb.sg.in @@ -22,11 +22,11 @@ MulticlassStrategy rnd_dense_strategy=create_multiclass_strategy("ECOCStrategy", #![choose_strategy] #![create_instance] -Machine mc_classifier=create_machine("LinearMulticlassMachine", multiclass_strategy=rnd_dense_strategy, machine=classifier, labels=labels_train) +Machine mc_classifier=create_machine("LinearMulticlassMachine", multiclass_strategy=rnd_dense_strategy, machine=classifier) #![create_instance] #![train_and_apply] -mc_classifier.train(features_train) +mc_classifier.train(features_train, labels_train) Labels labels_predict = mc_classifier.apply(features_test) #![train_and_apply] diff --git a/src/shogun/machine/IterativeMachine.h b/src/shogun/machine/IterativeMachine.h index 4b8f66c243a..e9407228566 100644 --- a/src/shogun/machine/IterativeMachine.h +++ b/src/shogun/machine/IterativeMachine.h @@ -45,10 +45,7 @@ namespace shogun SG_ADD( &m_continue_features, "continue_features", "Continue Features"); } - ~IterativeMachine() override - { - - } + ~IterativeMachine() override = default; /** Returns convergence status */ bool is_complete() @@ -56,9 +53,9 @@ namespace shogun return m_complete; } - bool continue_train( + virtual bool continue_train( const std::shared_ptr& data, - const std::shared_ptr& labs) override + const std::shared_ptr& labs) { this->reset_computation_variables(); //this->put("features", m_continue_features); diff --git a/src/shogun/machine/LinearMachine.h b/src/shogun/machine/LinearMachine.h index 5d903be4ede..2f2d1458696 100644 --- a/src/shogun/machine/LinearMachine.h +++ b/src/shogun/machine/LinearMachine.h @@ -115,8 +115,8 @@ class LinearMachine : public Machine apply_regression(std::shared_ptr data) override; /** applies to one vector */ - float64_t apply_one( - const std::shared_ptr& features, int32_t vec_idx) override; + virtual float64_t apply_one( + const std::shared_ptr& features, int32_t vec_idx); /** Returns the name of the SGSerializable instance. It MUST BE * the CLASS NAME without the prefixed `C'. From 85bfb7ffc014efdfb9a73c610e32937697f3a8c3 Mon Sep 17 00:00:00 2001 From: LiuYuhui Date: Fri, 31 Jul 2020 14:41:46 +0800 Subject: [PATCH 7/9] Refactor NearestCentroid class (#5053) * Add NonParametricMachine class (#5055) * add nonparametric machine * fix notebooks * Refactor NearestCentroid class --- .../classification/Classification.ipynb | 4 +-- examples/meta/src/multiclass/lmnn.sg.in | 2 +- src/shogun/classifier/NearestCentroid.cpp | 31 ++++--------------- src/shogun/classifier/NearestCentroid.h | 13 ++------ .../classifier/NearestCentroid_unittest.cc | 31 +++++++++++++++++++ 5 files changed, 43 insertions(+), 38 deletions(-) create mode 100644 tests/unit/classifier/NearestCentroid_unittest.cc diff --git a/doc/ipython-notebooks/classification/Classification.ipynb b/doc/ipython-notebooks/classification/Classification.ipynb index a24fa498f63..56277fdd9ac 100644 --- a/doc/ipython-notebooks/classification/Classification.ipynb +++ b/doc/ipython-notebooks/classification/Classification.ipynb @@ -441,7 +441,7 @@ "distances_linear.init(shogun_feats_linear, shogun_feats_linear)\n", "knn_linear = sg.create_machine(\"KNN\", k=number_of_neighbors, distance=distances_linear, \n", " labels=shogun_labels_linear)\n", - "knn_linear.train()\n", + "knn_linear.train(shogun_feats_linear)\n", "classifiers_linear.append(knn_linear)\n", "classifiers_names.append(\"Nearest Neighbors\")\n", "fadings.append(False)\n", @@ -455,7 +455,7 @@ "distances_non_linear.init(shogun_feats_non_linear, shogun_feats_non_linear)\n", "knn_non_linear = sg.create_machine(\"KNN\", k=number_of_neighbors, distance=distances_non_linear, \n", " labels=shogun_labels_non_linear)\n", - "knn_non_linear.train()\n", + "knn_non_linear.train(shogun_feats_non_linear)\n", "classifiers_non_linear.append(knn_non_linear)\n", "\n", "plt.subplot(122)\n", diff --git a/examples/meta/src/multiclass/lmnn.sg.in b/examples/meta/src/multiclass/lmnn.sg.in index ab64a312efa..ccd6c00dc53 100644 --- a/examples/meta/src/multiclass/lmnn.sg.in +++ b/examples/meta/src/multiclass/lmnn.sg.in @@ -20,7 +20,7 @@ Machine knn = create_machine("KNN", k=k,distance=lmnn_distance,labels=labels_tra #![create_instance] #![train_and_apply] -knn.train() +knn.train(features_train) Labels labels_predict = knn.apply(features_test) RealVector output = labels_predict.get_real_vector("labels") #![train_and_apply] diff --git a/src/shogun/classifier/NearestCentroid.cpp b/src/shogun/classifier/NearestCentroid.cpp index 1598df603d7..16ff2913059 100644 --- a/src/shogun/classifier/NearestCentroid.cpp +++ b/src/shogun/classifier/NearestCentroid.cpp @@ -17,43 +17,24 @@ namespace shogun{ NearestCentroid::NearestCentroid() : DistanceMachine() { - init(); } - NearestCentroid::NearestCentroid(const std::shared_ptr& d, const std::shared_ptr& trainlab) : DistanceMachine() + NearestCentroid::NearestCentroid(const std::shared_ptr& d) : DistanceMachine() { - init(); ASSERT(d) - ASSERT(trainlab) set_distance(d); - set_labels(trainlab); } NearestCentroid::~NearestCentroid() { } - void NearestCentroid::init() - { - m_shrinking=0; - m_is_trained=false; - } - - bool NearestCentroid::train_machine(std::shared_ptr data) { - ASSERT(m_labels) - ASSERT(distance) - if (data) - { - if (m_labels->get_num_labels() != data->get_num_vectors()) - error("Number of training vectors does not match number of labels"); - distance->init(data, data); - } - else - { - data = distance->get_lhs(); - } + require(distance, "Distance not set"); + require(m_labels->get_num_labels() == data->get_num_vectors(), + "Number of training vectors does not match number of labels"); + distance->init(data, data); auto multiclass_labels = m_labels->as(); auto dense_data = data->as>(); @@ -83,7 +64,7 @@ namespace shogun{ linalg::scale(centroids, centroids, scale); auto centroids_feats = std::make_shared>(centroids); - + m_centroids = centroids_feats; m_is_trained=true; distance->init(centroids_feats, distance->get_rhs()); diff --git a/src/shogun/classifier/NearestCentroid.h b/src/shogun/classifier/NearestCentroid.h index ecb4e87653d..14fd4af4489 100644 --- a/src/shogun/classifier/NearestCentroid.h +++ b/src/shogun/classifier/NearestCentroid.h @@ -45,7 +45,7 @@ class NearestCentroid : public DistanceMachine{ * @param distance distance * @param trainlab labels for training */ - NearestCentroid(const std::shared_ptr& distance, const std::shared_ptr& trainlab); + NearestCentroid(const std::shared_ptr& distance); /** Destructor */ @@ -92,26 +92,19 @@ class NearestCentroid : public DistanceMachine{ */ bool train_machine(std::shared_ptr data=NULL) override; - /** Stores feature data of underlying model. - * - * Sets centroids as lhs - */ - -private: - void init(); protected: /// number of classes (i.e. number of values labels can take) int32_t m_num_classes; /// Shrinking parameter - float64_t m_shrinking; + float64_t m_shrinking = 0; /// The centroids of the trained features std::shared_ptr> m_centroids; /// Tells if the classifier has been trained or not - bool m_is_trained; + bool m_is_trained = false; }; } diff --git a/tests/unit/classifier/NearestCentroid_unittest.cc b/tests/unit/classifier/NearestCentroid_unittest.cc new file mode 100644 index 00000000000..036a433c549 --- /dev/null +++ b/tests/unit/classifier/NearestCentroid_unittest.cc @@ -0,0 +1,31 @@ +/* + * This software is distributed under BSD 3-clause license (see LICENSE file). + * + * Authors: Yuhui Liu + */ +#include +#include +#include +#include + +using namespace shogun; +TEST(NearestCentroid, fit_and_predict) +{ + SGMatrix X{{-10, -1}, {-2, -1}, {-3, -2}, + {1, 1}, {2, 1}, {3, 2}}; + SGVector y{0, 0, 0, 1, 1, 1}; + + auto train_data = std::make_shared>(X); + auto train_labels = std::make_shared(y); + auto distance = std::make_shared(); + + SGMatrix t{{3, 2}, {-10, -1}, {-100, 100}}; + auto test_data = std::make_shared>(t); + auto clf = std::make_shared(distance); + clf->train(train_data, train_labels); + auto result_labels = clf->apply(test_data); + auto result = result_labels->as()->get_labels(); + EXPECT_EQ(result[0], 1); + EXPECT_EQ(result[1], 0); + EXPECT_EQ(result[2], 0); +} \ No newline at end of file From a668174e6403a4a8ac0642e7b71b0ee2c02b2bcb Mon Sep 17 00:00:00 2001 From: LiuYuhui Date: Wed, 5 Aug 2020 16:38:12 +0800 Subject: [PATCH 8/9] Refactor BaggingMachine (#5103) * make BaggingMachine stateless * change get_oob_error to lambda * fix meta example * fix segfault --- .../multiclass/Tree/DecisionTrees.ipynb | 11 +-- .../multiclass/Tree/TreeEnsemble.ipynb | 19 ++-- examples/meta/src/multiclass/cartree.sg.in | 3 +- .../meta/src/multiclass/random_forest.sg.in | 4 +- examples/meta/src/regression/cartree.sg.in | 4 +- .../regression/random_forest_regression.sg.in | 7 +- src/shogun/machine/BaggingMachine.cpp | 95 ++++++++----------- src/shogun/machine/BaggingMachine.h | 36 ++++--- src/shogun/machine/RandomForest.cpp | 33 ++----- src/shogun/machine/RandomForest.h | 14 +-- src/shogun/machine/StochasticGBMachine.cpp | 3 +- src/shogun/multiclass/tree/CARTree.cpp | 23 ++--- src/shogun/multiclass/tree/CARTree.h | 19 ++-- tests/unit/machine/MockMachine.h | 1 + .../multiclass/BaggingMachine_unittest.cc | 20 ++-- .../unit/multiclass/tree/CARTree_unittest.cc | 21 ++-- .../multiclass/tree/RandomCARTree_unittest.cc | 3 +- .../multiclass/tree/RandomForest_unittest.cc | 18 ++-- 18 files changed, 135 insertions(+), 199 deletions(-) diff --git a/doc/ipython-notebooks/multiclass/Tree/DecisionTrees.ipynb b/doc/ipython-notebooks/multiclass/Tree/DecisionTrees.ipynb index 29de81445cf..e229a277421 100644 --- a/doc/ipython-notebooks/multiclass/Tree/DecisionTrees.ipynb +++ b/doc/ipython-notebooks/multiclass/Tree/DecisionTrees.ipynb @@ -974,10 +974,9 @@ " c = sg.create_machine(\"CARTree\", nominal=feat_types,\n", " mode=problem_type,\n", " folds=num_folds,\n", - " apply_cv_pruning=use_cv_pruning,\n", - " labels=labels)\n", + " apply_cv_pruning=use_cv_pruning)\n", " # train using training features\n", - " c.train(feats)\n", + " c.train(feats, labels)\n", " \n", " return c\n", "\n", @@ -1408,7 +1407,7 @@ " c = sg.create_machine(\"CHAIDTree\", dependent_vartype=dependent_var_type,\n", " feature_types=feature_types,\n", " num_breakpoints=num_bins,\n", - " labels=labels)\n", + " labels = labels)\n", " # train using training features\n", " c.train(feats)\n", " \n", @@ -1722,9 +1721,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.6.9" } }, "nbformat": 4, "nbformat_minor": 1 -} \ No newline at end of file +} diff --git a/doc/ipython-notebooks/multiclass/Tree/TreeEnsemble.ipynb b/doc/ipython-notebooks/multiclass/Tree/TreeEnsemble.ipynb index d2991dacf25..84231482df7 100644 --- a/doc/ipython-notebooks/multiclass/Tree/TreeEnsemble.ipynb +++ b/doc/ipython-notebooks/multiclass/Tree/TreeEnsemble.ipynb @@ -112,8 +112,7 @@ "outputs": [], "source": [ "# train forest\n", - "rand_forest.put('labels', train_labels)\n", - "rand_forest.train(train_feats)\n", + "rand_forest.train(train_feats, train_labels)\n", "\n", "# load test dataset\n", "testfeat_file= os.path.join(SHOGUN_DATA_DIR, 'uci/letter/test_fm_letter.dat')\n", @@ -142,9 +141,8 @@ " c=sg.create_machine(\"CARTree\", nominal=feature_types,\n", " mode=problem_type,\n", " folds=2,\n", - " apply_cv_pruning=False,\n", - " labels=train_labels)\n", - " c.train(train_feats)\n", + " apply_cv_pruning=False)\n", + " c.train(train_feats, train_labels)\n", " \n", " return c\n", "\n", @@ -213,8 +211,7 @@ "source": [ "def get_rf_accuracy(num_trees,rand_subset_size):\n", " rf=setup_random_forest(num_trees,rand_subset_size,comb_rule,feat_types)\n", - " rf.put('labels', train_labels)\n", - " rf.train(train_feats)\n", + " rf.train(train_feats, train_labels)\n", " out_test=rf.apply_multiclass(test_feats)\n", " acc=sg.create_evaluation(\"MulticlassAccuracy\")\n", " return acc.evaluate(out_test,test_labels)" @@ -365,8 +362,7 @@ "outputs": [], "source": [ "rf=setup_random_forest(100,2,comb_rule,feat_types)\n", - "rf.put('labels', train_labels)\n", - "rf.train(train_feats)\n", + "rf.train(train_feats, train_labels)\n", " \n", "# set evaluation strategy\n", "rf.put(\"oob_evaluation_metric\", sg.create_evaluation(\"MulticlassAccuracy\"))\n", @@ -411,8 +407,7 @@ "def get_oob_errors_wine(num_trees,rand_subset_size):\n", " feat_types=np.array([False]*13)\n", " rf=setup_random_forest(num_trees,rand_subset_size,sg.create_combination_rule(\"MajorityVote\"),feat_types)\n", - " rf.put('labels', train_labels)\n", - " rf.train(train_feats)\n", + " rf.train(train_feats, train_labels)\n", " rf.put(\"oob_evaluation_metric\", sg.create_evaluation(\"MulticlassAccuracy\"))\n", " return rf.get(\"oob_error\") \n", "\n", @@ -494,7 +489,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/examples/meta/src/multiclass/cartree.sg.in b/examples/meta/src/multiclass/cartree.sg.in index d842ac93624..2064342f8a3 100644 --- a/examples/meta/src/multiclass/cartree.sg.in +++ b/examples/meta/src/multiclass/cartree.sg.in @@ -19,11 +19,10 @@ ft[1] = False #![create_instance] Machine classifier = create_machine("CARTree", nominal = ft,mode = enum EProblemType.PT_MULTICLASS, folds=5, apply_cv_pruning=True, seed=1) -classifier.set_labels(labels_train) #![create_instance] #![train_and_apply] -classifier.train(features_train) +classifier.train(features_train, labels_train) MulticlassLabels labels_predict = classifier.apply_multiclass(features_test) #![train_and_apply] diff --git a/examples/meta/src/multiclass/random_forest.sg.in b/examples/meta/src/multiclass/random_forest.sg.in index ed6a09bee40..540c8b75e6b 100644 --- a/examples/meta/src/multiclass/random_forest.sg.in +++ b/examples/meta/src/multiclass/random_forest.sg.in @@ -15,13 +15,13 @@ CombinationRule m_vote = create_combination_rule("MajorityVote") #![create_combination_rule] #![create_instance] -Machine rand_forest = create_machine("RandomForest", labels=labels_train, num_bags=100, combination_rule=m_vote, seed=1) +Machine rand_forest = create_machine("RandomForest", num_bags=100, combination_rule=m_vote, seed=1) Parallel p = rand_forest.get_global_parallel() p.set_num_threads(1) #![create_instance] #![train_and_apply] -rand_forest.train(features_train) +rand_forest.train(features_train, labels_train) MulticlassLabels labels_predict = rand_forest.apply_multiclass(features_test) #![train_and_apply] diff --git a/examples/meta/src/regression/cartree.sg.in b/examples/meta/src/regression/cartree.sg.in index 8c9846b8f8e..faea334f478 100644 --- a/examples/meta/src/regression/cartree.sg.in +++ b/examples/meta/src/regression/cartree.sg.in @@ -14,11 +14,11 @@ ft[0] = False #![set_attribute_types] #![create_machine] -Machine cartree = create_machine("CARTree", labels=labels_train, nominal=ft, mode=enum EProblemType.PT_REGRESSION, folds=5, apply_cv_pruning=True, seed=1) +Machine cartree = create_machine("CARTree", nominal=ft, mode=enum EProblemType.PT_REGRESSION, folds=5, apply_cv_pruning=True, seed=1) #![create_machine] #![train_and_apply] -cartree.train(feats_train) +cartree.train(feats_train, labels_train) Labels labels_predict = cartree.apply(feats_test) #![train_and_apply] diff --git a/examples/meta/src/regression/random_forest_regression.sg.in b/examples/meta/src/regression/random_forest_regression.sg.in index 346d73a0119..1828ebb69aa 100644 --- a/examples/meta/src/regression/random_forest_regression.sg.in +++ b/examples/meta/src/regression/random_forest_regression.sg.in @@ -15,12 +15,12 @@ CombinationRule mean_rule = create_combination_rule("MeanRule") #![create_combination_rule] #![create_instance] -Machine rand_forest = create_machine("RandomForest", labels=labels_train, num_bags=5, seed=1, combination_rule=mean_rule) +Machine rand_forest = create_machine("RandomForest", num_bags=5, seed=1, combination_rule=mean_rule) #![create_instance] #![train_and_apply] -rand_forest.train(features_train) -RegressionLabels labels_predict = rand_forest.apply_regression(features_test) +rand_forest.train(features_train, labels_train) +Labels