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280 lines
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280 lines
12 KiB
ReStructuredText
MLData
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===================
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.. highlight:: cpp
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For the machine learning algorithms, the data set is often stored in a file of the ``.csv``-like format. The file contains a table of predictor and response values where each row of the table corresponds to a sample. Missing values are supported. The UC Irvine Machine Learning Repository (http://archive.ics.uci.edu/ml/) provides many data sets stored in such a format to the machine learning community. The class ``MLData`` is implemented to easily load the data for training one of the OpenCV machine learning algorithms. For float values, only the ``'.'`` separator is supported. The table can have a header and in such case the user have to set the number of the header lines to skip them duaring the file reading.
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CvMLData
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--------
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.. ocv:class:: CvMLData
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Class for loading the data from a ``.csv`` file.
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::
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class CV_EXPORTS CvMLData
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{
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public:
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CvMLData();
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virtual ~CvMLData();
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int read_csv(const char* filename);
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const CvMat* get_values() const;
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const CvMat* get_responses();
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const CvMat* get_missing() const;
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void set_response_idx( int idx );
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int get_response_idx() const;
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void set_train_test_split( const CvTrainTestSplit * spl);
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const CvMat* get_train_sample_idx() const;
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const CvMat* get_test_sample_idx() const;
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void mix_train_and_test_idx();
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const CvMat* get_var_idx();
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void change_var_idx( int vi, bool state );
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const CvMat* get_var_types();
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void set_var_types( const char* str );
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int get_var_type( int var_idx ) const;
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void change_var_type( int var_idx, int type);
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void set_delimiter( char ch );
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char get_delimiter() const;
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void set_miss_ch( char ch );
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char get_miss_ch() const;
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const std::map<String, int>& get_class_labels_map() const;
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protected:
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...
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};
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CvMLData::read_csv
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------------------
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Reads the data set from a ``.csv``-like ``filename`` file and stores all read values in a matrix.
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.. ocv:function:: int CvMLData::read_csv(const char* filename)
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:param filename: The input file name
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While reading the data, the method tries to define the type of variables (predictors and responses): ordered or categorical. If a value of the variable is not numerical (except for the label for a missing value), the type of the variable is set to ``CV_VAR_CATEGORICAL``. If all existing values of the variable are numerical, the type of the variable is set to ``CV_VAR_ORDERED``. So, the default definition of variables types works correctly for all cases except the case of a categorical variable with numerical class labels. In this case, the type ``CV_VAR_ORDERED`` is set. You should change the type to ``CV_VAR_CATEGORICAL`` using the method :ocv:func:`CvMLData::change_var_type`. For categorical variables, a common map is built to convert a string class label to the numerical class label. Use :ocv:func:`CvMLData::get_class_labels_map` to obtain this map.
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Also, when reading the data, the method constructs the mask of missing values. For example, values are equal to `'?'`.
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CvMLData::get_values
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--------------------
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Returns a pointer to the matrix of predictors and response values
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.. ocv:function:: const CvMat* CvMLData::get_values() const
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The method returns a pointer to the matrix of predictor and response ``values`` or ``0`` if the data has not been loaded from the file yet.
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The row count of this matrix equals the sample count. The column count equals predictors ``+ 1`` for the response (if exists) count. This means that each row of the matrix contains values of one sample predictor and response. The matrix type is ``CV_32FC1``.
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CvMLData::get_responses
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-----------------------
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Returns a pointer to the matrix of response values
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.. ocv:function:: const CvMat* CvMLData::get_responses()
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The method returns a pointer to the matrix of response values or throws an exception if the data has not been loaded from the file yet.
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This is a single-column matrix of the type ``CV_32FC1``. Its row count is equal to the sample count, one column and .
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CvMLData::get_missing
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---------------------
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Returns a pointer to the mask matrix of missing values
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.. ocv:function:: const CvMat* CvMLData::get_missing() const
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The method returns a pointer to the mask matrix of missing values or throws an exception if the data has not been loaded from the file yet.
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This matrix has the same size as the ``values`` matrix (see :ocv:func:`CvMLData::get_values`) and the type ``CV_8UC1``.
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CvMLData::set_response_idx
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--------------------------
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Specifies index of response column in the data matrix
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.. ocv:function:: void CvMLData::set_response_idx( int idx )
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The method sets the index of a response column in the ``values`` matrix (see :ocv:func:`CvMLData::get_values`) or throws an exception if the data has not been loaded from the file yet.
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The old response columns become predictors. If ``idx < 0``, there is no response.
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CvMLData::get_response_idx
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--------------------------
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Returns index of the response column in the loaded data matrix
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.. ocv:function:: int CvMLData::get_response_idx() const
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The method returns the index of a response column in the ``values`` matrix (see :ocv:func:`CvMLData::get_values`) or throws an exception if the data has not been loaded from the file yet.
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If ``idx < 0``, there is no response.
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CvMLData::set_train_test_split
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------------------------------
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Divides the read data set into two disjoint training and test subsets.
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.. ocv:function:: void CvMLData::set_train_test_split( const CvTrainTestSplit * spl )
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This method sets parameters for such a split using ``spl`` (see :ocv:class:`CvTrainTestSplit`) or throws an exception if the data has not been loaded from the file yet.
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CvMLData::get_train_sample_idx
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------------------------------
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Returns the matrix of sample indices for a training subset
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.. ocv:function:: const CvMat* CvMLData::get_train_sample_idx() const
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The method returns the matrix of sample indices for a training subset. This is a single-row matrix of the type ``CV_32SC1``. If data split is not set, the method returns ``0``. If the data has not been loaded from the file yet, an exception is thrown.
