@@ -63,27 +63,18 @@ Importance of each variable is computed over all the splits on this variable in
...
@@ -63,27 +63,18 @@ Importance of each variable is computed over all the splits on this variable in
CvDTreeSplit
CvDTreeSplit
------------
------------
.. c:type:: CvDTreeSplit
.. c:type:: struct CvDTreeSplit
Decision tree node split ::
Decision tree node split.
struct CvDTreeSplit
The structure represents a possible decision tree node split. It has public members:
{
int var_idx;
* ``int var_idx`` Index of variable on which the split is created.
int inversed;
* ``int inversed`` If it is not null then inverse split rule is used that is a left branch and a right branch are switched.
float quality;
* ``float quality`` Quality of the split.
CvDTreeSplit* next;
* ``CvDTreeSplit* next`` Pointer to the next split in the node list of splits.
union
* ``int subset[2]`` Parameters of the split on a categorical variable.
{
* ``struct {float c; int split_point;} ord`` Parameters of the split on ordered variable.
int subset[2];
struct
{
float c;
int split_point;
}
ord;
};
};
.. index:: CvDTreeNode
.. index:: CvDTreeNode
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@@ -92,37 +83,31 @@ Decision tree node split ::
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@@ -92,37 +83,31 @@ Decision tree node split ::
CvDTreeNode
CvDTreeNode
-----------
-----------
.. c:type:: CvDTreeNode
.. c:type:: struct CvDTreeNode
Decision tree node ::
struct CvDTreeNode
{
int class_idx;
int Tn;
double value;
CvDTreeNode* parent;
CvDTreeNode* left;
CvDTreeNode* right;
CvDTreeSplit* split;
Decision tree node.
int sample_count;
int depth;
...
};
The structure represents a node in a decision tree. It has public members:
* ``int Tn`` Tree index in a sequence of pruned trees. Nodes with :math:`Tn \leq CvDTree::pruned\_tree\_idx` are not used at prediction stage (they are pruned).
* ``double value`` Value at the node: a class label in case of classification or estimated function value in case of regression.
* ``CvDTreeNode* parent`` Pointer to the parent node.
* ``CvDTreeNode* left`` Pointer to the left child node.
* ``CvDTreeNode* right`` Pointer to the right child node.
* ``CvDTreeSplit* split`` Pointer to the first (primary) split in the node list of splits.
* ``int sample_count`` Number of samples in the node.
* ``int depth`` Depth of the node.
Other numerous fields of ``CvDTreeNode`` are used internally at the training stage.
Other numerous fields of ``CvDTreeNode`` are used internally at the training stage.
.. index:: CvDTreeParams
.. index:: CvDTreeParams
.. _CvDTreeParams:
.. _CvDTreeParams:
CvDTreeParams
CvDTreeParams
-------------
-------------
.. c:type:: CvDTreeParams
.. c:type:: struct CvDTreeParams
Decision tree training parameters.
Decision tree training parameters.
...
@@ -136,7 +121,7 @@ CvDTreeParams::CvDTreeParams
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@@ -136,7 +121,7 @@ CvDTreeParams::CvDTreeParams
----------------------------
----------------------------
.. ocv:function:: CvDTreeParams::CvDTreeParams()
.. ocv:function:: CvDTreeParams::CvDTreeParams()
.. ocv:function:: CvDTreeParams( int max_depth, int min_sample_count, float regression_accuracy, bool use_surrogates, int max_categories, int cv_folds, bool use_1se_rule, bool truncate_pruned_tree, const float* priors )
.. ocv:function:: CvDTreeParams::CvDTreeParams( int max_depth, int min_sample_count, float regression_accuracy, bool use_surrogates, int max_categories, int cv_folds, bool use_1se_rule, bool truncate_pruned_tree, const float* priors )
:param max_depth: The maximum number of levels in a tree. The depth of a constructed tree may be smaller due to other termination criterias or pruning of the tree.
:param max_depth: The maximum number of levels in a tree. The depth of a constructed tree may be smaller due to other termination criterias or pruning of the tree.
...
@@ -144,7 +129,7 @@ CvDTreeParams::CvDTreeParams
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@@ -144,7 +129,7 @@ CvDTreeParams::CvDTreeParams
:param regression_accuracy: Termination criteria for regression trees. If all absolute differences between an estimated value in a node and values of train samples in this node are less than this parameter then the node will not be splitted.
:param regression_accuracy: Termination criteria for regression trees. If all absolute differences between an estimated value in a node and values of train samples in this node are less than this parameter then the node will not be splitted.