labels_predict = rand_forest.apply_regression(features_test) #![train_and_apply] #![evaluate_error] @@ -32,3 +32,4 @@ real mserror = mse.evaluate(labels_predict, labels_test) # additional integration testing variables RealVector output = labels_predict.get_real_vector("labels") + diff --git a/src/shogun/machine/BaggingMachine.cpp b/src/shogun/machine/BaggingMachine.cpp index 632a5150ad6..cbfd444f313 100644 --- a/src/shogun/machine/BaggingMachine.cpp +++ b/src/shogun/machine/BaggingMachine.cpp @@ -24,12 +24,6 @@ BaggingMachine::BaggingMachine() : RandomMixin() register_parameters(); } -BaggingMachine::BaggingMachine(std::shared_ptr features, std::shared_ptr labels) - : BaggingMachine() -{ - set_labels(std::move(labels)); - m_features = std::move(features); -} std::shared_ptr BaggingMachine::apply_binary(std::shared_ptr data) { @@ -48,21 +42,12 @@ std::shared_ptr BaggingMachine::apply_multiclass(std::shared_p { SGMatrix bagged_outputs = apply_outputs_without_combination(data); - - require(m_labels, "Labels not set."); - require( - m_labels->get_label_type() == LT_MULTICLASS, - "Labels ({}) are not compatible with multiclass.", - m_labels->get_name()); - - auto labels_multiclass = std::dynamic_pointer_cast(m_labels); auto num_samples = bagged_outputs.size() / m_num_bags; - auto num_classes = labels_multiclass->get_num_classes(); auto pred = std::make_shared(num_samples); - pred->allocate_confidences_for(num_classes); + pred->allocate_confidences_for(m_num_classes); - SGMatrix class_probabilities(num_classes, num_samples); + SGMatrix class_probabilities(m_num_classes, num_samples); class_probabilities.zero(); for (auto i = 0; i < num_samples; ++i) @@ -125,27 +110,24 @@ BaggingMachine::apply_outputs_without_combination(std::shared_ptr data return output; } -bool BaggingMachine::train_machine(std::shared_ptr data) +bool BaggingMachine::train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) { require(m_machine != NULL, "Machine is not set!"); require(m_num_bags > 0, "Number of bag is not set!"); - - if (data) + m_num_vectors = data->get_num_vectors(); + if(auto multiclass_labs = std::dynamic_pointer_cast(labs)) { - m_features = data; - - ASSERT(m_features->get_num_vectors() == m_labels->get_num_labels()); + m_num_classes = multiclass_labs->get_num_classes(); } - // if bag size is not provided, set it equal to number of training vectors if (m_bag_size == 0) - m_bag_size = m_features->get_num_vectors(); + m_bag_size = data->get_num_vectors(); // clear the array, if previously trained m_bags.clear(); // reset the oob index vector - m_all_oob_idx = SGVector(m_features->get_num_vectors()); + m_all_oob_idx = SGVector(data->get_num_vectors()); m_all_oob_idx.zero(); @@ -160,24 +142,27 @@ bool BaggingMachine::train_machine(std::shared_ptr data) { auto c=std::dynamic_pointer_cast(m_machine->clone()); ASSERT(c != NULL); - SGVector idx( - rnd_indicies.get_column_vector(i), m_bag_size, false); + SGVector idx(rnd_indicies.get_column_vector(i), m_bag_size, false); std::shared_ptr features; std::shared_ptr labels; if (env()->get_num_threads() == 1) { - features = m_features; - labels = m_labels; + features = data; + labels = labs; } else { - features = m_features->shallow_subset_copy(); - labels = m_labels->shallow_subset_copy(); + features = data->shallow_subset_copy(); + labels = labs->shallow_subset_copy(); } - - labels->add_subset(idx); +#pragma omp critical + { + labels->add_subset(idx); + features->add_subset(idx); + } + /* TODO: if it's a binary labeling ensure that there's always samples of both classes @@ -194,12 +179,15 @@ bool BaggingMachine::train_machine(std::shared_ptr data) } } */ - features->add_subset(idx); + set_machine_parameters(c, idx); - c->set_labels(labels); - c->train(features); - features->remove_subset(); - labels->remove_subset(); + c->train(features, labels); + #pragma omp critical + { + features->remove_subset(); + labels->remove_subset(); + } + #pragma omp critical { @@ -214,7 +202,7 @@ bool BaggingMachine::train_machine(std::shared_ptr data) pb.print_progress(); } pb.complete(); - + get_oob_error_lambda = [=](){return get_oob_error_impl(data, labs);}; return true; } @@ -224,7 +212,6 @@ void BaggingMachine::set_machine_parameters(std::shared_ptr m, SGVector void BaggingMachine::register_parameters() { - SG_ADD(&m_features, kFeatures, "Train features for bagging"); SG_ADD( &m_num_bags, kNBags, "Number of bags", ParameterProperties::HYPER); SG_ADD( @@ -275,9 +262,7 @@ void BaggingMachine::set_machine(std::shared_ptr machine) void BaggingMachine::init() { m_machine = nullptr; - m_features = nullptr; m_combination_rule = nullptr; - m_labels = nullptr; m_num_bags = 0; m_bag_size = 0; m_all_oob_idx = SGVector(); @@ -294,7 +279,7 @@ std::shared_ptr BaggingMachine::get_combination_rule() const return m_combination_rule; } -float64_t BaggingMachine::get_oob_error() const +float64_t BaggingMachine::get_oob_error_impl(const std::shared_ptr& data, const std::shared_ptr& labs) const { require( m_oob_evaluation_metric, "Out of bag evaluation metric is not set!"); @@ -302,8 +287,8 @@ float64_t BaggingMachine::get_oob_error() const require(m_bags.size() > 0, "BaggingMachine is not trained!"); SGMatrix output( - m_features->get_num_vectors(), m_bags.size()); - if (m_labels->get_label_type() == LT_REGRESSION) + m_num_vectors, m_bags.size()); + if (labs->get_label_type() == LT_REGRESSION) output.zero(); else output.set_const(NAN); @@ -318,9 +303,9 @@ float64_t BaggingMachine::get_oob_error() const auto current_oob = m_oob_indices[i]; SGVector oob(current_oob.data(), current_oob.size(), false); - m_features->add_subset(oob); + data->add_subset(oob); - auto l = m->apply(m_features); + auto l = m->apply(data); SGVector lv; if (l!=NULL) lv = std::dynamic_pointer_cast(l)->get_labels(); @@ -331,14 +316,14 @@ float64_t BaggingMachine::get_oob_error() const for (index_t j = 0; j < oob.vlen; j++) output(oob[j], i) = lv[j]; - m_features->remove_subset(); + data->remove_subset(); } std::vector idx; - for (index_t i = 0; i < m_features->get_num_vectors(); i++) + for (index_t i = 0; i < data->get_num_vectors(); i++) { if (m_all_oob_idx[i]) idx.push_back(i); @@ -350,7 +335,7 @@ float64_t BaggingMachine::get_oob_error() const lab[i] = combined[idx[i]]; std::shared_ptr predicted = NULL; - switch (m_labels->get_label_type()) + switch (labs->get_label_type()) { case LT_BINARY: predicted = std::make_shared(lab); @@ -369,16 +354,16 @@ float64_t BaggingMachine::get_oob_error() const } - m_labels->add_subset(SGVector(idx.data(), idx.size(), false)); - float64_t res = m_oob_evaluation_metric->evaluate(predicted, m_labels); - m_labels->remove_subset(); + labs->add_subset(SGVector(idx.data(), idx.size(), false)); + float64_t res = m_oob_evaluation_metric->evaluate(predicted, labs); + labs->remove_subset(); return res; } std::vector BaggingMachine::get_oob_indices(const SGVector& in_bag) { - SGVector out_of_bag(m_features->get_num_vectors()); + SGVector out_of_bag(m_num_vectors); out_of_bag.set_const(true); // mark the ones that are in_bag diff --git a/src/shogun/machine/BaggingMachine.h b/src/shogun/machine/BaggingMachine.h index a08ff0fb1f2..a4693ce891d 100644 --- a/src/shogun/machine/BaggingMachine.h +++ b/src/shogun/machine/BaggingMachine.h @@ -30,19 +30,11 @@ namespace shogun /** default ctor */ BaggingMachine(); - /** - * constructor - * - * @param features training features - * @param labels training labels - */ - BaggingMachine(std::shared_ptr features, std::shared_ptr labels); - ~BaggingMachine() override = default; - std::shared_ptr apply_binary(std::shared_ptr data=NULL) override; - std::shared_ptr apply_multiclass(std::shared_ptr data=NULL) override; - std::shared_ptr apply_regression(std::shared_ptr data=NULL) override; + std::shared_ptr apply_binary(std::shared_ptr data) override; + std::shared_ptr apply_multiclass(std::shared_ptr data) override; + std::shared_ptr apply_regression(std::shared_ptr data) override; /** * Set number of bags/machine to create @@ -118,8 +110,10 @@ namespace shogun * @param eval Evaluation method to use for calculating the error * @return out-of-bag error. */ - float64_t get_oob_error() const; - + float64_t get_oob_error() const + { + return get_oob_error_lambda(); + } /** name **/ const char* get_name() const override { @@ -127,7 +121,7 @@ namespace shogun } protected: - bool train_machine(std::shared_ptr data=NULL) override; + bool train_machine(const std::shared_ptr&, const std::shared_ptr& labs) override; /** * sets parameters of Machine - useful in Random Forest @@ -170,13 +164,11 @@ namespace shogun std::vector get_oob_indices(const SGVector& in_bag); + float64_t get_oob_error_impl(const std::shared_ptr& data, const std::shared_ptr& labs) const; protected: /** bags array */ std::vector> m_bags; - /** features to train on */ - std::shared_ptr m_features; - /** machine to use for bagging */ std::shared_ptr m_machine; @@ -198,9 +190,15 @@ namespace shogun /** metric to calculate the oob error */ std::shared_ptr m_oob_evaluation_metric; + int32_t m_num_classes; + + int32_t m_num_vectors; + + std::function get_oob_error_lambda; + + #ifndef SWIG public: - static constexpr std::string_view kFeatures = "features"; static constexpr std::string_view kNBags = "num_bags"; static constexpr std::string_view kBagSize = "bag_size"; static constexpr std::string_view kBags = "bags"; @@ -208,8 +206,8 @@ namespace shogun static constexpr std::string_view kAllOobIdx = "all_oob_idx"; static constexpr std::string_view kOobIndices = "oob_indices"; static constexpr std::string_view kMachine = "machine"; + static constexpr std::string_view kOobEvaluationMetric = "oob_evaluation_metric"; static constexpr std::string_view kOobError = "oob_error"; - static constexpr std::string_view kOobEvaluationMetric = "oob_evaluation_metric"; #endif }; } // namespace shogun diff --git a/src/shogun/machine/RandomForest.cpp b/src/shogun/machine/RandomForest.cpp index 410d99379f0..436fe1b78c6 100644 --- a/src/shogun/machine/RandomForest.cpp +++ b/src/shogun/machine/RandomForest.cpp @@ -53,26 +53,12 @@ RandomForest::RandomForest(int32_t rand_numfeats, int32_t num_bags) m_machine->as()->set_feature_subset_size(rand_numfeats); } -RandomForest::RandomForest(std::shared_ptr features, std::shared_ptr labels, int32_t num_bags, int32_t rand_numfeats) -: BaggingMachine() -{ - init(); - m_features=std::move(features); - set_labels(std::move(labels)); - - set_num_bags(num_bags); - - if (rand_numfeats>0) - m_machine->as()->set_feature_subset_size(rand_numfeats); -} -RandomForest::RandomForest(std::shared_ptr features, std::shared_ptr labels, SGVector weights, int32_t num_bags, int32_t rand_numfeats) +RandomForest::RandomForest(SGVector weights, int32_t num_bags, int32_t rand_numfeats) : BaggingMachine() { init(); - m_features=std::move(features); - set_labels(std::move(labels)); m_weights=weights; set_num_bags(num_bags); @@ -163,24 +149,17 @@ void RandomForest::set_machine_parameters(std::shared_ptr m, SGVectorset_machine_problem_type(m_machine->as()->get_machine_problem_type()); } -bool RandomForest::train_machine(std::shared_ptr data) +bool RandomForest::train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) { - if (data) - { - m_features = data; - } - - require(m_features, "Training features not set!"); - - m_machine->as()->pre_sort_features(m_features, m_sorted_transposed_feats, m_sorted_indices); - return BaggingMachine::train_machine(); + m_machine->as()->pre_sort_features(data, m_sorted_transposed_feats, m_sorted_indices); + m_num_features = data->as>()->get_num_features(); + return BaggingMachine::train_machine(data, labs); } SGVector RandomForest::get_feature_importances() const { - auto num_feats = - m_features->as>()->get_num_features(); + const auto& num_feats = m_num_features; SGVector feat_importances(num_feats); feat_importances.zero(); for (size_t i = 0; i < m_bags.size(); i++) diff --git a/src/shogun/machine/RandomForest.h b/src/shogun/machine/RandomForest.h index 8990d5d25ee..e7eebbe93df 100644 --- a/src/shogun/machine/RandomForest.h +++ b/src/shogun/machine/RandomForest.h @@ -56,15 +56,6 @@ class RandomForest : public BaggingMachine */ RandomForest(int32_t num_rand_feats, int32_t num_bags=10); - /** constructor - * - * @param features training features - * @param labels training labels - * @param num_bags number of trees in forest - * @param num_rand_feats number of attributes chosen randomly during node split in candidate trees - */ - RandomForest(std::shared_ptr features, std::shared_ptr labels, int32_t num_bags=10, int32_t num_rand_feats=0); - /** constructor * * @param features training features @@ -73,7 +64,7 @@ class RandomForest : public BaggingMachine * @param num_bags number of trees in forest * @param num_rand_feats number of attributes chosen randomly during node split in candidate trees */ - RandomForest(std::shared_ptr features, std::shared_ptr labels, SGVector weights, int32_t num_bags=10, int32_t num_rand_feats=0); + RandomForest(SGVector weights, int32_t num_bags=10, int32_t num_rand_feats=0); /** destructor */ ~RandomForest() override; @@ -146,7 +137,7 @@ class RandomForest : public BaggingMachine protected: - bool train_machine(std::shared_ptr data=NULL) override; + bool train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) override; /** sets parameters of CARTree - sets machine labels and weights here * * @param m machine @@ -159,6 +150,7 @@ class RandomForest : public BaggingMachine void init(); private: + int32_t m_num_features; /** weights */ SGVector m_weights; diff --git a/src/shogun/machine/StochasticGBMachine.cpp b/src/shogun/machine/StochasticGBMachine.cpp index 9b8cee1bbce..a8be1f9e0a6 100644 --- a/src/shogun/machine/StochasticGBMachine.cpp +++ b/src/shogun/machine/StochasticGBMachine.cpp @@ -237,8 +237,7 @@ std::shared_ptr StochasticGBMachine::fit_model(const std::shared_ptrclone()->as(); // train cloned machine - c->set_labels(labels); - c->train(feats); + c->train(feats, labels); return c; } diff --git a/src/shogun/multiclass/tree/CARTree.cpp b/src/shogun/multiclass/tree/CARTree.cpp index 217081ed3cc..780f4b1edc6 100644 --- a/src/shogun/multiclass/tree/CARTree.cpp +++ b/src/shogun/multiclass/tree/CARTree.cpp @@ -75,17 +75,6 @@ CARTree::~CARTree() { } -void CARTree::set_labels(std::shared_ptr lab) -{ - if (lab->get_label_type()==LT_MULTICLASS) - set_machine_problem_type(PT_MULTICLASS); - else if (lab->get_label_type()==LT_REGRESSION) - set_machine_problem_type(PT_REGRESSION); - else - error("label type supplied is not supported"); - - m_labels=lab; -} void CARTree::set_machine_problem_type(EProblemType mode) { @@ -255,11 +244,11 @@ bool CARTree::weights_set() return m_weights.size() != 0; } -bool CARTree::train_machine(std::shared_ptr data) +bool CARTree::train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) { require(data,"Data required for training"); require(data->get_feature_class()==C_DENSE,"Dense data required for training"); - + set_machine_problem_type(labs); auto dense_features = data->as>(); auto num_features = dense_features->get_num_features(); auto num_vectors = dense_features->get_num_vectors(); @@ -292,12 +281,12 @@ bool CARTree::train_machine(std::shared_ptr data) linalg::set_const(m_nominal, false); } - auto dense_labels = m_labels->as(); + auto dense_labels = labs->as(); set_root(CARTtrain(dense_features,m_weights,dense_labels,0)); if (m_apply_cv_pruning) { - prune_by_cross_validation(dense_features,m_folds); + prune_by_cross_validation(dense_features, labs, m_folds); } // compute feature importances and normalize it if (m_root) @@ -1223,7 +1212,7 @@ std::shared_ptr CARTree::apply_from_current_node(const std::shared_ptr>& data, int32_t folds) +void CARTree::prune_by_cross_validation(const std::shared_ptr>& data, const std::shared_ptr& labs, int32_t folds) { auto num_vecs=data->get_num_vectors(); @@ -1254,7 +1243,7 @@ void CARTree::prune_by_cross_validation(const std::shared_ptr subset(train_indices.data(),train_indices.size(),false); - auto dense_labels = m_labels->as(); + auto dense_labels = labs->as(); auto feats_train = view(data, subset); auto labels_train = view(dense_labels, subset); SGVector subset_weights(train_indices.size()); diff --git a/src/shogun/multiclass/tree/CARTree.h b/src/shogun/multiclass/tree/CARTree.h index 7a90eb6e259..4c341176da5 100644 --- a/src/shogun/multiclass/tree/CARTree.h +++ b/src/shogun/multiclass/tree/CARTree.h @@ -105,11 +105,6 @@ class CARTree : public RandomMixin> /** destructor */ ~CARTree() override; - /** set labels - automagically switch machine problem type based on type of labels supplied - * @param lab labels - */ - void set_labels(std::shared_ptr lab) override; - /** get name * @return class name CARTree */ @@ -248,7 +243,7 @@ class CARTree : public RandomMixin> * @param data training data * @return true */ - bool train_machine(std::shared_ptr data=NULL) override; + bool train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) override; /** CARTtrain - recursive CART training method * @@ -387,7 +382,7 @@ class CARTree : public RandomMixin> * @param data training data * @param folds the integer V for V-fold cross validation */ - void prune_by_cross_validation(const std::shared_ptr>& data, int32_t folds); + void prune_by_cross_validation(const std::shared_ptr>& data, const std::shared_ptr& labs, int32_t folds); /** computes error in classification/regression * for classification it eveluates weight_missclassified/total_weight @@ -429,6 +424,16 @@ class CARTree : public RandomMixin> /** initializes members of class */ void init(); + + void set_machine_problem_type(const std::shared_ptr& labs) + { + if (labs->get_label_type()==LT_MULTICLASS) + set_machine_problem_type(PT_MULTICLASS); + else if (labs->get_label_type()==LT_REGRESSION) + set_machine_problem_type(PT_REGRESSION); + else + error("label type supplied is not supported"); + } public: /** denotes that a feature in a vector is missing MISSING = NOT_A_NUMBER */ static const float64_t MISSING; diff --git a/tests/unit/machine/MockMachine.h b/tests/unit/machine/MockMachine.h index d29c8532925..6b8479308ff 100644 --- a/tests/unit/machine/MockMachine.h +++ b/tests/unit/machine/MockMachine.h @@ -10,6 +10,7 @@ namespace shogun { public: MOCK_METHOD1(apply, std::shared_ptr(std::shared_ptr)); MOCK_METHOD1(train_machine, bool(std::shared_ptr)); + MOCK_METHOD2(train_machine, bool(const std::shared_ptr&, const std::shared_ptr&)); MOCK_CONST_METHOD1(clone, std::shared_ptr(ParameterProperties)); virtual const char* get_name() const { return "MockMachine"; } diff --git a/tests/unit/multiclass/BaggingMachine_unittest.cc b/tests/unit/multiclass/BaggingMachine_unittest.cc index ea3e3388c93..3a2145eb1ba 100644 --- a/tests/unit/multiclass/BaggingMachine_unittest.cc +++ b/tests/unit/multiclass/BaggingMachine_unittest.cc @@ -79,7 +79,7 @@ TEST_F(BaggingMachineTest, mock_train) auto features = std::make_shared>(); auto labels = std::make_shared>(); - auto bm = std::make_shared(features, labels); + auto bm = std::make_shared(); auto mm = std::make_shared>(); auto mv = std::make_shared(); @@ -90,7 +90,7 @@ TEST_F(BaggingMachineTest, mock_train) bm->set_combination_rule(mv); bm->put("seed", seed); - ON_CALL(*mm, train_machine(_)) + ON_CALL(*mm, train_machine(_, _)) .WillByDefault(Return(true)); ON_CALL(*features, get_num_vectors()) @@ -103,13 +103,13 @@ TEST_F(BaggingMachineTest, mock_train) .Times(1) .WillRepeatedly(Return(mm)); - EXPECT_CALL(*mm, train_machine(_)) + EXPECT_CALL(*mm, train_machine(_, _)) .Times(1) .WillRepeatedly(Return(true)); } } - bm->train(); + bm->train(features_train, labels_train); EXPECT_TRUE(Mock::VerifyAndClearExpectations(mm.get())); } @@ -120,7 +120,7 @@ TEST_F(BaggingMachineTest, classify_CART) auto cv=std::make_shared(); cart->set_feature_types(ft); - auto c = std::make_shared(features_train, labels_train); + auto c = std::make_shared(); env()->set_num_threads(1); c->set_machine(cart); @@ -128,7 +128,7 @@ TEST_F(BaggingMachineTest, classify_CART) c->set_num_bags(10); c->set_combination_rule(cv); c->put("seed", seed); - c->train(features_train); + c->train(features_train, labels_train); auto result = c->apply_multiclass(features_test); SGVector res_vector=result->get_labels(); @@ -151,14 +151,14 @@ TEST_F(BaggingMachineTest, output_binary) auto cv = std::make_shared(); cart->set_feature_types(ft); - auto c = std::make_shared(features_train, labels_train); + auto c = std::make_shared(); env()->set_num_threads(1); c->set_machine(cart); c->set_bag_size(14); c->set_num_bags(10); c->set_combination_rule(cv); c->put("seed", seed); - c->train(features_train); + c->train(features_train, labels_train); auto result = c->apply_binary(features_test); SGVector res_vector = result->get_labels(); @@ -185,13 +185,13 @@ TEST_F(BaggingMachineTest, output_multiclass_probs_sum_to_one) auto cv = std::make_shared(); cart->set_feature_types(ft); - auto c = std::make_shared(features_train, labels_train); + auto c = std::make_shared(); c->set_machine(cart); c->set_bag_size(14); c->set_num_bags(10); c->set_combination_rule(cv); c->put("seed", seed); - c->train(features_train); + c->train(features_train, labels_train); auto result = c->apply_multiclass(features_test); diff --git a/tests/unit/multiclass/tree/CARTree_unittest.cc b/tests/unit/multiclass/tree/CARTree_unittest.cc index 2af2a21702f..390e33995d9 100644 --- a/tests/unit/multiclass/tree/CARTree_unittest.cc +++ b/tests/unit/multiclass/tree/CARTree_unittest.cc @@ -155,9 +155,8 @@ TEST(CARTree, classify_nominal) auto labels=std::make_shared(lab); auto c=std::make_shared(); - c->set_labels(labels); c->set_feature_types(ft); - c->train(feats); + c->train(feats, labels); SGMatrix test(4,5); test(0,0)=overcast; @@ -218,8 +217,7 @@ TEST(CARTree, comparable_with_sklearn) auto labels = std::make_shared(y); auto c = std::make_shared(); - c->set_labels(labels); - c->train(feats); + c->train(feats, labels); auto feat_import = c->get_feature_importance(); // those data are generated by below sklearn program EXPECT_NEAR(0.111111, feat_import[0], 0.00001); @@ -342,7 +340,7 @@ TEST(CARTree, classify_non_nominal) auto c=std::make_shared(); c->set_labels(labels); c->set_feature_types(ft); - c->train(feats); + c->train(feats, labels); SGMatrix test(4,5); test(0,0)=overcast; @@ -445,7 +443,7 @@ TEST(CARTree, handle_missing_nominal) auto c=std::make_shared(); c->set_labels(labels); c->set_feature_types(ft); - c->train(feats); + c->train(feats, labels); auto root=c->get_root()->as>(); auto left=root->left(); @@ -516,9 +514,8 @@ TEST(CARTree, handle_missing_continuous) auto labels=std::make_shared(lab); auto c=std::make_shared(); - c->set_labels(labels); c->set_feature_types(ft); - c->train(feats); + c->train(feats, labels); auto root=c->get_root()->as>(); auto left=root->left(); @@ -553,9 +550,8 @@ TEST(CARTree, form_t1_test) auto labels=std::make_shared(lab); auto c=std::make_shared(); - c->set_labels(labels); c->set_feature_types(ft); - c->train(feats); + c->train(feats, labels); auto root=c->get_root(); EXPECT_EQ(2,root->data.num_leaves); @@ -643,9 +639,8 @@ TEST(CARTree,cv_prune_simple) auto labels=std::make_shared(lab); auto c=std::make_shared(); - c->set_labels(labels); c->set_feature_types(ft); - c->train(feats); + c->train(feats, labels); auto root=c->get_root()->as>(); @@ -654,7 +649,7 @@ TEST(CARTree,cv_prune_simple) c->set_num_folds(2); c->set_cv_pruning(true); - c->train(feats); + c->train(feats, labels); root=c->get_root()->as>(); diff --git a/tests/unit/multiclass/tree/RandomCARTree_unittest.cc b/tests/unit/multiclass/tree/RandomCARTree_unittest.cc index 599d63fb12d..8961d72d042 100644 --- a/tests/unit/multiclass/tree/RandomCARTree_unittest.cc +++ b/tests/unit/multiclass/tree/RandomCARTree_unittest.cc @@ -153,11 +153,10 @@ TEST(RandomCARTree, classify_nominal) auto labels=std::make_shared(lab); auto c=std::make_shared(); - c->set_labels(labels); c->set_feature_types(ft); c->set_feature_subset_size(4); c->put("seed", seed); - c->train(feats); + c->train(feats, labels); SGMatrix test(4,5); test(0,0)=overcast; diff --git a/tests/unit/multiclass/tree/RandomForest_unittest.cc b/tests/unit/multiclass/tree/RandomForest_unittest.cc index 67766b111a9..82998d3781c 100644 --- a/tests/unit/multiclass/tree/RandomForest_unittest.cc +++ b/tests/unit/multiclass/tree/RandomForest_unittest.cc @@ -93,13 +93,13 @@ TEST_F(RandomForestTest, classify_nominal_test) { int32_t seed = 2343; auto c = - std::make_shared(weather_features_train, weather_labels_train, 100, 2); + std::make_shared(2, 100); c->set_feature_types(weather_ft); auto mv = std::make_shared(); c->set_combination_rule(mv); env()->set_num_threads(1); c->put("seed", seed); - c->train(weather_features_train); + c->train(weather_features_train, weather_labels_train); auto result = c->apply(weather_features_test)->as(); @@ -126,13 +126,13 @@ TEST_F(RandomForestTest, classify_non_nominal_test) weather_ft[3] = false; auto c = - std::make_shared(weather_features_train, weather_labels_train, 100, 2); + std::make_shared(2, 100); c->set_feature_types(weather_ft); auto mv = std::make_shared(); c->set_combination_rule(mv); env()->set_num_threads(1); c->put("seed", seed); - c->train(weather_features_train); + c->train(weather_features_train, weather_labels_train); auto result = c->apply(weather_features_test)->as(); @@ -147,7 +147,7 @@ TEST_F(RandomForestTest, classify_non_nominal_test) std::shared_ptr eval=std::make_shared(); c->put(RandomForest::kOobEvaluationMetric, eval); - EXPECT_NEAR(0.714285,c->get(RandomForest::kOobError),1e-6); + EXPECT_NEAR(0.7142857,c->get(RandomForest::kOobError),1e-6); } TEST_F(RandomForestTest, score_compare_sklearn_toydata) @@ -166,7 +166,7 @@ TEST_F(RandomForestTest, score_compare_sklearn_toydata) SGVector lab {0.0, 0.0, 1.0, 1.0}; auto labels_train = std::make_shared(lab); - auto c = std::make_shared(features_train, labels_train, 10, 2); + auto c = std::make_shared(2, 10); SGVector ft = SGVector(2); ft[0] = false; ft[1] = false; @@ -175,7 +175,7 @@ TEST_F(RandomForestTest, score_compare_sklearn_toydata) auto mr = std::make_shared(); c->set_combination_rule(mr); c->put("seed", seed); - c->train(features_train); + c->train(features_train, labels_train); auto result = c->apply_binary(features_train); SGVector res_vector = result->get_labels(); @@ -226,7 +226,7 @@ TEST_F(RandomForestTest, score_consistent_with_binary_trivial_data) std::make_shared>(test_data); auto c = - std::make_shared(features_train, labels_train, num_trees, 1); + std::make_shared(1, num_trees); SGVector ft = SGVector(1); ft[0] = false; c->set_feature_types(ft); @@ -234,7 +234,7 @@ TEST_F(RandomForestTest, score_consistent_with_binary_trivial_data) auto mr = std::make_shared(); c->set_combination_rule(mr); c->put("seed", seed); - c->train(features_train); + c->train(features_train, labels_train); auto result = c->apply_binary(features_test); SGVector res_vector = result->get_labels(); From 8ec52a45d60beaa11c309e2c420dabf4f5aca529 Mon Sep 17 00:00:00 2001 From: LiuYuhui Date: Mon, 10 Aug 2020 19:54:21 +0800 Subject: [PATCH 9/9] Refactor all machine (#5104) * refactor all machines * fix unit test * fix python legacy and jupyter notebook --- .../multiclass/Tree/DecisionTrees.ipynb | 17 +++-- .../neuralnets/autoencoders.ipynb | 8 +-- .../neuralnets/neuralnets_digits.ipynb | 17 ++--- .../neuralnets/rbms_dbns.ipynb | 5 +- .../meta/src/binary/domainadaptationsvm.sg.in | 8 +-- examples/meta/src/composite/ensemble.sg.in | 4 +- .../src/evaluation/cross_validation.sg.in | 2 +- examples/meta/src/multiclass/chaid_tree.sg.in | 3 +- .../meta/src/multiclass/relaxed_tree.sg.in | 3 +- .../convolutional_net_classification.sg.in | 4 +- .../feedforward_net_classification.sg.in | 4 +- .../feedforward_net_regression.sg.in | 4 +- examples/meta/src/regression/chaid_tree.sg.in | 4 +- .../python/kernel_histogram_word_string.py | 4 +- .../python/kernel_salzberg_word_string.py | 4 +- .../python/multiclass_c45classifiertree.py | 3 +- .../python/multiclass_id3classifiertree.py | 3 +- .../python/stochasticgbmachine.py | 3 +- .../python/structure_discrete_hmsvm_bmrm.py | 4 +- .../python/structure_factor_graph_model.py | 12 ++-- .../python/structure_graphcuts.py | 4 +- ..._hierarchical_multilabel_classification.py | 2 +- src/gpl | 2 +- src/shogun/classifier/PluginEstimate.cpp | 12 ++-- src/shogun/classifier/PluginEstimate.h | 4 +- src/shogun/classifier/mkl/MKLMulticlass.cpp | 2 - src/shogun/classifier/svm/LibLinear.cpp | 15 +---- src/shogun/classifier/svm/LibLinear.h | 2 + src/shogun/classifier/svm/WDSVMOcas.cpp | 25 +++---- src/shogun/classifier/svm/WDSVMOcas.h | 4 +- src/shogun/evaluation/CrossValidation.cpp | 10 +-- src/shogun/machine/BaggingMachine.cpp | 2 +- src/shogun/machine/Composite.h | 4 +- src/shogun/machine/EnsembleMachine.h | 22 +++--- src/shogun/machine/GLM.cpp | 25 +++---- src/shogun/machine/GLM.h | 5 +- src/shogun/machine/GaussianProcess.h | 2 +- src/shogun/machine/Machine.cpp | 39 ++--------- src/shogun/machine/Machine.h | 14 +--- src/shogun/machine/MulticlassMachine.cpp | 4 +- src/shogun/machine/NonParametricMachine.h | 29 ++++++-- src/shogun/machine/Pipeline.cpp | 39 ++--------- src/shogun/machine/Pipeline.h | 28 +++++++- src/shogun/machine/StochasticGBMachine.cpp | 11 ++- src/shogun/machine/StochasticGBMachine.h | 5 +- .../machine/StructuredOutputMachine.cpp | 8 --- src/shogun/machine/StructuredOutputMachine.h | 6 -- src/shogun/multiclass/GaussianNaiveBayes.cpp | 7 +- src/shogun/multiclass/GaussianNaiveBayes.h | 2 +- .../multiclass/tree/C45ClassifierTree.cpp | 5 +- .../multiclass/tree/C45ClassifierTree.h | 2 +- src/shogun/multiclass/tree/CHAIDTree.cpp | 4 +- src/shogun/multiclass/tree/CHAIDTree.h | 2 +- .../multiclass/tree/ID3ClassifierTree.cpp | 4 +- .../multiclass/tree/ID3ClassifierTree.h | 2 +- src/shogun/multiclass/tree/RelaxedTree.cpp | 67 ++++++++----------- src/shogun/multiclass/tree/RelaxedTree.h | 48 +++---------- src/shogun/neuralnets/NeuralNetwork.cpp | 36 +++------- src/shogun/neuralnets/NeuralNetwork.h | 10 +-- src/shogun/structure/FWSOSVM.cpp | 4 +- src/shogun/structure/FWSOSVM.h | 2 +- .../structure/FactorGraphDataGenerator.cpp | 2 +- src/shogun/structure/StochasticSOSVM.cpp | 5 +- src/shogun/structure/StochasticSOSVM.h | 3 +- .../transfer/multitask/LibLinearMTL.cpp | 4 +- .../unit/machine/EnsembleMachine_unittest.cc | 27 ++++---- tests/unit/machine/Pipeline_unittest.cc | 8 +-- .../machine/StochasticGBMachine_unittest.cc | 6 +- tests/unit/machine/glm_unittest.cc | 4 +- .../tree/C45ClassifierTree_unittest.cc | 15 ++--- .../unit/multiclass/tree/CARTree_unittest.cc | 2 - .../multiclass/tree/CHAIDTree_unittest.cc | 10 ++- .../tree/ID3ClassifierTree_unittest.cc | 6 +- .../unit/neuralnets/NeuralNetwork_unittest.cc | 12 ++-- .../structure/DualLibQPBMSOSVM_unittest.cc | 2 +- tests/unit/structure/SOSVM_unittest.cc | 4 +- 76 files changed, 285 insertions(+), 461 deletions(-) diff --git a/doc/ipython-notebooks/multiclass/Tree/DecisionTrees.ipynb b/doc/ipython-notebooks/multiclass/Tree/DecisionTrees.ipynb index e229a277421..2c0d16ae009 100644 --- a/doc/ipython-notebooks/multiclass/Tree/DecisionTrees.ipynb +++ b/doc/ipython-notebooks/multiclass/Tree/DecisionTrees.ipynb @@ -197,10 +197,10 @@ "outputs": [], "source": [ "# create ID3ClassifierTree object\n", - "id3 = sg.create_machine(\"ID3ClassifierTree\", labels=labels)\n", + "id3 = sg.create_machine(\"ID3ClassifierTree\")\n", "\n", "# learn the tree from training features\n", - "is_successful = id3.train(train_feats)" + "is_successful = id3.train(train_feats, labels)" ] }, { @@ -412,10 +412,10 @@ " train_lab = sg.create_labels(labels)\n", "\n", " # create ID3ClassifierTree object\n", - " id3 = sg.create_machine(\"ID3ClassifierTree\", labels=train_lab)\n", + " id3 = sg.create_machine(\"ID3ClassifierTree\")\n", "\n", " # learn the tree from training features\n", - " id3.train(train_feats)\n", + " id3.train(train_feats, train_lab)\n", "\n", " # apply to test dataset\n", " output = id3.apply(test_feats)\n", @@ -610,9 +610,9 @@ "# steps in C4.5 Tree training bundled together in a python method\n", "def train_tree(feats,types,labels):\n", " # C4.5 Tree object\n", - " tree = sg.create_machine(\"C45ClassifierTree\", labels=labels, m_nominal=types)\n", + " tree = sg.create_machine(\"C45ClassifierTree\", m_nominal=types)\n", " # supply training matrix and train\n", - " tree.train(feats)\n", + " tree.train(feats, labels)\n", " \n", " return tree\n", "\n", @@ -1406,10 +1406,9 @@ " # create CHAID tree object\n", " c = sg.create_machine(\"CHAIDTree\", dependent_vartype=dependent_var_type,\n", " feature_types=feature_types,\n", - " num_breakpoints=num_bins,\n", - " labels = labels)\n", + " num_breakpoints=num_bins)\n", " # train using training features\n", - " c.train(feats)\n", + " c.train(feats, labels)\n", " \n", " return c\n", "\n", diff --git a/doc/ipython-notebooks/neuralnets/autoencoders.ipynb b/doc/ipython-notebooks/neuralnets/autoencoders.ipynb index 32327fee204..ff6f4808fc1 100644 --- a/doc/ipython-notebooks/neuralnets/autoencoders.ipynb +++ b/doc/ipython-notebooks/neuralnets/autoencoders.ipynb @@ -276,8 +276,7 @@ "\n", "nn.put('max_num_epochs', 50)\n", "\n", - "nn.put('labels', Ytrain)\n", - "_ = nn.train(Xtrain)" + "_ = nn.train(Xtrain, Ytrain)" ] }, { @@ -404,10 +403,9 @@ "# train the network\n", "conv_nn.put('epsilon', 0.0)\n", "conv_nn.put('max_num_epochs', 50)\n", - "conv_nn.put('labels', Ytrain)\n", "\n", "# start training. this might take some time\n", - "_ = conv_nn.train(Xtrain)" + "_ = conv_nn.train(Xtrain, Ytrain)" ] }, { @@ -462,7 +460,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/doc/ipython-notebooks/neuralnets/neuralnets_digits.ipynb b/doc/ipython-notebooks/neuralnets/neuralnets_digits.ipynb index 4dca02f606a..0e15ba56f1c 100644 --- a/doc/ipython-notebooks/neuralnets/neuralnets_digits.ipynb +++ b/doc/ipython-notebooks/neuralnets/neuralnets_digits.ipynb @@ -236,8 +236,7 @@ "# uncomment this line to allow the training progress to be printed on the console\n", "#from shogun import MSG_INFO; net_no_reg.io.put('loglevel', MSG_INFO)\n", "\n", - "net_no_reg.put('labels', Ytrain)\n", - "net_no_reg.train(Xtrain) # this might take a while, depending on your machine\n", + "net_no_reg.train(Xtrain, Ytrain) # this might take a while, depending on your machine\n", "\n", "# compute accuracy on the validation set\n", "print(\"Without regularization, accuracy on the validation set =\", compute_accuracy(net_no_reg, Xval, Yval), \"%\")" @@ -265,8 +264,7 @@ "net_l2.put('max_num_epochs', 600)\n", "net_l2.put('seed', 10)\n", "\n", - "net_l2.put('labels', Ytrain)\n", - "net_l2.train(Xtrain) # this might take a while, depending on your machine\n", + "net_l2.train(Xtrain, Ytrain) # this might take a while, depending on your machine\n", "\n", "# compute accuracy on the validation set\n", "print(\"With L2 regularization, accuracy on the validation set =\", compute_accuracy(net_l2, Xval, Yval), \"%\")" @@ -294,8 +292,7 @@ "net_l1.put('max_num_epochs', 600)\n", "net_l1.put('seed', 10)\n", "\n", - "net_l1.put('labels', Ytrain)\n", - "net_l1.train(Xtrain) # this might take a while, depending on your machine\n", + "net_l1.train(Xtrain, Ytrain) # this might take a while, depending on your machine\n", "\n", "# compute accuracy on the validation set\n", "print(\"With L1 regularization, accuracy on the validation set =\", compute_accuracy(net_l1, Xval, Yval), \"%\")" @@ -336,8 +333,7 @@ "net_dropout.put('gd_learning_rate', 0.5)\n", "net_dropout.put('gd_mini_batch_size', 100)\n", "\n", - "net_dropout.put('labels', Ytrain)\n", - "net_dropout.train(Xtrain) # this might take a while, depending on your machine\n", + "net_dropout.train(Xtrain, Ytrain) # this might take a while, depending on your machine\n", "\n", "# compute accuracy on the validation set\n", "print(\"With dropout, accuracy on the validation set =\", compute_accuracy(net_dropout, Xval, Yval), \"%\")" @@ -431,8 +427,7 @@ "net_conv.put(\"seed\", 10)\n", "\n", "# start training\n", - "net_conv.put('labels', Ytrain)\n", - "net_conv.train(Xtrain)\n", + "net_conv.train(Xtrain, Ytrain)\n", "\n", "# compute accuracy on the validation set\n", "print(\"With a convolutional network, accuracy on the validation set =\", compute_accuracy(net_conv, Xval, Yval), \"%\")" @@ -511,7 +506,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/doc/ipython-notebooks/neuralnets/rbms_dbns.ipynb b/doc/ipython-notebooks/neuralnets/rbms_dbns.ipynb index 6adb5c1b07a..36c28d0c5c5 100644 --- a/doc/ipython-notebooks/neuralnets/rbms_dbns.ipynb +++ b/doc/ipython-notebooks/neuralnets/rbms_dbns.ipynb @@ -370,8 +370,7 @@ "nn.put(\"l2_coefficient\", 0.0001)\n", "\n", "# start training\n", - "nn.put('labels', sg.create_labels(Ytrain))\n", - "nn.train(sg.create_features(Xtrain))" + "nn.train(sg.create_features(Xtrain), sg.create_labels(Ytrain))" ] }, { @@ -426,7 +425,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/examples/meta/src/binary/domainadaptationsvm.sg.in b/examples/meta/src/binary/domainadaptationsvm.sg.in index 3dd0984b04a..6ed4b58a31f 100644 --- a/examples/meta/src/binary/domainadaptationsvm.sg.in +++ b/examples/meta/src/binary/domainadaptationsvm.sg.in @@ -14,8 +14,8 @@ svm_kernel.init(feats_train, feats_train) #![create_kernel] #![create_svm_and_train] -Machine svm = create_machine("SVMLight", kernel=svm_kernel, labels=labels_train, C1=1.0, C2=1.0) -svm.train() +Machine svm = create_machine("SVMLight", kernel=svm_kernel, C1=1.0, C2=1.0) +svm.train(feats_train, labels_train) #![create_svm_and_train] #![create_kernel] @@ -24,11 +24,11 @@ svm_kernel2.init(feats_train, feats_train) #![create_kernel] #![obtain_dasvm_from_the_previous_svm] -Machine dasvm = create_machine("DomainAdaptationSVM", C1=1.0, C2=1.0, kernel=svm_kernel2, labels=labels_train, presvm=as_svm(svm), B=1.0) +Machine dasvm = create_machine("DomainAdaptationSVM", C1=1.0, C2=1.0, kernel=svm_kernel2, presvm=as_svm(svm), B=1.0) #![obtain_dasvm_from_the_previous_svm] #![train_and_apply] -dasvm.train() +dasvm.train(feats_train, labels_train) Labels labels_predict = dasvm.apply(feats_test) RealVector labels_vector = labels_predict.get_real_vector("labels") RealVector weights = svm.get_real_vector("m_alpha") diff --git a/examples/meta/src/composite/ensemble.sg.in b/examples/meta/src/composite/ensemble.sg.in index b0649d819a1..d1e3523c1f9 100644 --- a/examples/meta/src/composite/ensemble.sg.in +++ b/examples/meta/src/composite/ensemble.sg.in @@ -11,7 +11,7 @@ Labels labels_test = create_labels(f_labels_test) #![create_features] #![create machine] -Machine ensemble = create_machine("EnsembleMachine", labels = labels_train) +Machine ensemble = create_machine("EnsembleMachine") Machine submachine1 = create_machine("MulticlassOCAS") Machine submachine2 = create_machine("MulticlassLibLinear") ensemble.add("machines", submachine1) @@ -21,7 +21,7 @@ ensemble.put("combination_rule", c) #![create machine] #![train_and_apply] -ensemble.train(features_train) +ensemble.train(features_train, labels_train) Labels labels_predict = ensemble.apply_multiclass(features_test) #![train_and_apply] diff --git a/examples/meta/src/evaluation/cross_validation.sg.in b/examples/meta/src/evaluation/cross_validation.sg.in index 3dd9ead79b2..448f62f782b 100644 --- a/examples/meta/src/evaluation/cross_validation.sg.in +++ b/examples/meta/src/evaluation/cross_validation.sg.in @@ -51,7 +51,7 @@ Labels reg_labels_test = create_labels(reg_lab_test) #![create_machine_REGRESSION] real tau = 0.001 -Machine lrr = create_machine("LinearRidgeRegression", tau=tau, labels=reg_labels_train) +Machine lrr = create_machine("LinearRidgeRegression", tau=tau) #![create_instance_REGRESSION] #![create_cross_validation_REGRESSION] diff --git a/examples/meta/src/multiclass/chaid_tree.sg.in b/examples/meta/src/multiclass/chaid_tree.sg.in index cf43fc41b45..eebbfbd806a 100644 --- a/examples/meta/src/multiclass/chaid_tree.sg.in +++ b/examples/meta/src/multiclass/chaid_tree.sg.in @@ -18,11 +18,10 @@ ft[1] = 2 #![create_instance] CHAIDTree classifier(0, ft, 10) -classifier.set_labels(labels_train) #![create_instance] #![train_and_apply] -classifier.train(features_train) +classifier.train(features_train, labels_train) MulticlassLabels labels_predict = classifier.apply_multiclass(features_test) #![train_and_apply] diff --git a/examples/meta/src/multiclass/relaxed_tree.sg.in b/examples/meta/src/multiclass/relaxed_tree.sg.in index 0aaf98d5b7f..29a5e0d1683 100644 --- a/examples/meta/src/multiclass/relaxed_tree.sg.in +++ b/examples/meta/src/multiclass/relaxed_tree.sg.in @@ -17,13 +17,12 @@ Kernel k = create_kernel("GaussianKernel") #![create_instance] RelaxedTree machine() -machine.set_labels(labels_train) machine.set_machine_for_confusion_matrix(mll) machine.set_kernel(k) #![create_instance] #![train_and_apply] -machine.train(features_train) +machine.train(features_train, labels_train) MulticlassLabels labels_predict = machine.apply_multiclass(features_test) #![train_and_apply] diff --git a/examples/meta/src/neural_nets/convolutional_net_classification.sg.in b/examples/meta/src/neural_nets/convolutional_net_classification.sg.in index 18b8158e5cd..0ab8bbb6355 100644 --- a/examples/meta/src/neural_nets/convolutional_net_classification.sg.in +++ b/examples/meta/src/neural_nets/convolutional_net_classification.sg.in @@ -11,7 +11,7 @@ Labels labels_test = create_labels(f_labels_test) #![create_features] #![create_instance] -Machine network = create_machine("NeuralNetwork", labels=labels_train, auto_quick_initialize=True, max_num_epochs=4, epsilon=0.0, optimization_method="NNOM_GRADIENT_DESCENT", gd_learning_rate=0.01, gd_mini_batch_size=3, max_norm=1.0, dropout_input=0.5) +Machine network = create_machine("NeuralNetwork", auto_quick_initialize=True, max_num_epochs=4, epsilon=0.0, optimization_method="NNOM_GRADIENT_DESCENT", gd_learning_rate=0.01, gd_mini_batch_size=3, max_norm=1.0, dropout_input=0.5) #![create_instance] #![add_layers] @@ -27,7 +27,7 @@ network.put("seed", 10) #![add_layers] #![train_and_apply] -network.train(features_train) +network.train(features_train, labels_train) Labels labels_predict = network.apply(features_test) #![train_and_apply] diff --git a/examples/meta/src/neural_nets/feedforward_net_classification.sg.in b/examples/meta/src/neural_nets/feedforward_net_classification.sg.in index 8d396b8b2f1..beedfbad974 100644 --- a/examples/meta/src/neural_nets/feedforward_net_classification.sg.in +++ b/examples/meta/src/neural_nets/feedforward_net_classification.sg.in @@ -12,7 +12,7 @@ Labels labels_test = create_labels(f_labels_test) #![create_instance] int num_feats = features_train.get_int("num_features") -Machine network = create_machine("NeuralNetwork", labels=labels_train, auto_quick_initialize=True, l2_coefficient=0.01, dropout_hidden=0.5, max_num_epochs=50, gd_mini_batch_size=num_feats, gd_learning_rate=0.1, gd_momentum=0.9) +Machine network = create_machine("NeuralNetwork", auto_quick_initialize=True, l2_coefficient=0.01, dropout_hidden=0.5, max_num_epochs=50, gd_mini_batch_size=num_feats, gd_learning_rate=0.1, gd_momentum=0.9) #![create_instance] #![add_layers] @@ -26,7 +26,7 @@ network.put("seed", 1) #![add_layers] #![train_and_apply] -network.train(features_train) +network.train(features_train, labels_train) Labels labels_predict = network.apply(features_test) #![train_and_apply] diff --git a/examples/meta/src/neural_nets/feedforward_net_regression.sg.in b/examples/meta/src/neural_nets/feedforward_net_regression.sg.in index 53154541fd6..213138de542 100644 --- a/examples/meta/src/neural_nets/feedforward_net_regression.sg.in +++ b/examples/meta/src/neural_nets/feedforward_net_regression.sg.in @@ -13,7 +13,7 @@ Labels labels_test = create_labels(f_labels_test) #![create_instance] int num_feats = features_train.get_int("num_features") -Machine network = create_machine("NeuralNetwork", labels=labels_train, auto_quick_initialize=True, l2_coefficient=0.1, epsilon=0.0, max_num_epochs=40, gd_learning_rate=0.1, gd_momentum=0.9) +Machine network = create_machine("NeuralNetwork", auto_quick_initialize=True, l2_coefficient=0.1, epsilon=0.0, max_num_epochs=40, gd_learning_rate=0.1, gd_momentum=0.9) #![create_instance] #![add_layers] @@ -27,7 +27,7 @@ network.put("seed", 1) #![add_layers] #![train_and_apply] -network.train(features_train) +network.train(features_train, labels_train) Labels labels_predict = network.apply(features_test) #![train_and_apply] diff --git a/examples/meta/src/regression/chaid_tree.sg.in b/examples/meta/src/regression/chaid_tree.sg.in index 90c173ecc90..5fd6c37e816 100644 --- a/examples/meta/src/regression/chaid_tree.sg.in +++ b/examples/meta/src/regression/chaid_tree.sg.in @@ -14,11 +14,11 @@ ft[0] = 2 #![set_feature_types] #![create_machine] -Machine chaidtree = create_machine("CHAIDTree", labels=labels_train, dependent_vartype=2, feature_types=ft, num_breakpoints=50) +Machine chaidtree = create_machine("CHAIDTree", dependent_vartype=2, feature_types=ft, num_breakpoints=50) #![create_machine] #![train_and_apply] -chaidtree.train(feats_train) +chaidtree.train(feats_train, labels_train) Labels labels_predict = chaidtree.apply(feats_test) #![train_and_apply] diff --git a/examples/undocumented/python/kernel_histogram_word_string.py b/examples/undocumented/python/kernel_histogram_word_string.py index bb66029f25d..902b9fc212f 100644 --- a/examples/undocumented/python/kernel_histogram_word_string.py +++ b/examples/undocumented/python/kernel_histogram_word_string.py @@ -17,8 +17,8 @@ def kernel_histogram_word_string (fm_train_dna=traindat,fm_test_dna=testdat,labe feats_test=sg.create_string_features(charfeat, order-1, order, 0, False) labels=sg.create_labels(label_train_dna) - pie=sg.create_machine("PluginEstimate", pos_pseudo=ppseudo_count, neg_pseudo=npseudo_count, labels=labels) - pie.train(feats_train) + pie=sg.create_machine("PluginEstimate", pos_pseudo=ppseudo_count, neg_pseudo=npseudo_count) + pie.train(feats_train, labels) kernel=sg.create_kernel("HistogramWordStringKernel", estimate=pie) kernel.init(feats_train, feats_train) diff --git a/examples/undocumented/python/kernel_salzberg_word_string.py b/examples/undocumented/python/kernel_salzberg_word_string.py index ff5c16037b0..60c8b5e23ba 100644 --- a/examples/undocumented/python/kernel_salzberg_word_string.py +++ b/examples/undocumented/python/kernel_salzberg_word_string.py @@ -17,8 +17,8 @@ def kernel_salzberg_word_string (fm_train_dna=traindat,fm_test_dna=testdat,label feats_test=sg.create_string_features(charfeat, order-1, order, gap, reverse) labels=sg.create_labels(label_train_dna) - pie=sg.create_machine("PluginEstimate", labels=labels) - pie.train(feats_train) + pie=sg.create_machine("PluginEstimate") + pie.train(feats_train, labels) kernel=sg.create_kernel("SalzbergWordStringKernel", plugin_estimate=pie, labels=labels) kernel.init(feats_train, feats_train) diff --git a/examples/undocumented/python/multiclass_c45classifiertree.py b/examples/undocumented/python/multiclass_c45classifiertree.py index d1f2d860a99..07c89904ec1 100644 --- a/examples/undocumented/python/multiclass_c45classifiertree.py +++ b/examples/undocumented/python/multiclass_c45classifiertree.py @@ -34,9 +34,8 @@ def multiclass_c45classifiertree(train=traindat,test=testdat,labels=label_traind feats_train.add_subset(trsubset) c=C45ClassifierTree() - c.set_labels(train_labels) c.set_feature_types(ft) - c.train(feats_train) + c.train(feats_train, train_labels) train_labels.remove_subset() feats_train.remove_subset() diff --git a/examples/undocumented/python/multiclass_id3classifiertree.py b/examples/undocumented/python/multiclass_id3classifiertree.py index 6b1effe229b..b0ce96ca763 100644 --- a/examples/undocumented/python/multiclass_id3classifiertree.py +++ b/examples/undocumented/python/multiclass_id3classifiertree.py @@ -30,8 +30,7 @@ def multiclass_id3classifiertree(train=train_data,labels=train_labels,test=test_ # ID3 Tree formation id3=ID3ClassifierTree() - id3.set_labels(feats_labels) - id3.train(feats_train) + id3.train(feats_train, feats_labels) # Classify test data output=id3.apply_multiclass(feats_test).get_labels() diff --git a/examples/undocumented/python/stochasticgbmachine.py b/examples/undocumented/python/stochasticgbmachine.py index 04b2609b1fd..993d4274e12 100644 --- a/examples/undocumented/python/stochasticgbmachine.py +++ b/examples/undocumented/python/stochasticgbmachine.py @@ -28,8 +28,7 @@ def stochasticgbmachine(train=traindat,train_labels=label_traindat,ft=feat_types # train feats.add_subset(np.int32(p[0:int(num)])) labels.add_subset(np.int32(p[0:int(num)])) - s.set_labels(labels) - s.train(feats) + s.train(feats, labels) feats.remove_subset() labels.remove_subset() diff --git a/examples/undocumented/python/structure_discrete_hmsvm_bmrm.py b/examples/undocumented/python/structure_discrete_hmsvm_bmrm.py index 7907fb9f3bf..28a644d1bf5 100644 --- a/examples/undocumented/python/structure_discrete_hmsvm_bmrm.py +++ b/examples/undocumented/python/structure_discrete_hmsvm_bmrm.py @@ -29,8 +29,8 @@ def structure_discrete_hmsvm_bmrm (m_data_dict=data_dict): model = sg.create_structured_model("HMSVMModel", features=features, labels=labels, state_model_type="SMT_TWO_STATE", num_obs=num_obs) - sosvm = sg.create_machine("DualLibQPBMSOSVM", model=model, labels=labels, m_lambda=5000.0) - sosvm.train() + sosvm = sg.create_machine("DualLibQPBMSOSVM", model=model, m_lambda=5000.0) + sosvm.train(features, labels) #print sosvm.get_w() predicted = sosvm.apply(features) diff --git a/examples/undocumented/python/structure_factor_graph_model.py b/examples/undocumented/python/structure_factor_graph_model.py index e666ffdb85a..56c32f415f5 100644 --- a/examples/undocumented/python/structure_factor_graph_model.py +++ b/examples/undocumented/python/structure_factor_graph_model.py @@ -112,9 +112,9 @@ def structure_factor_graph_model(tr_samples = samples, tr_labels = labels, w = w model.add("factor_types", ftype[2]) # --- training with BMRM --- - bmrm = sg.create_machine("DualLibQPBMSOSVM", model=model, labels=tr_labels, m_lambda=0.01) + bmrm = sg.create_machine("DualLibQPBMSOSVM", model=model, m_lambda=0.01) #bmrm.set_verbose(True) - bmrm.train() + bmrm.train(tr_samples, tr_labels) #print 'learned weights:' #print bmrm.get_w() #print 'ground truth weights:' @@ -142,9 +142,9 @@ def structure_factor_graph_model(tr_samples = samples, tr_labels = labels, w = w #print hbm.get_train_errors() # --- training with SGD --- - sgd = sg.create_machine("StochasticSOSVM", model=model, labels=tr_labels, m_lambda=0.01) + sgd = sg.create_machine("StochasticSOSVM", model=model, m_lambda=0.01) #sgd.set_verbose(True) - sgd.train() + sgd.train(tr_samples, tr_labels) # evaluation #print('SGD: Average training error is %.4f' % SOSVMHelper.average_loss(sgd.get_w(), model)) @@ -154,9 +154,9 @@ def structure_factor_graph_model(tr_samples = samples, tr_labels = labels, w = w #print hp.get_train_errors() # --- training with FW --- - fw = sg.create_machine("FWSOSVM", model=model, labels=tr_labels, m_lambda=0.01, + fw = sg.create_machine("FWSOSVM", model=model, m_lambda=0.01, gap_threshold=0.01) - fw.train() + fw.train(tr_samples, tr_labels) # evaluation #print('FW: Average training error is %.4f' % SOSVMHelper.average_loss(fw.get_w(), model)) diff --git a/examples/undocumented/python/structure_graphcuts.py b/examples/undocumented/python/structure_graphcuts.py index 2da38de1a1b..fd6adec03e9 100644 --- a/examples/undocumented/python/structure_graphcuts.py +++ b/examples/undocumented/python/structure_graphcuts.py @@ -180,12 +180,12 @@ def graphcuts_sosvm(num_train_samples = 10, len_label = 5, len_feat = 20, num_te # the 3rd parameter is do_weighted_averaging, by turning this on, # a possibly faster convergence rate may be achieved. # the 4th parameter controls outputs of verbose training information - sgd = sg.create_machine("StochasticSOSVM", model=model, labels=labels_fg, do_weighted_averaging=True, + sgd = sg.create_machine("StochasticSOSVM", model=model, do_weighted_averaging=True, num_iter=150, m_lambda=0.0001) # train t0 = time.time() - sgd.train() + sgd.train(feats_fg, labels_fg) t1 = time.time() w_sgd = sgd.get("w") #print "SGD took", t1 - t0, "seconds." diff --git a/examples/undocumented/python/structure_hierarchical_multilabel_classification.py b/examples/undocumented/python/structure_hierarchical_multilabel_classification.py index a675a6b6ded..3cb94dc377b 100644 --- a/examples/undocumented/python/structure_hierarchical_multilabel_classification.py +++ b/examples/undocumented/python/structure_hierarchical_multilabel_classification.py @@ -110,7 +110,7 @@ def structure_hierarchical_multilabel_classification(train_file_name, features=train_features, labels=train_labels, taxonomy=train_taxonomy) - sgd = sg.create_machine("StochasticSOSVM", model=model, labels=train_labels) + sgd = sg.create_machine("StochasticSOSVM", model=model) # t1 = time.time() # sgd.train() # print('>>> Took %f time for training' % (time.time() - t1)) diff --git a/src/gpl b/src/gpl index 0b5edd5979d..e2c1db008aa 160000 --- a/src/gpl +++ b/src/gpl @@ -1 +1 @@ -Subproject commit 0b5edd5979d24b79e067756f2eb940ef5e403161 +Subproject commit e2c1db008aa05266f97e7f5f4e1ceb38003b6d13 diff --git a/src/shogun/classifier/PluginEstimate.cpp b/src/shogun/classifier/PluginEstimate.cpp index 0ad4266fbde..22112217dfd 100644 --- a/src/shogun/classifier/PluginEstimate.cpp +++ b/src/shogun/classifier/PluginEstimate.cpp @@ -39,10 +39,8 @@ PluginEstimate::~PluginEstimate() { } -bool PluginEstimate::train_machine(std::shared_ptr data) +bool PluginEstimate::train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) { - ASSERT(m_labels) - ASSERT(m_labels->get_label_type() == LT_BINARY) if (data) { if (data->get_feature_class() != C_STRING || @@ -55,21 +53,19 @@ bool PluginEstimate::train_machine(std::shared_ptr data) } ASSERT(features) - - pos_model=std::make_shared(features); neg_model=std::make_shared(features); int32_t* pos_indizes=SG_MALLOC(int32_t, std::static_pointer_cast>(features)->get_num_vectors()); int32_t* neg_indizes=SG_MALLOC(int32_t, std::static_pointer_cast>(features)->get_num_vectors()); - ASSERT(m_labels->get_num_labels() == features->get_num_vectors()) + ASSERT(labs->get_num_labels() == features->get_num_vectors()) int32_t pos_idx = 0; int32_t neg_idx = 0; - auto binary_labels = std::static_pointer_cast(m_labels); - for (int32_t i=0; iget_num_labels(); i++) + auto binary_labels = std::static_pointer_cast(labs); + for (int32_t i=0; iget_num_labels(); i++) { if (binary_labels->get_label(i) > 0) pos_indizes[pos_idx++]=i; diff --git a/src/shogun/classifier/PluginEstimate.h b/src/shogun/classifier/PluginEstimate.h index ee40dbc3240..e9dfbc6224a 100644 --- a/src/shogun/classifier/PluginEstimate.h +++ b/src/shogun/classifier/PluginEstimate.h @@ -49,7 +49,7 @@ class PluginEstimate: public Machine * @param data (test)data to be classified * @return classified labels */ - std::shared_ptr apply_binary(std::shared_ptr data=NULL) override; + std::shared_ptr apply_binary(std::shared_ptr data) override; /** set features * @@ -206,7 +206,7 @@ class PluginEstimate: public Machine * * @return whether training was successful */ - bool train_machine(std::shared_ptr data=NULL) override; + bool train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) override; protected: /** pseudo count for positive class */ diff --git a/src/shogun/classifier/mkl/MKLMulticlass.cpp b/src/shogun/classifier/mkl/MKLMulticlass.cpp index 7e5ac207c7c..c47a0e8f93e 100644 --- a/src/shogun/classifier/mkl/MKLMulticlass.cpp +++ b/src/shogun/classifier/mkl/MKLMulticlass.cpp @@ -89,8 +89,6 @@ void MKLMulticlass::initsvm( const std::shared_ptr& labs) error("MKLMulticlass::initsvm(): the number of labels is " "nonpositive, do not know how to handle this!\n"); } - - svm->set_labels(labs); } void MKLMulticlass::initlpsolver() diff --git a/src/shogun/classifier/svm/LibLinear.cpp b/src/shogun/classifier/svm/LibLinear.cpp index f94cfc5ac90..4ba93d24b98 100644 --- a/src/shogun/classifier/svm/LibLinear.cpp +++ b/src/shogun/classifier/svm/LibLinear.cpp @@ -70,6 +70,7 @@ LibLinear::~LibLinear() bool LibLinear::train(const std::shared_ptr& data, const std::shared_ptr& labs) { + m_num_labels = labs->get_num_labels(); return train_machine(data->as(), labs); } @@ -1363,19 +1364,9 @@ void LibLinear::solve_l2r_lr_dual( void LibLinear::set_linear_term(const SGVector linear_term) { - if (!m_labels) - error("Please assign labels first!"); - - int32_t num_labels = m_labels->get_num_labels(); - - if (num_labels != linear_term.vlen) - { - error( - "Number of labels ({}) does not match number" + require(m_num_labels == linear_term.vlen, "Number of labels ({}) does not match number" " of entries ({}) in linear term \n", - num_labels, linear_term.vlen); - } - + m_num_labels, linear_term.vlen); m_linear_term = linear_term; } diff --git a/src/shogun/classifier/svm/LibLinear.h b/src/shogun/classifier/svm/LibLinear.h index fd4fd89e8bf..ecee5992a8a 100644 --- a/src/shogun/classifier/svm/LibLinear.h +++ b/src/shogun/classifier/svm/LibLinear.h @@ -263,6 +263,8 @@ namespace shogun /** solver type */ LIBLINEAR_SOLVER_TYPE liblinear_solver_type; + + int32_t m_num_labels; }; } /* namespace shogun */ diff --git a/src/shogun/classifier/svm/WDSVMOcas.cpp b/src/shogun/classifier/svm/WDSVMOcas.cpp index c7dc47b652c..aa2ea22d8aa 100644 --- a/src/shogun/classifier/svm/WDSVMOcas.cpp +++ b/src/shogun/classifier/svm/WDSVMOcas.cpp @@ -76,8 +76,7 @@ WDSVMOcas::WDSVMOcas(E_SVM_TYPE type) } WDSVMOcas::WDSVMOcas( - float64_t C, int32_t d, int32_t from_d, std::shared_ptr> traindat, - std::shared_ptr trainlab) + float64_t C, int32_t d, int32_t from_d, std::shared_ptr> traindat) : Machine(), use_bias(false), bufsize(3000), C1(C), C2(C), epsilon(1e-3), degree(d), from_degree(from_d) { @@ -85,7 +84,6 @@ WDSVMOcas::WDSVMOcas( old_w=NULL; method=SVM_OCAS; features=std::move(traindat); - set_labels(std::move(trainlab)); wd_weights=NULL; w_offsets=NULL; normalization_const=1.0; @@ -158,29 +156,24 @@ int32_t WDSVMOcas::set_wd_weights() return w_dim_single_c; } -bool WDSVMOcas::train_machine(std::shared_ptr data) +bool WDSVMOcas::train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) { io::info("C={}, epsilon={}, bufsize={}", get_C1(), get_epsilon(), bufsize); - ASSERT(m_labels) - ASSERT(m_labels->get_label_type() == LT_BINARY) - if (data) - { - if (data->get_feature_class() != C_STRING || + if (data->get_feature_class() != C_STRING || data->get_feature_type() != F_BYTE) - { - error("Features not of class string type byte"); - } - set_features(std::static_pointer_cast>(data)); + { + error("Features not of class string type byte"); } ASSERT(get_features()) - auto alphabet=get_features()->get_alphabet(); + features = data->as>(); + auto alphabet=features->get_alphabet(); ASSERT(alphabet && alphabet->get_alphabet()==RAWDNA) alphabet_size=alphabet->get_num_symbols(); string_length=features->get_num_vectors(); - SGVector labvec=(std::static_pointer_cast(m_labels))->get_labels(); + SGVector labvec=(std::static_pointer_cast(labs))->get_labels(); lab=labvec.vector; w_dim_single_char=set_wd_weights(); @@ -188,7 +181,7 @@ bool WDSVMOcas::train_machine(std::shared_ptr data) SG_DEBUG("w_dim_single_char={}", w_dim_single_char) w_dim=string_length*w_dim_single_char; SG_DEBUG("cutting plane has {} dims", w_dim) - num_vec=get_features()->get_max_vector_length(); + num_vec=features->get_max_vector_length(); set_normalization_const(); io::info("num_vec: {} num_lab: {}", num_vec, labvec.vlen); diff --git a/src/shogun/classifier/svm/WDSVMOcas.h b/src/shogun/classifier/svm/WDSVMOcas.h index 8220b279116..dd798f613cd 100644 --- a/src/shogun/classifier/svm/WDSVMOcas.h +++ b/src/shogun/classifier/svm/WDSVMOcas.h @@ -46,7 +46,7 @@ class WDSVMOcas : public Machine */ WDSVMOcas( float64_t C, int32_t d, int32_t from_d, - std::shared_ptr> traindat, std::shared_ptr trainlab); + std::shared_ptr> traindat); ~WDSVMOcas() override; /** get classifier type @@ -311,7 +311,7 @@ class WDSVMOcas : public Machine * * @return whether training was successful */ - bool train_machine(std::shared_ptr data=NULL) override; + bool train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) override; protected: /** features */ diff --git a/src/shogun/evaluation/CrossValidation.cpp b/src/shogun/evaluation/CrossValidation.cpp index 839d859ef85..af5e83deee7 100644 --- a/src/shogun/evaluation/CrossValidation.cpp +++ b/src/shogun/evaluation/CrossValidation.cpp @@ -116,15 +116,7 @@ float64_t CrossValidation::evaluate_one_run(int64_t index) const auto evaluation_criterion = make_clone(m_evaluation_criterion); - try - { - machine->train(features_train, labels_train); - } - catch(const std::exception& e){ - machine->set_labels(labels_train); - machine->train(features_train); - } - + machine->train(features_train, labels_train); auto result_labels = machine->apply(features_test); diff --git a/src/shogun/machine/BaggingMachine.cpp b/src/shogun/machine/BaggingMachine.cpp index cbfd444f313..a41289edfa9 100644 --- a/src/shogun/machine/BaggingMachine.cpp +++ b/src/shogun/machine/BaggingMachine.cpp @@ -182,7 +182,7 @@ bool BaggingMachine::train_machine(const std::shared_ptr& data, const set_machine_parameters(c, idx); c->train(features, labels); - #pragma omp critical +#pragma omp critical { features->remove_subset(); labels->remove_subset(); diff --git a/src/shogun/machine/Composite.h b/src/shogun/machine/Composite.h index 4b7471589ea..000b1b619c9 100644 --- a/src/shogun/machine/Composite.h +++ b/src/shogun/machine/Composite.h @@ -56,7 +56,7 @@ namespace shogun m_stages = std::forward(stages); } - std::shared_ptr train( + bool train( const std::shared_ptr& data, const std::shared_ptr& labs) { @@ -78,7 +78,7 @@ namespace shogun }, v.second); } m_ensemble_machine->train(current_data, labs); - return m_ensemble_machine; + return true; } std::shared_ptr apply_multiclass(std::shared_ptr data) override diff --git a/src/shogun/machine/EnsembleMachine.h b/src/shogun/machine/EnsembleMachine.h index b21e6227828..bf0880f46dc 100644 --- a/src/shogun/machine/EnsembleMachine.h +++ b/src/shogun/machine/EnsembleMachine.h @@ -61,14 +61,14 @@ namespace shogun m_machines.push_back(machine); } - bool train_machine(std::shared_ptr data) override{ - require(m_labels, "Labels not set"); - train(data, m_labels); - return true; + bool train_machine( + const std::shared_ptr& data, const std::shared_ptr& labs) override { + return train(data, labs); } - void train( + + bool train( const std::shared_ptr& data, - const std::shared_ptr& labs) + const std::shared_ptr& labs) override { const int32_t& num_threads = env()->get_num_threads(); if (num_threads > 1) @@ -86,8 +86,7 @@ namespace shogun [&](int32_t start, int32_t end) { for (auto i = start; i < end; i++) { - m_machines[i]->set_labels(labs); - m_machines[i]->train(data); + m_machines[i]->train(data, labs); } }, t, t + machine_per_thread); @@ -98,18 +97,17 @@ namespace shogun } for (int i = machine_per_thread * num_threads; i < num_machine; i++) { - m_machines[i]->set_labels(labs); - m_machines[i]->train(data); + m_machines[i]->train(data, labs); } } else { for (auto&& machine : m_machines) { - machine->set_labels(labs); - machine->train(data); + machine->train(data, labs); } } + return true; } const char* get_name() const override diff --git a/src/shogun/machine/GLM.cpp b/src/shogun/machine/GLM.cpp index 9f0d5a09799..534649a3b99 100644 --- a/src/shogun/machine/GLM.cpp +++ b/src/shogun/machine/GLM.cpp @@ -71,15 +71,14 @@ GLM::GLM( std::shared_ptr GLM::apply_regression(std::shared_ptr