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CvMLData::get_test_sample_idx
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-----------------------------
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Returns the matrix of sample indices for a testing subset
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.. ocv:function:: const CvMat* CvMLData::get_test_sample_idx() const
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CvMLData::mix_train_and_test_idx
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--------------------------------
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Mixes the indices of training and test samples
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.. ocv:function:: void CvMLData::mix_train_and_test_idx()
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The method shuffles the indices of training and test samples preserving sizes of training and test subsets if the data split is set by :ocv:func:`CvMLData::get_values`. If the data has not been loaded from the file yet, an exception is thrown.
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CvMLData::get_var_idx
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---------------------
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Returns the indices of the active variables in the data matrix
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.. ocv:function:: const CvMat* CvMLData::get_var_idx()
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The method returns the indices of variables (columns) used in the ``values`` matrix (see :ocv:func:`CvMLData::get_values`).
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It returns ``0`` if the used subset is not set. It throws an exception if the data has not been loaded from the file yet. Returned matrix is a single-row matrix of the type ``CV_32SC1``. Its column count is equal to the size of the used variable subset.
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CvMLData::change_var_idx
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------------------------
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Enables or disables particular variable in the loaded data
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.. ocv:function:: void CvMLData::change_var_idx( int vi, bool state )
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By default, after reading the data set all variables in the ``values`` matrix (see :ocv:func:`CvMLData::get_values`) are used. But you may want to use only a subset of variables and include/exclude (depending on ``state`` value) a variable with the ``vi`` index from the used subset. If the data has not been loaded from the file yet, an exception is thrown.
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CvMLData::get_var_types
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-----------------------
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Returns a matrix of the variable types.
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.. ocv:function:: const CvMat* CvMLData::get_var_types()
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The function returns a single-row matrix of the type ``CV_8UC1``, where each element is set to either ``CV_VAR_ORDERED`` or ``CV_VAR_CATEGORICAL``. The number of columns is equal to the number of variables. If data has not been loaded from file yet an exception is thrown.
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CvMLData::set_var_types
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-----------------------
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Sets the variables types in the loaded data.
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.. ocv:function:: void CvMLData::set_var_types( const char* str )
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In the string, a variable type is followed by a list of variables indices. For example: ``"ord[0-17],cat[18]"``, ``"ord[0,2,4,10-12], cat[1,3,5-9,13,14]"``, ``"cat"`` (all variables are categorical), ``"ord"`` (all variables are ordered).
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CvMLData::get_header_lines_number
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---------------------------------
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Returns a number of the table header lines.
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.. ocv:function:: int CvMLData::get_header_lines_number() const
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CvMLData::set_header_lines_number
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---------------------------------
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Sets a number of the table header lines.
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.. ocv:function:: void CvMLData::set_header_lines_number( int n )
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By default it is supposed that the table does not have a header, i.e. it contains only the data.
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CvMLData::get_var_type
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----------------------
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Returns type of the specified variable
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.. ocv:function:: int CvMLData::get_var_type( int var_idx ) const
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The method returns the type of a variable by the index ``var_idx`` ( ``CV_VAR_ORDERED`` or ``CV_VAR_CATEGORICAL``).
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CvMLData::change_var_type
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-------------------------
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Changes type of the specified variable
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.. ocv:function:: void CvMLData::change_var_type( int var_idx, int type)
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The method changes type of variable with index ``var_idx`` from existing type to ``type`` ( ``CV_VAR_ORDERED`` or ``CV_VAR_CATEGORICAL``).
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CvMLData::set_delimiter
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-----------------------
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Sets the delimiter in the file used to separate input numbers
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.. ocv:function:: void CvMLData::set_delimiter( char ch )
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The method sets the delimiter for variables in a file. For example: ``','`` (default), ``';'``, ``' '`` (space), or other characters. The floating-point separator ``'.'`` is not allowed.
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CvMLData::get_delimiter
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-----------------------
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Returns the currently used delimiter character.
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.. ocv:function:: char CvMLData::get_delimiter() const
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CvMLData::set_miss_ch
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---------------------
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Sets the character used to specify missing values
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.. ocv:function:: void CvMLData::set_miss_ch( char ch )
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The method sets the character used to specify missing values. For example: ``'?'`` (default), ``'-'``. The floating-point separator ``'.'`` is not allowed.
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CvMLData::get_miss_ch
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---------------------
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Returns the currently used missing value character.
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.. ocv:function:: char CvMLData::get_miss_ch() const
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CvMLData::get_class_labels_map
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-------------------------------
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Returns a map that converts strings to labels.
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.. ocv:function:: const std::map<String, int>& CvMLData::get_class_labels_map() const
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The method returns a map that converts string class labels to the numerical class labels. It can be used to get an original class label as in a file.
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CvTrainTestSplit
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----------------
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.. ocv:struct:: CvTrainTestSplit
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Structure setting the split of a data set read by :ocv:class:`CvMLData`.
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::
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struct CvTrainTestSplit
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{
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CvTrainTestSplit();
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CvTrainTestSplit( int train_sample_count, bool mix = true);
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CvTrainTestSplit( float train_sample_portion, bool mix = true);
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union
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{
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int count;
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float portion;
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} train_sample_part;
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int train_sample_part_mode;
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bool mix;
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};
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There are two ways to construct a split:
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* Set the training sample count (subset size) ``train_sample_count``. Other existing samples are located in a test subset.
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* Set a training sample portion in ``[0,..1]``. The flag ``mix`` is used to mix training and test samples indices when the split is set. Otherwise, the data set is split in the storing order: the first part of samples of a given size is a training subset, the second part is a test subset.
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