:param use_surrogates: If true then surrogate splits will be built. These splits allow to work with missing data.
:param use_surrogates: If true then surrogate splits will be built. These splits allow to work with missing data and compute variable importance correctly.
:param max_categories: Cluster possible values of a categorical variable into ``K`` :math:`\leq` ``max_categories`` clusters to find a suboptimal split. The clustering is applied only in n>2-class classification problems for categorical variables with ``N > max_categories`` possible values. See the Learning OpenCV book (page 489) for more detailed explanation.
:param max_categories: Cluster possible values of a categorical variable into ``K`` :math:`\leq` ``max_categories`` clusters to find a suboptimal split. The clustering is applied only in n>2-class classification problems for categorical variables with ``N > max_categories`` possible values. See the Learning OpenCV book (page 489) for more detailed explanation.
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@@ -172,105 +157,11 @@ The default constructor initializes all the parameters with the default values t
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@@ -172,105 +157,11 @@ The default constructor initializes all the parameters with the default values t
CvDTreeTrainData
CvDTreeTrainData
----------------
----------------
.. c:type:: CvDTreeTrainData
.. c:type:: struct CvDTreeTrainData
Decision tree training data and shared data for tree ensembles ::
struct CvDTreeTrainData
{
CvDTreeTrainData();
CvDTreeTrainData( const Mat& _train_data, int _tflag,
virtual CvDTreeNode* new_node( CvDTreeNode* parent, int count,
int storage_idx, int offset );
virtual CvDTreeSplit* new_split_ord( int vi, float cmp_val,
int split_point, int inversed, float quality );
virtual CvDTreeSplit* new_split_cat( int vi, float quality );
virtual void free_node_data( CvDTreeNode* node );
virtual void free_train_data();
virtual void free_node( CvDTreeNode* node );
int sample_count, var_all, var_count, max_c_count;
int ord_var_count, cat_var_count;
bool have_labels, have_priors;
bool is_classifier;
int buf_count, buf_size;
Decision tree training data and shared data for tree ensembles.
bool shared;
Mat& cat_count;
The structure is mostly used internally for storing both standalone trees and tree ensembles efficiently. Basically, it contains the following types of information:
Mat& cat_ofs;
Mat& cat_map;
Mat& counts;
Mat& buf;
Mat& direction;
Mat& split_buf;
Mat& var_idx;
Mat& var_type; // i-th element =
// k<0 - ordered
// k>=0 - categorical, see k-th element of cat_* arrays
Mat& priors;
CvDTreeParams params;
CvMemStorage* tree_storage;
CvMemStorage* temp_storage;
CvDTreeNode* data_root;
CvSet* node_heap;
CvSet* split_heap;
CvSet* cv_heap;
CvSet* nv_heap;
CvRNG rng;
};
This structure is mostly used internally for storing both standalone trees and tree ensembles efficiently. Basically, it contains the following types of information:
#. Training parameters, an instance of :ref:`CvDTreeParams`.
#. Training parameters, an instance of :ref:`CvDTreeParams`.
...
@@ -283,10 +174,10 @@ There are two ways of using this structure. In simple cases (for example, a stan
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@@ -283,10 +174,10 @@ There are two ways of using this structure. In simple cases (for example, a stan
:ref:`Boosting` ), there is no need to care or even to know about the structure. You just construct the needed statistical model, train it, and use it. The ``CvDTreeTrainData`` structure is constructed and used internally. However, for custom tree algorithms or another sophisticated cases, the structure may be constructed and used explicitly. The scheme is the following:
:ref:`Boosting` ), there is no need to care or even to know about the structure. You just construct the needed statistical model, train it, and use it. The ``CvDTreeTrainData`` structure is constructed and used internally. However, for custom tree algorithms or another sophisticated cases, the structure may be constructed and used explicitly. The scheme is the following:
#.
#.
The structure is initialized using the default constructor, followed by ``set_data`` , or it is built using the full form of constructor. The parameter ``_shared`` must be set to ``true`` .
The structure is initialized using the default constructor, followed by ``set_data``, or it is built using the full form of constructor. The parameter ``_shared`` must be set to ``true``.
#.
#.
One or more trees are trained using this data (see the special form of the method ``CvDTree::train`` ).
One or more trees are trained using this data (see the special form of the method :ocv:func:`CvDTree::train`).
#.
#.
The structure is released as soon as all the trees using it are released.
The structure is released as soon as all the trees using it are released.
