HashedDenseFeatures.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) 2013 Evangelos Anagnostopoulos
 * Copyright (C) 2013 Evangelos Anagnostopoulos
 */

#include <shogun/features/hashed/HashedDenseFeatures.h>
#include <shogun/base/Parameter.h>
#include <shogun/lib/Hash.h>
#include <shogun/lib/DynamicArray.h>
#include <shogun/io/SGIO.h>
#include <shogun/mathematics/Math.h>

#include <shogun/lib/SGVector.h>
#include <shogun/lib/SGSparseVector.h>

#include <string.h>

namespace shogun {
template <class ST>
CHashedDenseFeatures<ST>::CHashedDenseFeatures(int32_t size, bool use_quadr, bool keep_lin_terms)
: CDotFeatures(size)
{
    init(NULL, 0, use_quadr, keep_lin_terms);
}

template <class ST>
CHashedDenseFeatures<ST>::CHashedDenseFeatures(CDenseFeatures<ST>* feats, int32_t d,
    bool use_quadr, bool keep_lin_terms) : CDotFeatures()
{
    init(feats, d, use_quadr, keep_lin_terms);
}

template <class ST>
CHashedDenseFeatures<ST>::CHashedDenseFeatures(SGMatrix<ST> matrix, int32_t d, bool use_quadr,
    bool keep_lin_terms) : CDotFeatures()
{
    CDenseFeatures<ST>* feats = new CDenseFeatures<ST>(matrix);
    init(feats, d, use_quadr, keep_lin_terms);
}

template <class ST>
CHashedDenseFeatures<ST>::CHashedDenseFeatures(ST* src, int32_t num_feat, int32_t num_vec,
    int32_t d, bool use_quadr, bool keep_lin_terms) : CDotFeatures()
{
    CDenseFeatures<ST>* feats = new CDenseFeatures<ST>(src, num_feat, num_vec);
    init(feats, d, use_quadr, keep_lin_terms);
}

template <class ST>
CHashedDenseFeatures<ST>::CHashedDenseFeatures(CFile* loader, int32_t d, bool use_quadr,
    bool keep_lin_terms) : CDotFeatures(loader)
{
    CDenseFeatures<ST>* feats = new CDenseFeatures<ST>();
    feats->load(loader);
    init(feats, d, use_quadr, keep_lin_terms);
}

template <class ST>
void CHashedDenseFeatures<ST>::init(CDenseFeatures<ST>* feats, int32_t d, bool use_quadr,
    bool keep_lin_terms)
{
    dim = d;
    dense_feats = feats;
    SG_REF(dense_feats);
    use_quadratic = use_quadr;
    keep_linear_terms = keep_lin_terms;

    SG_ADD(&use_quadratic, "use_quadratic", "Whether to use quadratic features",
        MS_NOT_AVAILABLE);
    SG_ADD(&keep_linear_terms, "keep_linear_terms", "Whether to keep the linear terms or not",
        MS_NOT_AVAILABLE);
    SG_ADD(&dim, "dim", "Dimension of new feature space", MS_NOT_AVAILABLE);
    SG_ADD((CSGObject** ) &dense_feats, "dense_feats", "Dense features to work on",
        MS_NOT_AVAILABLE);

    set_generic<ST>();
}

template <class ST>
CHashedDenseFeatures<ST>::CHashedDenseFeatures(const CHashedDenseFeatures& orig)
: CDotFeatures(orig)
{
    init(orig.dense_feats, orig.dim, orig.use_quadratic, orig.keep_linear_terms);
}

template <class ST>
CHashedDenseFeatures<ST>::~CHashedDenseFeatures()
{
    SG_UNREF(dense_feats);
}

template <class ST>
CFeatures* CHashedDenseFeatures<ST>::duplicate() const
{
    return new CHashedDenseFeatures<ST>(*this);
}

template <class ST>
int32_t CHashedDenseFeatures<ST>::get_dim_feature_space() const
{
    return dim;
}

template <class ST>
float64_t CHashedDenseFeatures<ST>::dot(int32_t vec_idx1, CDotFeatures* df,
    int32_t vec_idx2)
{
    ASSERT(df)
    ASSERT(df->get_feature_type() == get_feature_type())
    ASSERT(df->get_feature_class() == get_feature_class())
    ASSERT(strcmp(df->get_name(), get_name())==0)

