TwoStateModel.cpp

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/*
 * 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) 2012 Fernando José Iglesias García
 * Copyright (C) 2012 Fernando José Iglesias García
 */

#include <shogun/structure/TwoStateModel.h>
#include <shogun/mathematics/Math.h>
#include <shogun/features/MatrixFeatures.h>

using namespace shogun;

CTwoStateModel::CTwoStateModel() : CStateModel()
{
    // The number of states in this state model is equal to four.
    // Although parameters are learnt only for two of them, other
    // two states (start and stop) are used
    m_num_states = 4;
    m_num_transmission_params = 4;

    m_state_loss_mat = SGMatrix< float64_t >(m_num_states, m_num_states);
    m_state_loss_mat.zero();
    for ( int32_t i = 0 ; i < m_num_states-1 ; ++i )
    {
        m_state_loss_mat(m_num_states-1, i) = 1;
        m_state_loss_mat(i, m_num_states-1) = 1;
    }

    // Initialize the start and stop states
    m_p = SGVector< float64_t >(m_num_states);
    m_q = SGVector< float64_t >(m_num_states);
    m_p.set_const(-CMath::INFTY);
    m_q.set_const(-CMath::INFTY);
    m_p[0] = 0; // start state
    m_q[1] = 0; // stop  state
}

CTwoStateModel::~CTwoStateModel()
{
}

SGMatrix< float64_t > CTwoStateModel::loss_matrix(CSequence* label_seq)
{
    SGVector< int32_t > state_seq = labels_to_states(label_seq);
    SGMatrix< float64_t > loss_mat(m_num_states, state_seq.vlen);

    for ( int32_t i = 0 ; i < loss_mat.num_cols ; ++i )
    {
        for ( int32_t s = 0 ; s < loss_mat.num_rows ; ++s )
            loss_mat(s,i) = m_state_loss_mat(s, state_seq[i]);
    }

    return loss_mat;
}

float64_t CTwoStateModel::loss(CSequence* label_seq_lhs, CSequence* label_seq_rhs)
{
    SGVector< int32_t > state_seq_lhs = labels_to_states(label_seq_lhs);
    SGVector< int32_t > state_seq_rhs = labels_to_states(label_seq_rhs);

    ASSERT(state_seq_lhs.vlen == state_seq_rhs.vlen);

    float64_t ret = 0.0;
    for ( int32_t i = 0 ; i < state_seq_lhs.vlen ; ++i )
        ret += m_state_loss_mat(state_seq_lhs[i], state_seq_rhs[i]);

    return ret;
}

SGVector< int32_t > CTwoStateModel::labels_to_states(CSequence* label_seq) const
{
    // 0 -> start state
    // 1 -> stop state
    // 2 -> negative state (label == 0)
    // 3 -> positive state (label == 1)

    SGVector< int32_t > seq_data = label_seq->get_data();
    SGVector< int32_t > state_seq(seq_data.size());
    for ( int32_t i = 1 ; i < state_seq.vlen-1 ; ++i )
    {
        //FIXME make independent of values 0-1 in labels
        state_seq[i] = seq_data[i] + 2;
    }

    // The first element is always start state
    state_seq[0] = 0;
    // The last element is always stop state
    state_seq[state_seq.vlen-1] = 1;

    return state_seq;
}

CSequence* CTwoStateModel::states_to_labels(SGVector< int32_t > state_seq) const
{
    SGVector< int32_t > label_seq(state_seq.vlen);

    //FIXME make independent of values 0-1 in labels
    // Legend for state indices:
    // 0 -> start state => label 0
    // 1 -> stop state => label 0
    // 2 -> negative state (label == 0) => label 0
    // 3 -> positive state (label == 1) => label 1
    label_seq.zero();
    for ( int32_t i = 0 ; i < state_seq.vlen ; ++i )
    {
        if ( state_seq[i] == 3 )
            label_seq[i] = 1;
    }

    CSequence* ret = new CSequence(label_seq);
    SG_REF(ret);
    return ret;
}

void CTwoStateModel::reshape_emission_params(SGVector< float64_t >& emission_weights,
        SGVector< float64_t > w, int32_t num_feats, int32_t num_obs)
{
    emission_weights.zero();

    // Legend for state indices:
    // 0 -> start state
    // 1 -> stop state
    // 2 -> negative state (label == 0)
    // 3 -> positive state (label == 1)
    //
    // start and stop states have no emission scores

    index_t em_idx, w_idx = m_num_transmission_params;
    for ( int32_t s = 2 ; s < m_num_states ; ++s )
    {
        for ( int32_t f = 0 ; f < num_feats ; ++f )
        {
            for ( int32_t o = 0 ; o < num_obs ; ++o )
            {
                em_idx = s*num_feats*num_obs + f*num_obs + o;
                emission_weights[em_idx] = w[w_idx++];
            }
        }
    }
}

void CTwoStateModel::reshape_transmission_params(
        SGMatrix< float64_t >& transmission_weights, SGVector< float64_t > w)
{
    transmission_weights.set_const(-CMath::INFTY);

    // Legend for state indices:
    // 0 -> start state
    // 1 -> stop state
    // 2 -> negative state (label == 0)
    // 3 -> positive state (label == 1)

