OpenMS
2.6.0
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Simple interface to support vector machines for classification (via LIBSVM). More...
#include <OpenMS/ANALYSIS/SVM/SimpleSVM.h>
Classes | |
struct | Prediction |
SVM prediction result. More... | |
Public Types | |
typedef std::map< String, std::vector< double > > | PredictorMap |
Mapping from predictor name to vector of predictor values. More... | |
typedef std::map< String, std::pair< double, double > > | ScaleMap |
Mapping from predictor name to predictor min and max. More... | |
Public Member Functions | |
SimpleSVM () | |
Default constructor. More... | |
~SimpleSVM () override | |
Destructor. More... | |
void | setup (PredictorMap &predictors, const std::map< Size, Int > &labels) |
Load data and train a model. More... | |
void | predict (std::vector< Prediction > &predictions, std::vector< Size > indexes=std::vector< Size >()) const |
Predict class labels (and probabilities). More... | |
void | getFeatureWeights (std::map< String, double > &feature_weights) const |
Get the weights used for features (predictors) in the SVM model. More... | |
void | writeXvalResults (const String &path) const |
Write cross-validation (parameter optimization) results to a CSV file. More... | |
const ScaleMap & | getScaling () const |
Get data range of predictors before scaling to [0, 1]. More... | |
Public Member Functions inherited from DefaultParamHandler | |
DefaultParamHandler (const String &name) | |
Constructor with name that is displayed in error messages. More... | |
DefaultParamHandler (const DefaultParamHandler &rhs) | |
Copy constructor. More... | |
virtual | ~DefaultParamHandler () |
Destructor. More... | |
virtual DefaultParamHandler & | operator= (const DefaultParamHandler &rhs) |
Assignment operator. More... | |
virtual bool | operator== (const DefaultParamHandler &rhs) const |
Equality operator. More... | |
void | setParameters (const Param ¶m) |
Sets the parameters. More... | |
const Param & | getParameters () const |
Non-mutable access to the parameters. More... | |
const Param & | getDefaults () const |
Non-mutable access to the default parameters. More... | |
const String & | getName () const |
Non-mutable access to the name. More... | |
void | setName (const String &name) |
Mutable access to the name. More... | |
const std::vector< String > & | getSubsections () const |
Non-mutable access to the registered subsections. More... | |
Protected Types | |
typedef std::vector< std::vector< double > > | SVMPerformance |
Classification performance for different param. combinations (C/gamma): More... | |
Protected Member Functions | |
void | scaleData_ (PredictorMap &predictors) |
Scale predictor values to range 0-1. More... | |
void | convertData_ (const PredictorMap &predictors) |
Convert predictors to LIBSVM format. More... | |
std::pair< double, double > | chooseBestParameters_ () const |
Choose best SVM parameters based on cross-validation results. More... | |
void | optimizeParameters_ () |
Run cross-validation to optimize SVM parameters. More... | |
Protected Member Functions inherited from DefaultParamHandler | |
virtual void | updateMembers_ () |
This method is used to update extra member variables at the end of the setParameters() method. More... | |
void | defaultsToParam_ () |
Updates the parameters after the defaults have been set in the constructor. More... | |
Static Protected Member Functions | |
static void | printNull_ (const char *) |
Dummy function to suppress LIBSVM output. More... | |
Protected Attributes | |
std::vector< std::vector< struct svm_node > > | nodes_ |
Values of predictors (LIBSVM format) More... | |
struct svm_problem | data_ |
SVM training data (LIBSVM format) More... | |
struct svm_parameter | svm_params_ |
SVM parameters (LIBSVM format) More... | |
struct svm_model * | model_ |
Pointer to SVM model (LIBSVM format) More... | |
std::vector< String > | predictor_names_ |
Names of predictors in the model (excluding uninformative ones) More... | |
Size | n_parts_ |
Number of partitions for cross-validation. More... | |
std::vector< double > | log2_C_ |
Parameter values to try during optimization. More... | |
std::vector< double > | log2_gamma_ |
ScaleMap | scaling_ |
Mapping from predictor name to predictor min and max. More... | |
SVMPerformance | performance_ |
Cross-validation results. More... | |
Protected Attributes inherited from DefaultParamHandler | |
Param | param_ |
Container for current parameters. More... | |
Param | defaults_ |
Container for default parameters. This member should be filled in the constructor of derived classes! More... | |
std::vector< String > | subsections_ |
Container for registered subsections. This member should be filled in the constructor of derived classes! More... | |
String | error_name_ |
Name that is displayed in error messages during the parameter checking. More... | |
bool | check_defaults_ |
If this member is set to false no checking if parameters in done;. More... | |
bool | warn_empty_defaults_ |
If this member is set to false no warning is emitted when defaults are empty;. More... | |
Additional Inherited Members | |
Static Public Member Functions inherited from DefaultParamHandler | |
static void | writeParametersToMetaValues (const Param &write_this, MetaInfoInterface &write_here, const String &prefix="") |
Writes all parameters to meta values. More... | |
Simple interface to support vector machines for classification (via LIBSVM).