    CHashedDenseFeatures<ST>* feats = (CHashedDenseFeatures<ST>* ) df;
    ASSERT(feats->get_dim_feature_space() == get_dim_feature_space())

    SGSparseVector<ST> vec_1 = get_hashed_feature_vector(vec_idx1);

    bool same_vec = (df == this) && (vec_idx1 == vec_idx2);
    SGSparseVector<ST> vec_2 = same_vec ? vec_1 : feats->get_hashed_feature_vector(vec_idx2);
    float64_t result = vec_1.sparse_dot(vec_2);

    return result;
}

template <class ST>
float64_t CHashedDenseFeatures<ST>::dense_dot(int32_t vec_idx1, const float64_t* vec2,
    int32_t vec2_len)
{
    ASSERT(vec2_len == dim)

    SGVector<ST> vec = dense_feats->get_feature_vector(vec_idx1);

    float64_t result = 0;

    int32_t hash_cache_size = use_quadratic ? vec.vlen : 0;
    SGVector<uint32_t> hash_cache(hash_cache_size);

    for (index_t i=0; i<vec.vlen; i++)
    {
        uint32_t h_idx = CHash::MurmurHash3((uint8_t* ) &i, sizeof (index_t), i);
        if (use_quadratic)
            hash_cache[i] = h_idx;

        if ( (!use_quadratic) || keep_linear_terms)
            result += vec2[h_idx % dim] * vec[i];
    }

    if (use_quadratic)
    {
        for (index_t i=0; i<vec.size(); i++)
        {
            int32_t n_idx = i * vec.size() + i;
            uint32_t idx = CHash::MurmurHash3((uint8_t* ) &n_idx, sizeof (index_t), n_idx) % dim;
            result += vec2[idx] * vec[i] * vec[i];

            for (index_t j=i+1; j<vec.size(); j++)
            {
                idx = (hash_cache[i] ^ hash_cache[j]) % dim;
                result += vec2[idx] * vec[i] * vec[j];
            }
        }
    }

    dense_feats->free_feature_vector(vec, vec_idx1);
    return result;
}

template <class ST>
void CHashedDenseFeatures<ST>::add_to_dense_vec(float64_t alpha, int32_t vec_idx1,
    float64_t* vec2, int32_t vec2_len, bool abs_val)
{
    float64_t val = abs_val ? CMath::abs(alpha) : alpha;
    ASSERT(vec2_len == dim)

    SGVector<ST> vec = dense_feats->get_feature_vector(vec_idx1);

    int32_t hash_cache_size = use_quadratic ? vec.vlen : 0;
    SGVector<uint32_t> hash_cache(hash_cache_size);

    for (index_t i=0; i<vec.vlen; i++)
    {
        uint32_t h_idx = CHash::MurmurHash3((uint8_t* ) &i, sizeof (index_t), i);

        if (use_quadratic)
            hash_cache[i] = h_idx;

        if ( (!use_quadratic) || keep_linear_terms)
            vec2[h_idx % dim] += val * vec[i];
    }

    if (use_quadratic)
    {
        for (index_t i=0; i<vec.size(); i++)
        {
            int32_t n_idx = i * vec.size() + i;
            uint32_t idx = CHash::MurmurHash3((uint8_t* ) &n_idx, sizeof (index_t), n_idx) % dim;
            vec2[idx] += val * vec[i] * vec[i];

            for (index_t j=i+1; j<vec.size(); j++)
            {
                idx = (hash_cache[i] ^ hash_cache[j]) % dim;
                vec2[idx] += val * vec[i] * vec[j];
            }
        }
    }
    dense_feats->free_feature_vector(vec, vec_idx1);
}

template <class ST>
int32_t CHashedDenseFeatures<ST>::get_nnz_features_for_vector(int32_t num)
{
    return dim;
}

