VwConditionalProbabilityTree.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 Chiyuan Zhang
 * Copyright (C) 2012 Chiyuan Zhang
 */

#include <vector>
#include <stack>
#include <shogun/multiclass/tree/VwConditionalProbabilityTree.h>

using namespace shogun;
using namespace std;

CMulticlassLabels* CVwConditionalProbabilityTree::apply_multiclass(CFeatures* data)
{
    if (data)
    {
        if (data->get_feature_class() != C_STREAMING_VW)
            SG_ERROR("Expected StreamingVwFeatures\n");
        set_features(dynamic_cast<CStreamingVwFeatures*>(data));
    }

    vector<int32_t> predicts;

    m_feats->start_parser();
    while (m_feats->get_next_example())
    {
        predicts.push_back(apply_multiclass_example(m_feats->get_example()));
        m_feats->release_example();
    }
    m_feats->end_parser();

    CMulticlassLabels *labels = new CMulticlassLabels(predicts.size());
    for (size_t i=0; i < predicts.size(); ++i)
        labels->set_int_label(i, predicts[i]);
    return labels;
}

int32_t CVwConditionalProbabilityTree::apply_multiclass_example(VwExample* ex)
{
    ex->ld->label = FLT_MAX; // this will disable VW learning from this example

    compute_conditional_probabilities(ex);
    SGVector<float64_t> probs(m_leaves.size());
    for (map<int32_t,node_t*>::iterator it = m_leaves.begin(); it != m_leaves.end(); ++it)
    {
        probs[it->first] = accumulate_conditional_probability(it->second);
    }
    return SGVector<float64_t>::arg_max(probs.vector, 1, probs.vlen);
}

void CVwConditionalProbabilityTree::compute_conditional_probabilities(VwExample *ex)
{
    stack<node_t *> nodes;
    nodes.push(m_root);

    while (!nodes.empty())
    {
        node_t *node = nodes.top();
        nodes.pop();
        if (node->left())
        {
            nodes.push(node->left());
            nodes.push(node->right());

            // don't calculate for leaf
            node->data.p_right = train_node(ex, node);
        }
    }
}

float64_t CVwConditionalProbabilityTree::accumulate_conditional_probability(node_t *leaf)
{
    float64_t prob = 1;
    node_t *par = leaf->parent();
    while (par != NULL)
    {
        if (leaf == par->left())
            prob *= (1-par->data.p_right);
        else
            prob *= par->data.p_right;

        leaf = par;
        par = leaf->parent();
    }

    return prob;
}

bool CVwConditionalProbabilityTree::train_machine(CFeatures* data)
{
    if (data)
    {
        if (data->get_feature_class() != C_STREAMING_VW)
            SG_ERROR("Expected StreamingVwFeatures\n");
        set_features(dynamic_cast<CStreamingVwFeatures*>(data));
    }
    else
    {
        if (!m_feats)
            SG_ERROR("No data features provided\n");
    }

    m_machines->reset_array();
    SG_UNREF(m_root);
    m_root = NULL;

    m_leaves.clear();

    m_feats->start_parser();
    for (int32_t ipass=0; ipass < m_num_passes; ++ipass)
    {
        while (m_feats->get_next_example())
        {
            train_example(m_feats->get_example());
            m_feats->release_example();
        }

        if (ipass < m_num_passes-1)
            m_feats->reset_stream();
    }
    m_feats->end_parser();

    return true;
}

void CVwConditionalProbabilityTree::train_example(VwExample *ex)
{
    int32_t label = static_cast<int32_t>(ex->ld->label);

    if (m_root == NULL)
    {
        m_root = new node_t();
        m_root->data.label = label;
        printf("  insert %d %p\n", label, m_root);
        m_leaves.insert(make_pair(label, m_root));
        m_root->machine(create_machine(ex));
        return;
    }

    if (m_leaves.find(label) != m_leaves.end())
    {
        train_path(ex, m_leaves[label]);
    }
    else
    {
        node_t *node = m_root;
        while (node->left() != NULL)
        {
            // not a leaf
            bool is_left = which_subtree(node, ex);
            if (is_left)
                ex->ld->label = 0;
            else
                ex->ld->label = 1;
            train_node(ex, node);

            if (is_left)
                node = node->left();
            else
                node = node->right();
        }

        printf("  remove %d %p\n", node->data.label, m_leaves[node->data.label]);
        m_leaves.erase(node->data.label);

        node_t *left_node = new node_t();
        left_node->data.label = node->data.label;
        node->data.label = -1;
        CVowpalWabbit *node_vw = dynamic_cast<CVowpalWabbit *>(m_machines->get_element(node->machine()));
        CVowpalWabbit *vw = new CVowpalWabbit(node_vw);
        SG_UNREF(node_vw);
        vw->set_learner();
        m_machines->push_back(vw);
        left_node->machine(m_machines->get_num_elements()-1);
        printf("  insert %d %p\n", left_node->data.label, left_node);
        m_leaves.insert(make_pair(left_node->data.label, left_node));
        node->left(left_node);

        node_t *right_node = new node_t();
        right_node->data.label = label;
        right_node->machine(create_machine(ex));
        printf("  insert %d %p\n", label, right_node);
        m_leaves.insert(make_pair(label, right_node));
        node->right(right_node);
    }
}

void CVwConditionalProbabilityTree::train_path(VwExample *ex, node_t *node)
{
    ex->ld->label = 0;
    train_node(ex, node);

    node_t *par = node->parent();
    while (par != NULL)
    {
        if (par->left() == node)
            ex->ld->label = 0;
        else
            ex->ld->label = 1;

        train_node(ex, par);
        node = par;
        par = node->parent();
    }
}

float64_t CVwConditionalProbabilityTree::train_node(VwExample *ex, node_t *node)
{
    CVowpalWabbit *vw = dynamic_cast<CVowpalWabbit*>(m_machines->get_element(node->machine()));
    ASSERT(vw);
    float64_t pred = vw->predict_and_finalize(ex);
    if (ex->ld->label != FLT_MAX)
        vw->get_learner()->train(ex, ex->eta_round);
    SG_UNREF(vw);
    return pred;
}

int32_t CVwConditionalProbabilityTree::create_machine(VwExample *ex)
{
    CVowpalWabbit *vw = new CVowpalWabbit(m_feats);
    vw->set_learner();
    ex->ld->label = 0;
    vw->predict_and_finalize(ex);
    m_machines->push_back(vw);
    return m_machines->get_num_elements()-1;
}