This class supports (multi-class) classification with a linear or RBF kernel. It uses cross-validation to optimize the SVM parameters C and (RBF kernel only) gamma.
SVM models are generated by the the setup() method. To simplify the scaling of input data (predictors), the data for both the training and the test set together are passed in as parameter predictors
. Given N observations of M predictors, the data are coded as a map of predictors (size M), each a numeric vector of values for different observations (size N).
The parameter labels
of setup() defines the training set; it contains the indexes of observations (corresponding to positions in the vectors in predictors
) together with the class labels for training.
To predict class labels based on a model, use the predict() method. The parameter indexes
of predict() takes a vector of indexes corresponding to the observations for which predictions should be made. (With an empty vector, the default, predictions are made for all observations, including those used for training.)
Name | Type | Default | Restrictions | Description |
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kernel | string | RBF | RBF, linear | SVM kernel |
xval | int | 5 | min: 1 | Number of partitions for cross-validation (parameter optimization) |
log2_C | float list | [-5.0, -3.0, -1.0, 1.0, 3.0, 5.0, 7.0, 9.0, 11.0, 13.0, 15.0] | Values to try for the SVM parameter 'C' during parameter optimization. A value 'x' is used as 'C = 2^x'. | |
log2_gamma | float list | [-15.0, -13.0, -11.0, -9.0, -7.0, -5.0, -3.0, -1.0, 1.0, 3.0] | Values to try for the SVM parameter 'gamma' during parameter optimization (RBF kernel only). A value 'x' is used as 'gamma = 2^x'. | |
epsilon | float | 1.0e-03 | min: 0.0 | Stopping criterion |
cache_size | float | 100.0 | min: 1.0 | Size of the kernel cache (in MB) |
no_shrinking | string | false | true, false | Disable the shrinking heuristics |
struct OpenMS::SimpleSVM::Prediction |
typedef std::map<String, std::vector<double> > PredictorMap |
Mapping from predictor name to vector of predictor values.
Mapping from predictor name to predictor min and max.
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Classification performance for different param. combinations (C/gamma):
SimpleSVM | ( | ) |
Default constructor.
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override |
Destructor.
Choose best SVM parameters based on cross-validation results.
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Convert predictors to LIBSVM format.
Get the weights used for features (predictors) in the SVM model.
Currently only supported for two-class classification. If a linear kernel is used, the weights are informative for ranking features.
Exception::Precondition | if no model has been trained, or if the classification involves more than two classes |
const ScaleMap& getScaling | ( | ) | const |
Get data range of predictors before scaling to [0, 1].
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protected |
Run cross-validation to optimize SVM parameters.
void predict | ( | std::vector< Prediction > & | predictions, |
std::vector< Size > | indexes = std::vector< Size >() |
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) | const |
Predict class labels (and probabilities).
predictions | Output vector of prediction results (same order as indexes ). |
indexes | Vector of observation indexes for which predictions are desired. If empty (default), predictions are made for all observations. |
Exception::Precondition | if no model has been trained |
Exception::InvalidValue | if an invalid index is used in indexes |
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inlinestaticprotected |
Dummy function to suppress LIBSVM output.
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protected |
Scale predictor values to range 0-1.
void setup | ( | PredictorMap & | predictors, |
const std::map< Size, Int > & | labels | ||
) |
Load data and train a model.
predictors | Mapping from predictor name to vector of predictor values (for different observations). All vectors should have the same length; values will be changed by scaling. |
labels | Mapping from observation index to class label in the training set. |
Exception::IllegalArgument | if predictors is empty |
Exception::InvalidValue | if an invalid index is used in labels |
Exception::MissingInformation | if there are fewer than two class labels in labels , or if there are not enough observations for cross-validation |
void writeXvalResults | ( | const String & | path | ) | const |
Write cross-validation (parameter optimization) results to a CSV file.
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SVM training data (LIBSVM format)
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Parameter values to try during optimization.
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Pointer to SVM model (LIBSVM format)
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Number of partitions for cross-validation.
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Values of predictors (LIBSVM format)
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Cross-validation results.
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Names of predictors in the model (excluding uninformative ones)
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Mapping from predictor name to predictor min and max.
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protected |
SVM parameters (LIBSVM format)