Added auto_train_rbf_classifier()

dlib/CMakeLists.txt

@@ -243,6 +243,7 @@ if (NOT TARGET dlib)
       global_optimization/global_function_search.cpp
       filtering/kalman_filter.cpp
       test_for_odr_violations.cpp
+      svm/auto.cpp
       )
 
 

dlib/all/source.cpp

@@ -90,6 +90,7 @@
 #include "../data_io/mnist.cpp"
 #include "../global_optimization/global_function_search.cpp"
 #include "../filtering/kalman_filter.cpp"
+#include "../svm/auto.cpp"
 
 
 #define DLIB_ALL_SOURCE_END

dlib/svm.h

@@ -54,6 +54,7 @@
 #include "svm/active_learning.h"
 #include "svm/svr_linear_trainer.h"
 #include "svm/sequence_segmenter.h"
+#include "svm/auto.h"
 
 #endif // DLIB_SVm_HEADER
 

dlib/svm/auto.cpp

+// Copyright (C) 2018  Davis E. King (davis@dlib.net)
+// License: Boost Software License   See LICENSE.txt for the full license.
+#ifndef DLIB_AUTO_LEARnING_CPP_
+#define DLIB_AUTO_LEARnING_CPP_
+
+#include "auto.h"
+#include "../global_optimization.h"
+#include "svm_c_trainer.h"
+
+#include <iostream>
+#include <thread>
+
+namespace dlib
+{
+
+    normalized_function<decision_function<radial_basis_kernel<matrix<double,0,1>>>> auto_train_rbf_classifier (
+        std::vector<matrix<double,0,1>> x,
+        std::vector<double> y,
+        const std::chrono::nanoseconds max_runtime,
+        bool be_verbose 
+    )
+    {
+        const auto num_positive_training_samples = sum(mat(y)>0);
+        const auto num_negative_training_samples = sum(mat(y)<0);
+        DLIB_CASSERT(num_positive_training_samples >= 6 && num_negative_training_samples >= 6,
+            "You must provide at least 6 examples of each class to this training routine.");
+        // make sure requires clause is not broken
+        DLIB_CASSERT(is_binary_classification_problem(x,y) == true,
+            "\tdecision_function svm_c_trainer::train(x,y)"
+            << "\n\t invalid inputs were given to this function"
+            << "\n\t x.size(): " << x.size() 
+            << "\n\t y.size(): " << y.size() 
+            << "\n\t is_binary_classification_problem(x,y): " << is_binary_classification_problem(x,y)
+        );
+
+
+        randomize_samples(x,y);
+
+        vector_normalizer<matrix<double,0,1>> normalizer;
+        // let the normalizer learn the mean and standard deviation of the samples
+        normalizer.train(x);
+        for (auto& samp : x)
+            samp = normalizer(samp);
+
+
+        normalized_function<decision_function<radial_basis_kernel<matrix<double,0,1>>>> df;
+        df.normalizer = normalizer;
+
+        typedef radial_basis_kernel<matrix<double,0,1>> kernel_type;
+
+        std::mutex m;
+        auto cross_validation_score = [&](const double gamma, const double c1, const double c2) 
+        {
+            svm_c_trainer<kernel_type> trainer;
+            trainer.set_kernel(kernel_type(gamma));
+            trainer.set_c_class1(c1);
+            trainer.set_c_class2(c2);
+
+            // Finally, perform 6-fold cross validation and then print and return the results.
+            matrix<double> result = cross_validate_trainer(trainer, x, y, 6);
+            if (be_verbose)
+            {
+                std::lock_guard<std::mutex> lock(m);
+                std::cout << "gamma: " << std::setw(11) << gamma << "  c1: " << std::setw(11) << c1 <<  "  c2: " << std::setw(11) << c2 <<  "  cross validation accuracy: " << result << std::flush;
+            }
+
+            // return the f1 score plus a penalty for picking large parameter settings
+            // since those are, a priori less likely to generalize.
+            return 2*prod(result)/sum(result) - std::max(c1,c2)/1e12 - gamma/1e8;
+        };
+
+
+        std::cout << "Searching for best RBF-SVM training parameters..." << std::endl;
+        auto result = find_max_global(
+            default_thread_pool(),
+            cross_validation_score, 
+            {1e-5, 1e-5, 1e-5},  // lower bound constraints on gamma, c1, and c2, respectively
+            {100,  1e6,  1e6},   // upper bound constraints on gamma, c1, and c2, respectively
+            max_runtime);
+
+        double best_gamma = result.x(0);
+        double best_c1    = result.x(1);
+        double best_c2    = result.x(2);
+
+        std::cout << " best cross-validation score: " << result.y << std::endl;
+        std::cout << " best gamma: " << best_gamma << "   best c1: " << best_c1 << "    best c2: "<< best_c2  << std::endl;
+
+        svm_c_trainer<kernel_type> trainer;
+        trainer.set_kernel(kernel_type(best_gamma));
+        trainer.set_c_class1(best_c1);
+        trainer.set_c_class2(best_c2);
+
+        std::cout << "Training final classifier with best parameters..." << std::endl;
+        df.function = trainer.train(x,y);
+
+        return df;
+    }
+}
+
+#endif // DLIB_AUTO_LEARnING_CPP_
+
+

dlib/svm/auto.h

+// Copyright (C) 2018  Davis E. King (davis@dlib.net)
+// License: Boost Software License   See LICENSE.txt for the full license.
+#ifndef DLIB_AUTO_LEARnING_Hh_
+#define DLIB_AUTO_LEARnING_Hh_
+
+#include "auto_abstract.h"
+#include "../algs.h"
+#include "function.h"
+#include "kernel.h"
+#include <chrono>
+#include <vector>
+
+namespace dlib
+{
+
+    normalized_function<decision_function<radial_basis_kernel<matrix<double,0,1>>>> auto_train_rbf_classifier (
+        std::vector<matrix<double,0,1>> x,
+        std::vector<double> y,
+        const std::chrono::nanoseconds max_runtime,
+        bool be_verbose = true
+    );
+}
+
+#endif // DLIB_AUTO_LEARnING_Hh_
+

dlib/svm/auto_abstract.h

+// Copyright (C) 2018  Davis E. King (davis@dlib.net)
+// License: Boost Software License   See LICENSE.txt for the full license.
+#undef DLIB_AUTO_LEARnING_ABSTRACT_Hh_
+#ifdef DLIB_AUTO_LEARnING_ABSTRACT_Hh_
+
+#include "kernel_abstract.h"
+#include "function_abstract.h"
+#include <chrono>
+#include <vector>
+
+namespace dlib
+{
+
+    normalized_function<decision_function<radial_basis_kernel<matrix<double,0,1>>>> auto_train_rbf_classifier (
+        std::vector<matrix<double,0,1>> x,
+        std::vector<double> y,
+        const std::chrono::nanoseconds max_runtime,
+        bool be_verbose = true
+    );
+    /*!
+        requires
+            - is_binary_classification_problem(x,y) == true
+            - y contains at least 6 examples of each class.
+        ensures
+            - This routine trains a radial basis function SVM on the given binary
+              classification training data.  It uses the svm_c_trainer to do this.  It also
+              uses find_max_global() and 6-fold cross-validation to automatically determine
+              the best settings of the SVM's hyper parameters.
+            - The hyperparameter search will run for about max_runtime and will print
+              messages to the screen as it runs if be_verbose==true.
+    !*/
+}
+
+#endif // DLIB_AUTO_LEARnING_ABSTRACT_Hh_
+
+