template <class ST>
void* CHashedDenseFeatures<ST>::get_feature_iterator(int32_t vector_index)
{
    SG_NOTIMPLEMENTED;
    return NULL;
}
template <class ST>
bool CHashedDenseFeatures<ST>::get_next_feature(int32_t& index, float64_t& value,
    void* iterator)
{
    SG_NOTIMPLEMENTED;
    return false;
}
template <class ST>
void CHashedDenseFeatures<ST>::free_feature_iterator(void* iterator)
{
    SG_NOTIMPLEMENTED;
}

template <class ST>
const char* CHashedDenseFeatures<ST>::get_name() const
{
    return "HashedDenseFeatures";
}

template <class ST>
EFeatureType CHashedDenseFeatures<ST>::get_feature_type() const
{
    return F_UINT;
}

template <class ST>
EFeatureClass CHashedDenseFeatures<ST>::get_feature_class() const
{
    return C_SPARSE;
}

template <class ST>
int32_t CHashedDenseFeatures<ST>::get_num_vectors() const
{
    return dense_feats->get_num_vectors();
}

template <class ST>
SGSparseVector<ST> CHashedDenseFeatures<ST>::get_hashed_feature_vector(int32_t vec_idx)
{
    SGVector<ST> vec = dense_feats->get_feature_vector(vec_idx);
    SGSparseVector<ST> hashed_vec = CHashedDenseFeatures<ST>::hash_vector(
            vec, dim, use_quadratic, keep_linear_terms);
    dense_feats->free_feature_vector(vec, vec_idx);
    return hashed_vec;
}

template <class ST>
SGSparseVector<ST> CHashedDenseFeatures<ST>::hash_vector(SGVector<ST> vec, int32_t dim,
    bool use_quadratic, bool keep_linear_terms)
{
    SGVector<ST> h_vec(dim);
    SGVector<ST>::fill_vector(h_vec.vector, dim, 0);

    int32_t hash_cache_size = use_quadratic ? vec.vlen : 0;
    SGVector<uint32_t> hash_cache(hash_cache_size);

    for (index_t i=0; i<vec.size(); i++)
    {
        uint32_t hash = CHash::MurmurHash3((uint8_t* ) &i, sizeof (index_t), i);
        if (use_quadratic)
            hash_cache[i] = hash;

        if ( (!use_quadratic) || keep_linear_terms)
            h_vec[hash % dim] += vec[i];
    }

    if (use_quadratic)
    {
        for (index_t i=0; i<vec.size(); i++)
        {
            index_t n_idx = i * vec.size() + i;
            uint32_t idx = CHash::MurmurHash3((uint8_t* ) &n_idx, sizeof (index_t), n_idx) % dim;
            h_vec[idx] += vec[i] * vec[i];

            for (index_t j=i+1; j<vec.size(); j++)
            {
                idx = (hash_cache[i] ^ hash_cache[j]) % dim;
                h_vec[idx] += vec[i] * vec[j];
            }
        }
    }

    int32_t num_nnz_feats = 0;
    for(index_t i=0; i<dim; i++)
    {
        if (h_vec[i]!=0)
            num_nnz_feats++;
    }

    SGSparseVector<ST> hashed_vector(num_nnz_feats);

    int32_t sparse_feat_index = 0;
    for (index_t i=0; i<dim; i++)
    {
        if (h_vec[i]!=0)
        {
            hashed_vector.features[sparse_feat_index].feat_index = i;
            hashed_vector.features[sparse_feat_index++].entry = h_vec[i];
        }
    }

    return hashed_vector;
}

template class CHashedDenseFeatures<bool>;
template class CHashedDenseFeatures<char>;
template class CHashedDenseFeatures<int8_t>;
template class CHashedDenseFeatures<uint8_t>;
template class CHashedDenseFeatures<int16_t>;
template class CHashedDenseFeatures<uint16_t>;
template class CHashedDenseFeatures<int32_t>;
template class CHashedDenseFeatures<uint32_t>;
template class CHashedDenseFeatures<int64_t>;
template class CHashedDenseFeatures<uint64_t>;
template class CHashedDenseFeatures<float32_t>;
template class CHashedDenseFeatures<float64_t>;
template class CHashedDenseFeatures<floatmax_t>;
}