VowpalWabbit.h

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/*
 * Copyright (c) 2009 Yahoo! Inc.  All rights reserved.  The copyrights
 * embodied in the content of this file are licensed under the BSD
 * (revised) open source license.
 *
 * This program is free software; you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation; either version 3 of the License, or
 * (at your option) any later version.
 *
 * Written (W) 2011 Shashwat Lal Das
 * Adaptation of Vowpal Wabbit v5.1.
 * Copyright (C) 2011 Berlin Institute of Technology and Max-Planck-Society.
 */

#ifndef _VOWPALWABBIT_H__
#define _VOWPALWABBIT_H__

#include <shogun/classifier/vw/vw_common.h>
#include <shogun/classifier/vw/learners/VwAdaptiveLearner.h>
#include <shogun/classifier/vw/learners/VwNonAdaptiveLearner.h>
#include <shogun/classifier/vw/VwRegressor.h>

#include <shogun/features/streaming/StreamingVwFeatures.h>
#include <shogun/machine/OnlineLinearMachine.h>

namespace shogun
{
class CVowpalWabbit: public COnlineLinearMachine
{
public:

    MACHINE_PROBLEM_TYPE(PT_BINARY);

    CVowpalWabbit();

    CVowpalWabbit(CStreamingVwFeatures* feat);

    CVowpalWabbit(CVowpalWabbit *vw);

    ~CVowpalWabbit();

    void reinitialize_weights();

    void set_no_training(bool dont_train) { no_training = dont_train; }

    void set_adaptive(bool adaptive_learning);

    void set_exact_adaptive_norm(bool exact_adaptive);

    void set_num_passes(int32_t passes)
    {
        env->num_passes = passes;
    }

    void load_regressor(char* file_name);

    void set_regressor_out(char* file_name, bool is_text = true);

    void set_prediction_out(char* file_name);

    void add_quadratic_pair(char* pair);

    virtual bool train_machine(CFeatures* feat = NULL);

    virtual float32_t predict_and_finalize(VwExample* ex);

    float32_t compute_exact_norm(VwExample* &ex, float32_t& sum_abs_x);

    float32_t compute_exact_norm_quad(float32_t* weights, VwFeature& page_feature, v_array<VwFeature> &offer_features,
                      vw_size_t mask, float32_t g, float32_t& sum_abs_x);

    virtual CVwEnvironment* get_env()
    {
        SG_REF(env);
        return env;
    }

    virtual const char* get_name() const { return "VowpalWabbit"; }

    virtual void set_learner();

    CVwLearner* get_learner() { return learner; }

private:
    virtual void init(CStreamingVwFeatures* feat = NULL);

    virtual float32_t inline_l1_predict(VwExample* &ex);

    virtual float32_t inline_predict(VwExample* &ex);

    virtual float32_t finalize_prediction(float32_t ret);

    virtual void output_example(VwExample* &ex);

    virtual void print_update(VwExample* &ex);

    virtual void output_prediction(int32_t f, float32_t res, float32_t weight, v_array<char> tag);

    void set_verbose(bool verbose);

protected:
    CStreamingVwFeatures* features;

    CVwEnvironment* env;

    CVwLearner* learner;

    CVwRegressor* reg;

private:
    bool quiet;

    bool no_training;

    float32_t dump_interval;
    float32_t sum_loss_since_last_dump;
    float64_t old_weighted_examples;

    char* reg_name;
    bool reg_dump_text;

    bool save_predictions;
    int32_t prediction_fd;
};

}
#endif // _VOWPALWABBIT_H__