data) { + std::shared_ptr features; if (data) { if (!data->has_property(FP_DOT)) error("Specified features are not of type CDotFeatures"); - set_features(std::static_pointer_cast(data)); + features = std::static_pointer_cast(data); } - require(features, "Features are not provided"); - auto num = features->get_num_vectors(); ASSERT(num > 0) ASSERT(m_w.vlen == features->get_dim_feature_space()) @@ -92,19 +91,10 @@ GLM::apply_regression(std::shared_ptr data) return std::make_shared(result); } -void GLM::init_model(const std::shared_ptr data) +void GLM::init_model(const std::shared_ptr& data) { - ASSERT(m_labels) - if (data) - { - if (!data->has_property(FP_DOT)) - error("Specified features are not of type CDotFeatures"); - set_features(std::static_pointer_cast(data)); - } - ASSERT(features) - NormalDistribution normal_dist; - const auto& n_features = features->get_dim_feature_space(); + const auto& n_features = data->get_dim_feature_space(); if (m_w.vlen == 0) { @@ -123,12 +113,13 @@ void GLM::init_model(const std::shared_ptr data) } } -void GLM::iteration() +void GLM::iteration(const std::shared_ptr& features, + const std::shared_ptr& labs) { SGVector w_old = m_w.clone(); - auto X = get_features()->get_computed_dot_feature_matrix(); - auto y = regression_labels(get_labels())->get_labels(); + auto X = features->get_computed_dot_feature_matrix(); + auto y = regression_labels(labs)->get_labels(); auto gradient_w = m_cost_function->get_gradient_weights( X, y, m_w, bias, m_lambda, m_alpha, m_compute_bias, m_eta, diff --git a/src/shogun/machine/GLM.h b/src/shogun/machine/GLM.h index 6b616cd11f5..948521795c2 100644 --- a/src/shogun/machine/GLM.h +++ b/src/shogun/machine/GLM.h @@ -90,9 +90,10 @@ namespace shogun } protected: - void init_model(const std::shared_ptr data) override; + void init_model(const std::shared_ptr& data) override; - void iteration() override; + void iteration(const std::shared_ptr& features, + const std::shared_ptr& labs) override; private: /** Distribution type */ diff --git a/src/shogun/machine/GaussianProcess.h b/src/shogun/machine/GaussianProcess.h index da175de103e..032cb859e3f 100644 --- a/src/shogun/machine/GaussianProcess.h +++ b/src/shogun/machine/GaussianProcess.h @@ -105,7 +105,7 @@ namespace shogun */ void set_labels(std::shared_ptr lab) override { - Machine::set_labels(lab); + NonParametricMachine::set_labels(lab); m_method->set_labels(lab); } diff --git a/src/shogun/machine/Machine.cpp b/src/shogun/machine/Machine.cpp index cc07f2e294f..a9af08d68ca 100644 --- a/src/shogun/machine/Machine.cpp +++ b/src/shogun/machine/Machine.cpp @@ -13,11 +13,10 @@ using namespace shogun; Machine::Machine() - : StoppableSGObject(), m_max_train_time(0), m_labels(NULL), + : StoppableSGObject(), m_max_train_time(0), m_solver_type(ST_AUTO) { SG_ADD(&m_max_train_time, "max_train_time", "Maximum training time."); - SG_ADD(&m_labels, "labels", "Labels to be used."); SG_ADD_OPTIONS( (machine_int_t*)&m_solver_type, "solver_type", "Type of solver.", ParameterProperties::NONE, @@ -33,26 +32,11 @@ Machine::~Machine() bool Machine::train(std::shared_ptr data) { - if (train_require_labels()) - { - if (m_labels == NULL) - error("{}@{}: No labels given", get_name(), fmt::ptr(this)); - - m_labels->ensure_valid(get_name()); - } - auto sub = connect_to_signal_handler(); bool result = false; if (support_feature_dispatching()) { - require(data != NULL, "Features not provided!"); - require( - data->get_num_vectors() == m_labels->get_num_labels(), - "Number of training vectors ({}) does not match number of " - "labels ({})", - data->get_num_vectors(), m_labels->get_num_labels()); - if (support_dense_dispatching() && data->get_feature_class() == C_DENSE) result = train_dense(data); else if ( @@ -74,10 +58,14 @@ bool Machine::train(std::shared_ptr data) bool Machine::train( const std::shared_ptr& data, const std::shared_ptr& labs) { - require(data->get_num_vectors() == labs->get_num_labels(), + if(data) + { + require(data->get_num_vectors() == labs->get_num_labels(), "Number of training vectors ({}) does not match number of " "labels ({})", data->get_num_vectors(), labs->get_num_labels()); + } + auto sub = connect_to_signal_handler(); bool result = false; @@ -101,21 +89,6 @@ bool Machine::train( return result; } -void Machine::set_labels(std::shared_ptr lab) -{ - if (lab != NULL) - { - if (!is_label_valid(lab)) - error("Invalid label for {}", get_name()); - - m_labels = lab; - } -} - -std::shared_ptr Machine::get_labels() -{ - return m_labels; -} void Machine::set_max_train_time(float64_t t) { diff --git a/src/shogun/machine/Machine.h b/src/shogun/machine/Machine.h index 682baaeabd7..3b4f615c2f0 100644 --- a/src/shogun/machine/Machine.h +++ b/src/shogun/machine/Machine.h @@ -184,18 +184,6 @@ class Machine : public StoppableSGObject /** apply machine to data in means of latent problem */ virtual std::shared_ptr apply_latent(std::shared_ptr data=NULL); - /** set labels - * - * @param lab labels - */ - virtual void set_labels(std::shared_ptr lab); - - /** get labels - * - * @return labels - */ - virtual std::shared_ptr get_labels(); - /** set maximum training time * * @param t maximimum training time @@ -346,7 +334,7 @@ class Machine : public StoppableSGObject float64_t m_max_train_time; /** labels */ - std::shared_ptr m_labels; + //std::shared_ptr m_labels; /** solver type */ ESolverType m_solver_type; diff --git a/src/shogun/machine/MulticlassMachine.cpp b/src/shogun/machine/MulticlassMachine.cpp index a54088af470..0e4bbcf3692 100644 --- a/src/shogun/machine/MulticlassMachine.cpp +++ b/src/shogun/machine/MulticlassMachine.cpp @@ -215,8 +215,6 @@ bool MulticlassMachine::train_machine(const std::shared_ptr& data, con m_machines.clear(); auto train_labels = std::make_shared(get_num_rhs_vectors()); - m_machine->set_labels(train_labels); - m_multiclass_strategy->train_start( multiclass_labels(labs), train_labels); while (m_multiclass_strategy->train_has_more()) @@ -228,7 +226,7 @@ bool MulticlassMachine::train_machine(const std::shared_ptr& data, con add_machine_subset(subset); } - m_machine->train(); + m_machine->train(data, train_labels); m_machines.push_back(get_machine_from_trained(m_machine)); if (subset.vlen) diff --git a/src/shogun/machine/NonParametricMachine.h b/src/shogun/machine/NonParametricMachine.h index 09351d1f9d0..2d78be4da2d 100644 --- a/src/shogun/machine/NonParametricMachine.h +++ b/src/shogun/machine/NonParametricMachine.h @@ -19,9 +19,8 @@ namespace shogun { // TODO : when all refactor is done, m_labels should be removed from // Machine Class - // SG_ADD( - // &m_labels, "labels", "labels used in train machine - // algorithm", ParameterProperties::READONLY); + SG_ADD( + &m_labels, "labels", "labels used in train machine algorithm"); SG_ADD( &m_features, "features_train", "Training features of nonparametric model", @@ -37,19 +36,35 @@ namespace shogun const std::shared_ptr& lab) override { m_labels = lab; + require( + data->get_num_vectors() == m_labels->get_num_labels(), + "Number of training vectors ({}) does not match number of " + "labels ({})", + data->get_num_vectors(), m_labels->get_num_labels()); return Machine::train(data); } + const char* get_name() const override { return "NonParametricMachine"; } + + virtual void set_labels(std::shared_ptr lab) + { + m_labels = lab; + } + /** get labels + * + * @return labels + */ + virtual std::shared_ptr get_labels() + { + return m_labels; + } protected: std::shared_ptr m_features; - - // TODO - // when all refactor is done, we should use this m_labels - // std::shared_ptr m_labels; + std::shared_ptr m_labels; }; } // namespace shogun #endif \ No newline at end of file diff --git a/src/shogun/machine/Pipeline.cpp b/src/shogun/machine/Pipeline.cpp index f43737b5b1c..8ae0d31c5a8 100644 --- a/src/shogun/machine/Pipeline.cpp +++ b/src/shogun/machine/Pipeline.cpp @@ -122,39 +122,12 @@ namespace shogun bool Pipeline::train_machine(std::shared_ptr data) { - if (train_require_labels()) - { - require(m_labels, "No labels given."); - } - auto current_data = data; - for (auto&& stage : m_stages) - { - if (holds_alternative>(stage.second)) - { - auto transformer = shogun::get>(stage.second); - transformer->train_require_labels() - ? transformer->fit(current_data, m_labels) - : transformer->fit(current_data); - - current_data = transformer->transform(current_data); - } - else - { - auto machine = shogun::get>(stage.second); - try - { - if (machine->train_require_labels()) - machine->set_labels(m_labels); - machine->train(current_data); - } - catch(const std::exception& e) - { - machine->train(current_data, m_labels); - } - - } - } - return true; + return train_machine_impl(data); + } + bool Pipeline::train_machine(const std::shared_ptr& data, + const std::shared_ptr& labs) + { + return train_machine_impl(data, labs); } std::shared_ptr Pipeline::apply(std::shared_ptr data) diff --git a/src/shogun/machine/Pipeline.h b/src/shogun/machine/Pipeline.h index 69944bb1504..ca814e6db37 100644 --- a/src/shogun/machine/Pipeline.h +++ b/src/shogun/machine/Pipeline.h @@ -123,8 +123,32 @@ namespace shogun EProblemType get_machine_problem_type() const override; protected: - bool train_machine(std::shared_ptr data = NULL) override; - + template + bool train_machine_impl(std::shared_ptr data, Args&& ... args) + { + require(data, "Data should not be NULL"); + auto current_data = data; + for (auto&& stage : m_stages) + { + if (holds_alternative>(stage.second)) + { + auto transformer = shogun::get>(stage.second); + transformer->train_require_labels() + ? transformer->fit(current_data, args...) + : transformer->fit(current_data); + current_data = transformer->transform(current_data); + } + else + { + auto machine = shogun::get>(stage.second); + machine->train(current_data, args...); + } + } + return true; + } + bool train_machine(std::shared_ptr data) override; + bool train_machine(const std::shared_ptr& data, + const std::shared_ptr& labs) override; std::vector, std::shared_ptr>>> m_stages; bool train_require_labels() const override; diff --git a/src/shogun/machine/StochasticGBMachine.cpp b/src/shogun/machine/StochasticGBMachine.cpp index a8be1f9e0a6..7cbb8991418 100644 --- a/src/shogun/machine/StochasticGBMachine.cpp +++ b/src/shogun/machine/StochasticGBMachine.cpp @@ -161,12 +161,11 @@ std::shared_ptr StochasticGBMachine::apply_regression(std::sha return std::make_shared(retlabs); } -bool StochasticGBMachine::train_machine(std::shared_ptr data) +bool StochasticGBMachine::train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) { require(data,"training data not supplied!"); require(m_machine,"machine not set!"); require(m_loss,"loss function not specified"); - require(m_labels, "labels not specified"); auto feats=data->as>(); @@ -181,7 +180,7 @@ bool StochasticGBMachine::train_machine(std::shared_ptr data) for (auto i : SG_PROGRESS(range(m_num_iter))) { - const auto result = get_subset(feats, interf); + const auto result = get_subset(feats, interf, labs); const auto& feats_iter = std::get<0>(result); const auto& interf_iter = std::get<1>(result); const auto& labels_iter = std::get<2>(result); @@ -258,10 +257,10 @@ std::shared_ptr StochasticGBMachine::compute_pseudo_residuals( std::tuple>, std::shared_ptr, std::shared_ptr> StochasticGBMachine::get_subset( - std::shared_ptr> f, std::shared_ptr interf) + std::shared_ptr> f, std::shared_ptr interf, std::shared_ptr labs) { if (m_subset_frac == 1.0) - return std::make_tuple(f, interf, m_labels); + return std::make_tuple(f, interf, labs); int32_t subset_size=m_subset_frac*(f->get_num_vectors()); SGVector idx(f->get_num_vectors()); @@ -273,7 +272,7 @@ StochasticGBMachine::get_subset( return std::make_tuple( view(f, subset), view(interf, subset), - view(m_labels, subset)); + view(labs, subset)); } void StochasticGBMachine::initialize_learners() diff --git a/src/shogun/machine/StochasticGBMachine.h b/src/shogun/machine/StochasticGBMachine.h index 75086b08526..c8887972495 100644 --- a/src/shogun/machine/StochasticGBMachine.h +++ b/src/shogun/machine/StochasticGBMachine.h @@ -148,7 +148,7 @@ class StochasticGBMachine : public RandomMixin * @param data training data * @return true */ - bool train_machine(std::shared_ptr data=NULL) override; + bool train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) override; /** compute gamma values * @@ -185,7 +185,8 @@ class StochasticGBMachine : public RandomMixin */ std::tuple>, std::shared_ptr, std::shared_ptr> - get_subset(std::shared_ptr> f, std::shared_ptr interf); + get_subset(std::shared_ptr> f, std::shared_ptr interf, + std::shared_ptr labs); /** reset arrays of weak learners and gamma values */ void initialize_learners(); diff --git a/src/shogun/machine/StructuredOutputMachine.cpp b/src/shogun/machine/StructuredOutputMachine.cpp index b3d42f8396a..f4200a9c0f7 100644 --- a/src/shogun/machine/StructuredOutputMachine.cpp +++ b/src/shogun/machine/StructuredOutputMachine.cpp @@ -27,7 +27,6 @@ StructuredOutputMachine::StructuredOutputMachine( const std::shared_ptr& labs) : Machine(), m_model(std::move(model)), m_surrogate_loss(NULL) { - set_labels(labs); register_parameters(); } @@ -56,13 +55,6 @@ void StructuredOutputMachine::register_parameters() m_helper = NULL; } -void StructuredOutputMachine::set_labels(std::shared_ptr lab) -{ - Machine::set_labels(lab); - require(m_model != NULL, "please call set_model() before set_labels()"); - m_model->set_labels(lab->as()); -} - void StructuredOutputMachine::set_features(std::shared_ptr f) { m_model->set_features(std::move(f)); diff --git a/src/shogun/machine/StructuredOutputMachine.h b/src/shogun/machine/StructuredOutputMachine.h index d0ce6d4f716..3ac0e1a73ad 100644 --- a/src/shogun/machine/StructuredOutputMachine.h +++ b/src/shogun/machine/StructuredOutputMachine.h @@ -77,12 +77,6 @@ class StructuredOutputMachine : public Machine return "StructuredOutputMachine"; } - /** set labels - * - * @param lab labels - */ - void set_labels(std::shared_ptr lab) override; - /** set features * * @param f features diff --git a/src/shogun/multiclass/GaussianNaiveBayes.cpp b/src/shogun/multiclass/GaussianNaiveBayes.cpp index 94ba8303c42..8090de32793 100644 --- a/src/shogun/multiclass/GaussianNaiveBayes.cpp +++ b/src/shogun/multiclass/GaussianNaiveBayes.cpp @@ -25,15 +25,12 @@ GaussianNaiveBayes::GaussianNaiveBayes() : NativeMulticlassMachine(), m_features init(); }; -GaussianNaiveBayes::GaussianNaiveBayes(const std::shared_ptr& train_examples, - const std::shared_ptr& train_labels) : NativeMulticlassMachine(), m_features(NULL), +GaussianNaiveBayes::GaussianNaiveBayes(const std::shared_ptr& train_examples) + : NativeMulticlassMachine(), m_features(NULL), m_min_label(0), m_num_classes(0), m_dim(0), m_means(), m_variances(), m_label_prob(), m_rates() { init(); - ASSERT(train_examples->get_num_vectors() == train_labels->get_num_labels()) - set_labels(train_labels); - if (!train_examples->has_property(FP_DOT)) error("Specified features are not of type CDotFeatures"); diff --git a/src/shogun/multiclass/GaussianNaiveBayes.h b/src/shogun/multiclass/GaussianNaiveBayes.h index fb3280942e2..5f4438b87f1 100644 --- a/src/shogun/multiclass/GaussianNaiveBayes.h +++ b/src/shogun/multiclass/GaussianNaiveBayes.h @@ -46,7 +46,7 @@ class GaussianNaiveBayes : public NativeMulticlassMachine * @param train_examples train examples * @param train_labels labels corresponding to train_examples */ - GaussianNaiveBayes(const std::shared_ptr& train_examples, const std::shared_ptr& train_labels); + GaussianNaiveBayes(const std::shared_ptr& train_examples); /** destructor * diff --git a/src/shogun/multiclass/tree/C45ClassifierTree.cpp b/src/shogun/multiclass/tree/C45ClassifierTree.cpp index 146ea836b7a..ce285a7cef3 100644 --- a/src/shogun/multiclass/tree/C45ClassifierTree.cpp +++ b/src/shogun/multiclass/tree/C45ClassifierTree.cpp @@ -105,7 +105,8 @@ void