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@@ -297,85 +188,11 @@ There are two ways of using this structure. In simple cases (for example, a stan
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@@ -297,85 +188,11 @@ There are two ways of using this structure. In simple cases (for example, a stan
CvDTree
CvDTree
-------
-------
.. c:type:: CvDTree
.. ocv:class:: class CvDTree : public CvStatModel
Decision tree ::
Decision tree.
class CvDTree : public CvStatModel
The class implements a decision tree predictor as described in the beginning of this section.
{
public:
CvDTree();
virtual ~CvDTree();
virtual bool train( const Mat& _train_data, int _tflag,
There are four ``train`` methods in :ocv:class:`CvDTree`:
* The **first two** methods follow the generic ``CvStatModel::train`` conventions. It is the most complete form. Both data layouts (``tflag=CV_ROW_SAMPLE`` and ``tflag=CV_COL_SAMPLE``) are supported, as well as sample and variable subsets, missing measurements, arbitrary combinations of input and output variable types, and so on. The last parameter contains all of the necessary training parameters (see the :ref:`CvDTreeParams` description).
* The **third** method uses :ocv:class:`CvMLData` to pass training data to a decision tree.
* The first method follows the generic ``CvStatModel::train`` conventions. It is the most complete form. Both data layouts ( ``_tflag=CV_ROW_SAMPLE`` and ``_tflag=CV_COL_SAMPLE`` ) are supported, as well as sample and variable subsets, missing measurements, arbitrary combinations of input and output variable types, and so on. The last parameter contains all of the necessary training parameters (see the
* The **last** method ``train`` is mostly used for building tree ensembles. It takes the pre-constructed :ref:`CvDTreeTrainData` instance and an optional subset of the training set. The indices in ``subsample_idx`` are counted relatively to the ``_sample_idx`` , passed to the ``CvDTreeTrainData`` constructor. For example, if ``_sample_idx=[1, 5, 7, 100]`` , then ``subsample_idx=[0,3]`` means that the samples ``[1, 100]`` of the original training set are used.
:ref:`CvDTreeParams` description).
* The second method ``train`` is mostly used for building tree ensembles. It takes the pre-constructed
:ref:`CvDTreeTrainData` instance and an optional subset of the training set. The indices in ``_subsample_idx`` are counted relatively to the ``_sample_idx`` , passed to the ``CvDTreeTrainData`` constructor. For example, if ``_sample_idx=[1, 5, 7, 100]`` , then ``_subsample_idx=[0,3]`` means that the samples ``[1, 100]`` of the original training set are used.
.. index:: CvDTree::predict
.. index:: CvDTree::predict
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@@ -405,22 +226,19 @@ There are two ``train`` methods in ``CvDTree`` :
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@@ -405,22 +226,19 @@ There are two ``train`` methods in ``CvDTree`` :
Returns the leaf node of a decision tree corresponding to the input vector.
Returns the leaf node of a decision tree corresponding to the input vector.
The method takes the feature vector and an optional missing measurement mask as input, traverses the decision tree, and returns the reached leaf node as output. The prediction result, either the class label or the estimated function value, may be retrieved as the ``value`` field of the
The last parameter is normally set to ``false`` , implying a regular
:param preprocessedInput: This parameter is normally set to ``false``, implying a regular input. If it is ``true``, the method assumes that all the values of the discrete input variables have been already normalized to :math:`0` to :math:`num\_of\_categories_i-1` ranges since the decision tree uses such normalized representation internally. It is useful for faster prediction with tree ensembles. For ordered input variables, the flag is not used.
input. If it is ``true`` , the method assumes that all the values of
the discrete input variables have been already normalized to
The method traverses the decision tree and returns the reached leaf node as output. The prediction result, either the class label or the estimated function value, may be retrieved as the ``value`` field of the :ref:`CvDTreeNode` structure, for example: ``dtree->predict(sample,mask)->value``.
:math:`0` to
:math:`num\_of\_categories_i-1` ranges since the decision tree uses such
normalized representation internally. It is useful for faster prediction
with tree ensembles. For ordered input variables, the flag is not used.
.. index:: CvDTree::calc_error
.. index:: CvDTree::calc_error
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@@ -470,6 +288,18 @@ CvDTree::get_root
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@@ -470,6 +288,18 @@ CvDTree::get_root
Returns the root of the decision tree.
Returns the root of the decision tree.
.. index:: CvDTree::get_pruned_tree_idx
.. _CvDTree::get_pruned_tree_idx:
CvDTree::get_pruned_tree_idx
----------------------------
.. ocv:function:: int CvDTree::get_pruned_tree_idx() const
Returns the ``CvDTree::pruned_tree_idx`` parameter.
This parameter is used to prunde a decision tree. See the ``CvDTreeNode::Tn`` parameter.