    // From start
    transmission_weights(0,2) = 0;    // to negative
    transmission_weights(0,3) = 0;    // to positive
    // From negative
    transmission_weights(2,1) = 0;    // to stop
    transmission_weights(2,2) = w[0]; // to negative
    transmission_weights(2,3) = w[1]; // to positive
    // From positive
    transmission_weights(3,1) = 0;    // to stop
    transmission_weights(3,2) = w[3]; // to positive
    transmission_weights(3,3) = w[2]; // to negative
}

void CTwoStateModel::weights_to_vector(SGVector< float64_t >& psi,
        SGMatrix< float64_t > transmission_weights,
        SGVector< float64_t > emission_weights,
        int32_t num_feats, int32_t num_obs) const
{
    // Legend for state indices:
    // 0 -> start state
    // 1 -> stop state
    // 2 -> negative state
    // 3 -> positive state
    psi[0] = transmission_weights(2,2);
    psi[1] = transmission_weights(2,3);
    psi[2] = transmission_weights(3,3);
    psi[3] = transmission_weights(3,2);

    // start and stop states have no emission scores
    index_t obs_idx, psi_idx = m_num_transmission_params;
    for ( int32_t s = 2 ; s < m_num_states ; ++s )
    {
        for ( int32_t f = 0 ; f < num_feats ; ++f )
        {
            for ( int32_t o = 0 ; o < num_obs ; ++o )
            {
                obs_idx = s*num_feats*num_obs + f*num_obs + o;
                psi[psi_idx++] = emission_weights[obs_idx];
            }
        }
    }

}

SGVector< int32_t > CTwoStateModel::get_monotonicity(int32_t num_free_states,
        int32_t num_feats) const
{
    REQUIRE(num_free_states == 2, "Using the TwoStateModel only two states are free\n");

    SGVector< int32_t > monotonicity(num_feats*num_free_states);

    for ( int32_t i = 0 ; i < num_feats ; ++i )
        monotonicity[i] = -1;
    for ( int32_t i = num_feats ; i < 2*num_feats ; ++i )
        monotonicity[i] = +1;

    return monotonicity;
}

CHMSVMModel* CTwoStateModel::simulate_two_state_data()
{
    // Number of examples
    int32_t num_exm = 1000;
    // Length of each example sequence
    int32_t exm_len = 250;
    // Number of different states
    int32_t num_states = 2;
    // Total number of features
    int32_t num_features = 10;
    // Number of features to be pure noise
    int32_t num_noise_features = 2;
    // Min and max length of positive block
    int32_t block_len[] = {10, 100};
    // Min and max number of positive blocks per example
    int32_t num_blocks[] = {0, 3};

    // Proportion of wrong labels
    float64_t prop_distort = 0.2;
    // Standard deviation of Gaussian noise
    float64_t noise_std = 4;

    // Generate label sequence randomly containing from num_blocks[0] to
    // num_blocks[1] blocks of positive labels each of length between
    // block_len[0] and block_len[1]

    CHMSVMLabels* labels = new CHMSVMLabels(num_exm, num_states);
    SGVector< int32_t > ll(num_exm*exm_len);
    ll.zero();
    int32_t rnb, rl, rp;

    for ( int32_t i = 0 ; i < num_exm ; ++i)
    {
        SGVector< int32_t > lab(exm_len);
        lab.zero();
        rnb = num_blocks[0] + CMath::ceil((num_blocks[1]-num_blocks[0])*
            CMath::random(0.0, 1.0)) - 1;

        for ( int32_t j = 0 ; j < rnb ; ++j )
        {
            rl = block_len[0] + CMath::ceil((block_len[1]-block_len[0])*
                CMath::random(0.0, 1.0)) - 1;
            rp = CMath::ceil((exm_len-rl)*CMath::random(0.0, 1.0));

            for ( int32_t idx = rp-1 ; idx < rp+rl ; ++idx )
            {
                lab[idx] = 1;
                ll[i*exm_len + idx] = 1;
            }
        }

        labels->add_label(lab);
    }

    // Generate features by
    // i) introducing label noise, i.e. flipping a propotion prop_distort
    // of labels and
    // ii) adding Gaussian noise to the (distorted) label sequence

    SGVector< int32_t >   distort(num_exm*exm_len);
    SGVector< int32_t >   d1(CMath::round(distort.vlen*prop_distort));
    SGVector< int32_t >   d2(d1.vlen);
    SGVector< int32_t >   lf;
    SGMatrix< float64_t > signal(num_features, distort.vlen);

    for ( int32_t i = 0 ; i < num_features ; ++i )
    {
        lf = ll;
        distort.randperm();

        for ( int32_t j = 0 ; j < d1.vlen ; ++j )
            d1[j] = distort[j];

        for ( int32_t j = 0 ; j < d2.vlen ; ++j )
            d2[j] = distort[ distort.vlen-d2.vlen+j ];

        for ( int32_t j = 0 ; j < d1.vlen ; ++j )
            lf[ d1[j] ] = lf[ d2[j] ];

        int32_t idx = i*signal.num_cols;
        for ( int32_t j = 0 ; j < signal.num_cols ; ++j )
            signal[idx++] = lf[j] + noise_std*CMath::normal_random((float64_t)0.0, 1.0);
    }

    // Substitute some features by pure noise
    SGVector< int32_t > ridx(num_features);
    ridx.randperm();
    for ( int32_t i = 0 ; i < num_noise_features ; ++i )
    {
        int32_t idx = i*signal.num_cols;
        for ( int32_t j = 0 ; j < signal.num_cols ; ++j )
            signal[idx++] = noise_std*CMath::normal_random((float64_t)0.0, 1.0);
    }

    CMatrixFeatures< float64_t >* features =
        new CMatrixFeatures< float64_t >(signal, exm_len, num_exm);

    return new CHMSVMModel(features, labels, SMT_TWO_STATE, 3);
}