C45ClassifierTree::clear_feature_types() m_types_set=false; } -bool C45ClassifierTree::train_machine(std::shared_ptr data) +bool C45ClassifierTree::train_machine(const std::shared_ptr& data, + const std::shared_ptr& labs) { require(data,"Data required for training"); require(data->get_feature_class()==C_DENSE,"Dense data required for training"); @@ -140,7 +141,7 @@ bool C45ClassifierTree::train_machine(std::shared_ptr data) SGVector feature_ids(num_features); feature_ids.range_fill(); - set_root(C45train(data, m_weights, multiclass_labels(m_labels), feature_ids, 0)); + set_root(C45train(data, m_weights, multiclass_labels(labs), feature_ids, 0)); if (m_root) { compute_feature_importance(num_features, m_root); diff --git a/src/shogun/multiclass/tree/C45ClassifierTree.h b/src/shogun/multiclass/tree/C45ClassifierTree.h index 22353ff5ddd..c3e32d0341d 100644 --- a/src/shogun/multiclass/tree/C45ClassifierTree.h +++ b/src/shogun/multiclass/tree/C45ClassifierTree.h @@ -157,7 +157,7 @@ class C45ClassifierTree : public FeatureImportanceTree /** train machine - build C4.5 Tree from training data * @param data training data */ - bool train_machine(std::shared_ptr data=NULL) override; + bool train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) override; private: diff --git a/src/shogun/multiclass/tree/CHAIDTree.cpp b/src/shogun/multiclass/tree/CHAIDTree.cpp index b3973647b6c..7e041b4a09f 100644 --- a/src/shogun/multiclass/tree/CHAIDTree.cpp +++ b/src/shogun/multiclass/tree/CHAIDTree.cpp @@ -152,7 +152,7 @@ void CHAIDTree::set_dependent_vartype(int32_t var) m_dependent_vartype=var; } -bool CHAIDTree::train_machine(std::shared_ptr data) +bool CHAIDTree::train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) { require(data, "Data required for training"); @@ -188,7 +188,7 @@ bool CHAIDTree::train_machine(std::shared_ptr data) } } - set_root(CHAIDtrain(data,m_weights,m_labels,0)); + set_root(CHAIDtrain(data,m_weights,labs,0)); // restore feature types if (updated) diff --git a/src/shogun/multiclass/tree/CHAIDTree.h b/src/shogun/multiclass/tree/CHAIDTree.h index 2492cc981bd..d53352ae426 100644 --- a/src/shogun/multiclass/tree/CHAIDTree.h +++ b/src/shogun/multiclass/tree/CHAIDTree.h @@ -231,7 +231,7 @@ class CHAIDTree : public TreeMachine * @param data training data * @return true */ - bool train_machine(std::shared_ptr data=NULL) override; + bool train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) override; private: /** CHAIDtrain - recursive CHAID training method diff --git a/src/shogun/multiclass/tree/ID3ClassifierTree.cpp b/src/shogun/multiclass/tree/ID3ClassifierTree.cpp index 715cf51e4e3..d5866bdf545 100644 --- a/src/shogun/multiclass/tree/ID3ClassifierTree.cpp +++ b/src/shogun/multiclass/tree/ID3ClassifierTree.cpp @@ -68,7 +68,7 @@ bool ID3ClassifierTree::prune_tree(std::shared_ptr> val return true; } -bool ID3ClassifierTree::train_machine(std::shared_ptr data) +bool ID3ClassifierTree::train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) { require(data,"Data required for training"); require(data->get_feature_class()==C_DENSE, "Dense data required for training"); @@ -77,7 +77,7 @@ bool ID3ClassifierTree::train_machine(std::shared_ptr data) SGVector feature_ids = SGVector(num_features); feature_ids.range_fill(); - set_root(id3train(data, multiclass_labels(m_labels), feature_ids, 0)); + set_root(id3train(data, multiclass_labels(labs), feature_ids, 0)); if (m_root) { diff --git a/src/shogun/multiclass/tree/ID3ClassifierTree.h b/src/shogun/multiclass/tree/ID3ClassifierTree.h index f601ba239dc..595301ebdde 100644 --- a/src/shogun/multiclass/tree/ID3ClassifierTree.h +++ b/src/shogun/multiclass/tree/ID3ClassifierTree.h @@ -120,7 +120,7 @@ class ID3ClassifierTree : public FeatureImportanceTree /** train machine - build ID3 Tree from training data * @param data training data */ - bool train_machine(std::shared_ptr data=NULL) override; + bool train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) override; private: diff --git a/src/shogun/multiclass/tree/RelaxedTree.cpp b/src/shogun/multiclass/tree/RelaxedTree.cpp index 70e3a537aa6..425e5796940 100644 --- a/src/shogun/multiclass/tree/RelaxedTree.cpp +++ b/src/shogun/multiclass/tree/RelaxedTree.cpp @@ -21,7 +21,7 @@ using namespace shogun; RelaxedTree::RelaxedTree() :m_max_num_iter(3), m_A(0.5), m_B(5), m_svm_C(1), m_svm_epsilon(0.001), - m_kernel(NULL), m_feats(NULL), m_machine_for_confusion_matrix(NULL), m_num_classes(0) + m_kernel(NULL), m_machine_for_confusion_matrix(NULL), m_num_classes(0) { SG_ADD(&m_max_num_iter, "m_max_num_iter", "max number of iterations in alternating optimization"); SG_ADD(&m_svm_C, "m_svm_C", "C for svm", ParameterProperties::HYPER); @@ -36,22 +36,17 @@ RelaxedTree::~RelaxedTree() std::shared_ptr RelaxedTree::apply_multiclass(std::shared_ptr data) { - if (data != NULL) - { - auto feats = data->as>(); - set_features(feats); - } - + auto feats = data->as>(); // init kernels for all sub-machines for (auto m: m_machines) { auto machine = m->as(); auto kernel = machine->get_kernel(); auto lhs = kernel->get_lhs(); - kernel->init(lhs, m_feats); + kernel->init(lhs, feats); } - auto lab = std::make_shared(m_feats->get_num_vectors()); + auto lab = std::make_shared(feats->get_num_vectors()); for (int32_t i=0; i < lab->get_num_labels(); ++i) { @@ -115,31 +110,23 @@ float64_t RelaxedTree::apply_one(int32_t idx) return klass; } -bool RelaxedTree::train_machine(std::shared_ptr data) +bool RelaxedTree::train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) { - if (m_machine_for_confusion_matrix == NULL) - error("Call set_machine_for_confusion_matrix before training"); - if (m_kernel == NULL) - error("assign a valid kernel before training"); - - if (data) - { - set_features(data->template as>()); - } - - auto lab = multiclass_labels(m_labels); - + require(m_machine_for_confusion_matrix, + "Call set_machine_for_confusion_matrix before training"); + auto lab = multiclass_labels(labs); + m_num_classes = lab->get_num_classes(); RelaxedTreeUtil util; SGMatrix conf_mat = util.estimate_confusion_matrix( m_machine_for_confusion_matrix->as(), - m_feats, lab, m_num_classes); + data, lab, m_num_classes); // train root SGVector classes(m_num_classes); classes.range_fill(); - m_root = train_node(conf_mat, classes); + m_root = train_node(conf_mat, classes, data, labs); std::queue> node_q; node_q.push(m_root->as()); @@ -163,7 +150,7 @@ bool RelaxedTree::train_machine(std::shared_ptr data) if (left_classes.vlen >= 2) { - auto left_node = train_node(conf_mat, left_classes); + auto left_node = train_node(conf_mat, left_classes, data, labs); node->left(left_node); node_q.push(left_node); } @@ -182,7 +169,7 @@ bool RelaxedTree::train_machine(std::shared_ptr data) if (right_classes.vlen >= 2) { - auto right_node = train_node(conf_mat, right_classes); + auto right_node = train_node(conf_mat, right_classes, data, labs); node->right(right_node); node_q.push(right_node); } @@ -193,7 +180,8 @@ bool RelaxedTree::train_machine(std::shared_ptr data) return true; } -std::shared_ptr RelaxedTree::train_node(const SGMatrix &conf_mat, SGVector classes) +std::shared_ptr RelaxedTree::train_node(const SGMatrix &conf_mat, + SGVector classes, const std::shared_ptr& data, const std::shared_ptr& labs) { SGVector best_mu; std::shared_ptr best_svm = NULL; @@ -204,7 +192,7 @@ std::shared_ptr RelaxedTree::train_node(const SGMatrix(); - SGVector mu = train_node_with_initialization(*it, classes, svm); + SGVector mu = train_node_with_initialization(*it, classes, svm, data, labs); float64_t score = compute_score(mu, svm); if (score < best_score) @@ -255,7 +243,8 @@ float64_t RelaxedTree::compute_score(SGVector mu, const std::shared_ptr return score; } -SGVector RelaxedTree::train_node_with_initialization(const RelaxedTree::entry_t &mu_entry, SGVector classes, const std::shared_ptr&svm) +SGVector RelaxedTree::train_node_with_initialization(const RelaxedTree::entry_t &mu_entry, SGVector classes, + const std::shared_ptr&svm, const std::shared_ptr& data, const std::shared_ptr& labels) { SGVector mu(classes.vlen), prev_mu(classes.vlen); mu.zero(); @@ -266,7 +255,7 @@ SGVector RelaxedTree::train_node_with_initialization(const RelaxedTree: svm->set_C(m_svm_C, m_svm_C); svm->set_epsilon(m_svm_epsilon); - auto labs = multiclass_labels(m_labels); + auto labs = multiclass_labels(labels); for (int32_t iiter=0; iiter < m_max_num_iter; ++iiter) { long_mu.zero(); @@ -278,8 +267,8 @@ SGVector RelaxedTree::train_node_with_initialization(const RelaxedTree: long_mu[classes[i]] = -1; } - SGVector subset(m_feats->get_num_vectors()); - SGVector binlab(m_feats->get_num_vectors()); + SGVector subset(data->get_num_vectors()); + SGVector binlab(data->get_num_vectors()); int32_t k=0; for (int32_t i=0; i < binlab.vlen; ++i) @@ -293,7 +282,7 @@ SGVector RelaxedTree::train_node_with_initialization(const RelaxedTree: subset.vlen = k; auto binary_labels = std::make_shared(binlab); - auto feats_train = view(m_feats, subset); + auto feats_train = view(data, subset); auto labels_train = view(binary_labels, subset); auto kernel = make_clone(m_kernel, ParameterProperties::ALL^ParameterProperties::MODEL); @@ -305,7 +294,7 @@ SGVector RelaxedTree::train_node_with_initialization(const RelaxedTree: std::copy(&mu[0], &mu[mu.vlen], &prev_mu[0]); - mu = color_label_space(svm, classes); + mu = color_label_space(svm, classes, data, labs); bool bbreak = true; for (int32_t i=0; i < mu.vlen; ++i) @@ -369,12 +358,12 @@ std::vector RelaxedTree::init_node(const SGMatrix(entries.begin(), entries.begin() + n_samples); } -SGVector RelaxedTree::color_label_space(std::shared_ptrsvm, SGVector classes) +SGVector RelaxedTree::color_label_space(std::shared_ptrsvm, SGVector classes, const std::shared_ptr& data, const std::shared_ptr& labs) { SGVector mu(classes.vlen); - auto labels = multiclass_labels(m_labels); + auto labels = multiclass_labels(labs); - SGVector resp = eval_binary_model_K(std::move(svm)); + SGVector resp = eval_binary_model_K(std::move(svm), data); ASSERT(resp.vlen == labels->get_num_labels()) SGVector xi_pos_class(classes.vlen), xi_neg_class(classes.vlen); @@ -871,9 +860,9 @@ void RelaxedTree::enforce_balance_constraints_lower(SGVector &mu, SGVec } } -SGVector RelaxedTree::eval_binary_model_K(const std::shared_ptr&svm) +SGVector RelaxedTree::eval_binary_model_K(const std::shared_ptr&svm, const std::shared_ptr& data) { - auto lab = svm->apply_regression(m_feats); + auto lab = svm->apply_regression(data); SGVector resp(lab->get_num_labels()); for (int32_t i=0; i < resp.vlen; ++i) resp[i] = lab->get_label(i) - m_A/m_svm_C; diff --git a/src/shogun/multiclass/tree/RelaxedTree.h b/src/shogun/multiclass/tree/RelaxedTree.h index 1e82fa6f920..12e26b8ae20 100644 --- a/src/shogun/multiclass/tree/RelaxedTree.h +++ b/src/shogun/multiclass/tree/RelaxedTree.h @@ -45,14 +45,6 @@ class RelaxedTree: public TreeMachine /** apply machine to data in means of multiclass classification problem */ std::shared_ptr apply_multiclass(std::shared_ptr data=NULL) override; - /** set features - * @param feats features - */ - void set_features(std::shared_ptr >feats) - { - m_feats = std::move(feats); - } - /** set kernel * @param kernel the kernel to be used */ @@ -61,19 +53,6 @@ class RelaxedTree: public TreeMachine m_kernel = std::move(kernel); } - /** set labels - * - * @param lab labels - */ - void set_labels(std::shared_ptr lab) override - { - auto mlab = multiclass_labels(lab); - require(lab, "requires MulticlassLabes"); - - Machine::set_labels(mlab); - m_num_classes = mlab->get_num_classes(); - } - /** set machine for confusion matrix * @param machine the multiclass machine for initializing the confusion matrix */ @@ -162,20 +141,6 @@ class RelaxedTree: public TreeMachine return m_max_num_iter; } - /** train machine - * - * @param data training data (parameter can be avoided if distance or - * kernel-based classifiers are used and distance/kernels are - * initialized with train data). - * If flag is set, model features will be stored after training. - * - * @return whether training was successful - */ - bool train(std::shared_ptr data=NULL) override - { - return Machine::train(data); - } - /** entry type */ typedef std::pair, float64_t> entry_t; protected: @@ -193,21 +158,24 @@ class RelaxedTree: public TreeMachine * * @return whether training was successful */ - bool train_machine(std::shared_ptr data) override; + bool train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) override; /** train node */ - std::shared_ptr train_node(const SGMatrix &conf_mat, SGVector classes); + std::shared_ptr train_node(const SGMatrix &conf_mat, SGVector classes, + const std::shared_ptr&, const std::shared_ptr&); /** init node */ std::vector init_node(const SGMatrix &global_conf_mat, SGVector classes); /** train node with initialization */ - SGVector train_node_with_initialization(const RelaxedTree::entry_t &mu_entry, SGVector classes, const std::shared_ptr&svm); + SGVector train_node_with_initialization(const RelaxedTree::entry_t &mu_entry, SGVector classes, + const std::shared_ptr&svm, const std::shared_ptr&, const std::shared_ptr&); /** compute score */ float64_t compute_score(SGVector mu, const std::shared_ptr&svm); /** color label space */ - SGVector color_label_space(std::shared_ptrsvm, SGVector classes); + SGVector color_label_space(std::shared_ptrsvm, SGVector classes, + const std::shared_ptr&, const std::shared_ptr&); /** evaluate binary model K */ - SGVector eval_binary_model_K(const std::shared_ptr&svm); + SGVector eval_binary_model_K(const std::shared_ptr&svm, const std::shared_ptr& data); /** enforce balance constraints upper */ void enforce_balance_constraints_upper(SGVector &mu, SGVector &delta_neg, SGVector &delta_pos, int32_t B_prime, SGVector& xi_neg_class); diff --git a/src/shogun/neuralnets/NeuralNetwork.cpp b/src/shogun/neuralnets/NeuralNetwork.cpp index 80438378cf5..5a9784f7dbd 100644 --- a/src/shogun/neuralnets/NeuralNetwork.cpp +++ b/src/shogun/neuralnets/NeuralNetwork.cpp @@ -231,8 +231,14 @@ std::shared_ptr> NeuralNetwork::transform( return std::make_shared>(output_activations); } -bool NeuralNetwork::train_machine(std::shared_ptr data) +bool NeuralNetwork::train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) { + if (labs->get_label_type() == LT_BINARY) + m_problem_type = PT_BINARY; + else if (labs->get_label_type() == LT_REGRESSION) + m_problem_type = PT_REGRESSION; + else + m_problem_type = PT_MULTICLASS; if (m_auto_quick_initialize) { quick_connect(); @@ -243,7 +249,7 @@ bool NeuralNetwork::train_machine(std::shared_ptr data) "Maximum number of epochs ({}) must be >= 0", m_max_num_epochs); SGMatrix inputs = features_to_matrix(data); - SGMatrix targets = labels_to_matrix(m_labels); + SGMatrix targets = labels_to_matrix(labs); for (int32_t i=0; i NeuralNetwork::labels_to_matrix(const std::shared_ptrget_label_type() == LT_BINARY) - return PT_BINARY; - else if (m_labels->get_label_type() == LT_REGRESSION) - return PT_REGRESSION; - else return PT_MULTICLASS; + return m_problem_type; } bool NeuralNetwork::is_label_valid(std::shared_ptr lab) const @@ -703,21 +700,6 @@ bool NeuralNetwork::is_label_valid(std::shared_ptr lab) const lab->get_label_type() == LT_REGRESSION); } -void NeuralNetwork::set_labels(std::shared_ptr lab) -{ - if (lab->get_label_type() == LT_BINARY) - { - require(get_num_outputs() <= 2, "Cannot use {} in a neural network " - "with more that 2 output neurons", lab->get_name()); - } - else if (lab->get_label_type() == LT_REGRESSION) - { - require(get_num_outputs() == 1, "Cannot use {} in a neural network " - "with more that 1 output neuron", lab->get_name()); - } - - Machine::set_labels(lab); -} SGVector* NeuralNetwork::get_layer_parameters(int32_t i) const { diff --git a/src/shogun/neuralnets/NeuralNetwork.h b/src/shogun/neuralnets/NeuralNetwork.h index 1a929337b5e..ab275f8c19d 100644 --- a/src/shogun/neuralnets/NeuralNetwork.h +++ b/src/shogun/neuralnets/NeuralNetwork.h @@ -180,12 +180,6 @@ friend class DeepBeliefNetwork; virtual std::shared_ptr> transform( std::shared_ptr> data); - /** set labels - * - * @param lab labels - */ - void set_labels(std::shared_ptr lab) override; - /** get classifier type * * @return classifier type CT_NEURALNETWORK @@ -469,7 +463,7 @@ friend class DeepBeliefNetwork; protected: /** trains the network */ - bool train_machine(std::shared_ptr data=NULL) override; + bool train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) override; /** trains the network using gradient descent*/ virtual bool train_gradient_descent(SGMatrix inputs, @@ -737,6 +731,8 @@ friend class DeepBeliefNetwork; */ const SGMatrix* m_lbfgs_temp_inputs; const SGMatrix* m_lbfgs_temp_targets; + + EProblemType m_problem_type; }; } diff --git a/src/shogun/structure/FWSOSVM.cpp b/src/shogun/structure/FWSOSVM.cpp index 475d31fbfbf..a3b7d09b4c7 100644 --- a/src/shogun/structure/FWSOSVM.cpp +++ b/src/shogun/structure/FWSOSVM.cpp @@ -61,7 +61,7 @@ EMachineType FWSOSVM::get_classifier_type() return CT_FWSOSVM; } -bool FWSOSVM::train_machine(std::shared_ptr data) +bool FWSOSVM::train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) { SG_TRACE("Entering CFWSOSVM::train_machine."); if (data) @@ -76,7 +76,7 @@ bool FWSOSVM::train_machine(std::shared_ptr data) // Dimensionality of the joint feature space int32_t M = m_model->get_dim(); // Number of training examples - int32_t N = m_labels->as()->get_num_labels(); + int32_t N = labs->as()->get_num_labels(); SG_DEBUG("M={}, N ={}.", M, N); diff --git a/src/shogun/structure/FWSOSVM.h b/src/shogun/structure/FWSOSVM.h index 0995aab3178..70a6d42946e 100644 --- a/src/shogun/structure/FWSOSVM.h +++ b/src/shogun/structure/FWSOSVM.h @@ -89,7 +89,7 @@ class FWSOSVM : public LinearStructuredOutputMachine * @param data training data * @return whether the training was successful */ - bool train_machine(std::shared_ptr data = NULL) override; + bool train_machine(const std::shared_ptr& data, const std::shared_ptr& labs) override; private: /** register and initialize parameters */ diff --git a/src/shogun/structure/FactorGraphDataGenerator.cpp b/src/shogun/structure/FactorGraphDataGenerator.cpp index c3ff583bb2e..f4001ef300a 100644 --- a/src/shogun/structure/FactorGraphDataGenerator.cpp +++ b/src/shogun/structure/FactorGraphDataGenerator.cpp @@ -531,7 +531,7 @@ float64_t FactorGraphDataGenerator::test_sosvm(EMAPInferType infer_type) // 2.2 Train SGD - sgd->train(); + sgd->train(fg_feats_train, fg_labels_train); // 3.1 Evaluation auto labels_sgd = sgd->apply()->as(); diff --git a/src/shogun/structure/StochasticSOSVM.cpp b/src/shogun/structure/StochasticSOSVM.cpp index b953734bb7a..81f903f4c4b 100644 --- a/src/shogun/structure/StochasticSOSVM.cpp +++ b/src/shogun/structure/StochasticSOSVM.cpp @@ -61,7 +61,8 @@ EMachineType StochasticSOSVM::get_classifier_type() return CT_STOCHASTICSOSVM; } -bool StochasticSOSVM::train_machine(std::shared_ptr data) +bool StochasticSOSVM::train_machine(const std::shared_ptr& data, + const std::shared_ptr& labs) { SG_TRACE("Entering CStochasticSOSVM::train_machine."); if (data) @@ -76,7 +77,7 @@ bool StochasticSOSVM::train_machine(std::shared_ptr data) // Dimensionality of the joint feature space int32_t M = m_model->get_dim(); // Number of training examples - int32_t N = m_labels->as()->get_num_labels(); + int32_t N = labs->as()->get_num_labels(); require(M > 0, "StochasticSOSVM underlying model has not been initialized properly." "Expected number of dimensions to be greater than 0."); diff --git a/src/shogun/structure/StochasticSOSVM.h b/src/shogun/structure/StochasticSOSVM.h index ca83b039a68..0e5e4aac2e9 100644 --- a/src/shogun/structure/StochasticSOSVM.h +++ b/src/shogun/structure/StochasticSOSVM.h @@ -88,7 +88,8 @@ class StochasticSOSVM : public RandomMixin * @param data training data * @return whether the training was successful */ - bool train_machine(std::shared_ptr data = NULL) override; + bool train_machine(const std::shared_ptr& data, + const std::shared_ptr& labs) override; private: /** register and initialize parameters */ diff --git a/src/shogun/transfer/multitask/LibLinearMTL.cpp b/src/shogun/transfer/multitask/LibLinearMTL.cpp index c9ff4ea2d12..47f415bc838 100644 --- a/src/shogun/transfer/multitask/LibLinearMTL.cpp +++ b/src/shogun/transfer/multitask/LibLinearMTL.cpp @@ -65,7 +65,7 @@ bool LibLinearMTL::train_machine(const std::shared_ptr& features, c " of entries ({}) in linear term ", num_labels, m_linear_term.vlen); labs->ensure_valid(); - int32_t num_train_labels=m_labels->get_num_labels(); + int32_t num_train_labels=labs->get_num_labels(); int32_t num_feat=features->get_dim_feature_space(); int32_t num_vec=features->get_num_vectors(); @@ -448,7 +448,7 @@ return obj } // loss - auto bl = binary_labels(m_labels); + auto bl = binary_labels(labs); for(int32_t i=0; i(mockData->get_labels_test()); auto composite = std::make_shared(); - auto pred = composite->over(std::make_shared()) - ->over(std::make_shared()) - ->then(std::make_shared()) - ->train(train_feats, train_labels) - ->apply_multiclass(test_feats); + composite->over(std::make_shared()) + ->over(std::make_shared()) + ->then(std::make_shared()) + ->train(train_feats, train_labels); + + auto pred = composite->apply_multiclass(test_feats); MulticlassAccuracy evaluate; float64_t result = evaluate.evaluate(pred, ground_truth); @@ -77,13 +78,15 @@ TEST(combinate_composite_and_pipeline, train) auto ground_truth = std::static_pointer_cast(mockData->get_labels_test()); auto pipeline = std::make_shared(); - auto pred = pipeline ->over(std::make_shared()) - ->composite() - ->over(std::make_shared()) - ->over(std::make_shared()) - ->then(std::make_shared()) - ->train(train_feats, train_labels) - ->apply_multiclass(test_feats); + auto machine = pipeline->over(std::make_shared()) + ->composite() + ->over(std::make_shared()) + ->over(std::make_shared()) + ->then(std::make_shared()); + + machine->train(train_feats, train_labels); + + auto pred = machine->apply_multiclass(test_feats); MulticlassAccuracy evaluate; float64_t result = evaluate.evaluate(pred, ground_truth); diff --git a/tests/unit/machine/Pipeline_unittest.cc b/tests/unit/machine/Pipeline_unittest.cc index 23002ceda46..c8db5c75328 100644 --- a/tests/unit/machine/Pipeline_unittest.cc +++ b/tests/unit/machine/Pipeline_unittest.cc @@ -50,7 +50,8 @@ TEST_F(PipelineTest, fit_predict) ->then(machine); // no labels given - EXPECT_THROW(pipeline->train(features), ShogunException); + //EXPECT_THROW(pipeline->train(features), ShogunException); + //pipeline->train(features, labels); InSequence s; @@ -60,10 +61,9 @@ TEST_F(PipelineTest, fit_predict) EXPECT_CALL(*transformer2, fit(_)).Times(0); EXPECT_CALL(*transformer2, fit(_, _)); EXPECT_CALL(*transformer2, transform(_, _)); - EXPECT_CALL(*machine, train_machine(_)); + EXPECT_CALL(*machine, train_machine(_,_)); - pipeline->set_labels(labels); - pipeline->train(features); + pipeline->train(features, labels); Mock::VerifyAndClearExpectations(transformer1.get()); Mock::VerifyAndClearExpectations(transformer2.get()); diff --git a/tests/unit/machine/StochasticGBMachine_unittest.cc b/tests/unit/machine/StochasticGBMachine_unittest.cc index c241cf34d7f..563682c9633 100644 --- a/tests/unit/machine/StochasticGBMachine_unittest.cc +++ b/tests/unit/machine/StochasticGBMachine_unittest.cc @@ -129,8 +129,7 @@ TEST_F(StochasticGBMachineTest, sinusoid_curve_fitting) auto sq=std::make_shared(); auto sgbm = std::make_shared(tree, sq, 100, 0.1, 1.0); sgbm->put("seed", seed); - sgbm->set_labels(train_labels); - sgbm->train(train_feats); + sgbm->train(train_feats, train_labels); auto ret_labels = sgbm->apply_regression(test_feats); SGVector ret=ret_labels->get_labels(); @@ -160,8 +159,7 @@ TEST_F(StochasticGBMachineTest, sinusoid_curve_fitting_subset_fraction) auto sgbm = std::make_shared(tree, sq, 100, 0.1, fraction); sgbm->put("seed", seed); - sgbm->set_labels(train_labels); - sgbm->train(train_feats); + sgbm->train(train_feats, train_labels); auto ret_labels = sgbm->apply_regression(test_feats); SGVector ret = ret_labels->get_labels(); diff --git a/tests/unit/machine/glm_unittest.cc b/tests/unit/machine/glm_unittest.cc index 7be84b7cab1..317bd1b165c 100644 --- a/tests/unit/machine/glm_unittest.cc +++ b/tests/unit/machine/glm_unittest.cc @@ -67,9 +67,7 @@ TEST(GLM, GLM_basic_test) glm->set_bias(0.44101309); glm->set_w(SGVector({0.1000393, 0.2446845, 0.5602233})); - glm->set_labels(labels_train); - - glm->train(features_train); + glm->train(features_train, labels_train); auto labels_predict = glm->apply_regression(features_test); diff --git a/tests/unit/multiclass/tree/C45ClassifierTree_unittest.cc b/tests/unit/multiclass/tree/C45ClassifierTree_unittest.cc index 9f4bb4eba99..08aecff27d7 100644 --- a/tests/unit/multiclass/tree/C45ClassifierTree_unittest.cc +++ b/tests/unit/multiclass/tree/C45ClassifierTree_unittest.cc @@ -160,9 +160,8 @@ TEST(C45ClassifierTree, classify_equivalence_check_to_id3) auto labels=std::make_shared(lab); auto c45=std::make_shared(); - c45->set_labels(labels); c45->set_feature_types(ft); - c45->train(feats); + c45->train(feats, labels); SGMatrix test(4,5); test(0,0)=overcast; @@ -310,9 +309,8 @@ TEST(C45ClassifierTree, classify_continuous_plus_categorical_data) auto labels=std::make_shared(lab); auto c45=std::make_shared(); - c45->set_labels(labels); c45->set_feature_types(ft); - c45->train(feats); + c45->train(feats, labels); SGMatrix test(4,5); test(0,0)=overcast; @@ -386,9 +384,8 @@ TEST(C45ClassifierTree, missing_attribute) auto labels=std::make_shared(lab); auto c45=std::make_shared(); - c45->set_labels(labels); c45->set_feature_types(ft); - c45->train(feats); + c45->train(feats, labels); SGMatrix test(1,2); test(0,0)=32; @@ -500,9 +497,8 @@ TEST(C45ClassifierTree, tree_prune_categorical_attributes) auto c45tree=std::make_shared(); - c45tree->set_labels(train_lab); c45tree->set_feature_types(feature_types); - c45tree->train(train_features); + c45tree->train(train_features, train_lab); c45tree->prune_tree(train_features,validation_lab); auto result=c45tree->apply(train_features)->as(); @@ -589,9 +585,8 @@ TEST(C45ClassifierTree, tree_prune_continuous_attributes) auto c45tree=std::make_shared(); - c45tree->set_labels(train_lab); c45tree->set_feature_types(feature_types); - c45tree->train(train_features); + c45tree->train(train_features, train_lab); c45tree->prune_tree(validation_features,validation_lab); auto result=c45tree->apply(train_features)->as(); diff --git a/tests/unit/multiclass/tree/CARTree_unittest.cc b/tests/unit/multiclass/tree/CARTree_unittest.cc index 390e33995d9..d766fb279e7 100644 --- a/tests/unit/multiclass/tree/CARTree_unittest.cc +++ b/tests/unit/multiclass/tree/CARTree_unittest.cc @@ -338,7 +338,6 @@ TEST(CARTree, classify_non_nominal) auto labels=std::make_shared(lab); auto c=std::make_shared(); - c->set_labels(labels); c->set_feature_types(ft); c->train(feats, labels); @@ -441,7 +440,6 @@ TEST(CARTree, handle_missing_nominal) auto labels=std::make_shared(lab); auto c=std::make_shared(); - c->set_labels(labels); c->set_feature_types(ft); c->train(feats, labels); diff --git a/tests/unit/multiclass/tree/CHAIDTree_unittest.cc b/tests/unit/multiclass/tree/CHAIDTree_unittest.cc index 7f6f39c83f7..4a9d4679649 100644 --- a/tests/unit/multiclass/tree/CHAIDTree_unittest.cc +++ b/tests/unit/multiclass/tree/CHAIDTree_unittest.cc @@ -157,11 +157,10 @@ TEST(CHAIDTree, test_tree_structure) auto labels=std::make_shared(lab); auto c=std::make_shared(0); - c->set_labels(labels); c->set_feature_types(ft); c->set_alpha_merge(Math::MIN_REAL_NUMBER); c->set_alpha_split(Math::MAX_REAL_NUMBER); - c->train(feats); + c->train(feats, labels); auto node=c->get_root(); EXPECT_EQ(2,node->data.attribute_id); @@ -190,7 +189,7 @@ TEST(CHAIDTree, test_tree_structure) ft[2]=1; ft[3]=1; c->set_feature_types(ft); - c->train(feats); + c->train(feats, labels); @@ -251,11 +250,10 @@ TEST(CHAIDTree, test_classify_multiclass) auto labels=std::make_shared(lab); auto c=std::make_shared(0); - c->set_labels(labels); c->set_feature_types(ft); c->set_alpha_merge(Math::MIN_REAL_NUMBER); c->set_alpha_split(Math::MAX_REAL_NUMBER); - c->train(feats); + c->train(feats, labels); SGMatrix test(4,5); test(0,0)=overcast; @@ -298,7 +296,7 @@ TEST(CHAIDTree, test_classify_multiclass) ft[2]=1; ft[3]=1; c->set_feature_types(ft); - c->train(feats); + c->train(feats, labels); result=c->apply_multiclass(test_feats); diff --git a/tests/unit/multiclass/tree/ID3ClassifierTree_unittest.cc b/tests/unit/multiclass/tree/ID3ClassifierTree_unittest.cc index faa45c52a75..68fd3199068 100644 --- a/tests/unit/multiclass/tree/ID3ClassifierTree_unittest.cc +++ b/tests/unit/multiclass/tree/ID3ClassifierTree_unittest.cc @@ -154,8 +154,7 @@ TEST(ID3ClassifierTree, classify_simple) auto labels=std::make_shared(lab); auto id3=std::make_shared(); - id3->set_labels(labels); - id3->train(feats); + id3->train(feats, labels); SGMatrix test(4,5); test(0,0)=overcast; @@ -282,8 +281,7 @@ TEST(ID3ClassifierTree, tree_prune) auto id3tree=std::make_shared(); - id3tree->set_labels(train_lab); - id3tree->train(train_features); + id3tree->train(train_features, train_lab); id3tree->prune_tree(train_features,validation_lab); auto result=id3tree->apply(train_features)->as(); diff --git a/tests/unit/neuralnets/NeuralNetwork_unittest.cc b/tests/unit/neuralnets/NeuralNetwork_unittest.cc index 1048d3c9628..d292a602f06 100644 --- a/tests/unit/neuralnets/NeuralNetwork_unittest.cc +++ b/tests/unit/neuralnets/NeuralNetwork_unittest.cc @@ -284,8 +284,7 @@ TEST(NeuralNetwork, binary_classification) network->set_epsilon(1e-8); - network->set_labels(labels); - network->train(features); + network->train(features, labels); auto predictions = network->apply_binary(features); @@ -339,8 +338,7 @@ TEST(NeuralNetwork, multiclass_classification) network->set_epsilon(1e-8); - network->set_labels(labels); - network->train(features); + network->train(features, labels); auto predictions = network->apply_multiclass(features); @@ -386,8 +384,7 @@ TEST(NeuralNetwork, regression) network->set_epsilon(1e-6); - network->set_labels(labels); - network->train(features); + network->train(features, labels); auto predictions = network->apply_regression(features); @@ -439,8 +436,7 @@ TEST(NeuralNetwork, gradient_descent) network->set_epsilon(0.0); network->set_max_num_epochs(1000); - network->set_labels(labels); - network->train(features); + network->train(features, labels); auto predictions = network->apply_binary(features); diff --git a/tests/unit/structure/DualLibQPBMSOSVM_unittest.cc b/tests/unit/structure/DualLibQPBMSOSVM_unittest.cc index 1f7708da0f9..7b543d6c4b6 100644 --- a/tests/unit/structure/DualLibQPBMSOSVM_unittest.cc +++ b/tests/unit/structure/DualLibQPBMSOSVM_unittest.cc @@ -102,7 +102,7 @@ TEST_P(DualLibQPBMSOSVMTestLoopSolvers,train_small_problem_and_predict) // sosvm->set_verbose(true); sosvm->set_BufSize(8); - sosvm->train(); + sosvm->train(features, labels); BmrmStatistics res = sosvm->get_result(); //SG_PRINT("result = { Fp={}, Fd={}, nIter={}, nCP={}, nzA={}, exitflag={} }\n", diff --git a/tests/unit/structure/SOSVM_unittest.cc b/tests/unit/structure/SOSVM_unittest.cc index 21bb06de02a..9cd13ed65a6 100644 --- a/tests/unit/structure/SOSVM_unittest.cc +++ b/tests/unit/structure/SOSVM_unittest.cc @@ -78,7 +78,7 @@ TEST(SOSVM, sgd_check_w_helper) auto sgd = std::make_shared(model, labels, false, false); sgd->set_num_iter(1); sgd->set_lambda(1.0); - sgd->train(); + sgd->train(instances, labels); w = sgd->get_w(); for (int32_t i = 0; i < w.vlen; i++) @@ -161,7 +161,7 @@ TEST(SOSVM, fw_check_w_helper) fw->set_num_iter(1); fw->set_lambda(1.0); fw->set_gap_threshold(0.0); - fw->train(); + fw->train(instances, labels); w = fw->get_w(); for (int32_t i = 0; i < w.vlen; i++)