SHOGUN
4.1.0
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Class CDependenceMaximization, base class for all feature selection preprocessors which select a subset of features that shows maximum dependence between the features and the labels. This is done via an implementation of CIndependenceTest, m_estimator inside compute_measures() (see class documentation of CFeatureSelection), which performs a statistical test for a given feature \(\mathbf{X}_i\) from the set of features \(\mathbf{X}\), and the labels \(\mathbf{Y}\). The test checks
\[ \textbf{H}_0 : P\left(\mathbf{X}\setminus \mathbf{X}_i, \mathbf{Y}\right) =P\left(\mathbf{X}\setminus \mathbf{X}_i\right)P\left(\mathbf{Y}\right) \]
The test statistic is then used as a measure which signifies the independence between the rest of the features and the labels - higher the value of the test statistic, greater the dependency between the rest of the features and the class labels, and therefore lesser significant the current feature becomes. Therefore, highest scoring features are removed. The removal policy thus can only be shogun::N_LARGEST and shogun::PERCENTILE_LARGEST and it can be set via set_policy() call. remove_feats() method handles the removal of features based on the specified policy.
The estimator cannot be set via user interface, rather its subclasses initialize this estimator with appropriate instances internally.
This class also overrides set_labels() method to create a feature object from the labels and sets this as features \(\mathbf{Y}\sim q\) to the estimator which is required to compute the measure.
Definition at line 70 of file DependenceMaximization.h.
Public Attributes | |
SGIO * | io |
Parallel * | parallel |
Version * | version |
Parameter * | m_parameters |
Parameter * | m_model_selection_parameters |
Parameter * | m_gradient_parameters |
uint32_t | m_hash |
Protected Member Functions | |
virtual CFeatures * | create_transformed_copy (CFeatures *features, index_t idx) |
virtual CFeatures * | apply_backward_elimination (CFeatures *features) |
virtual void | precompute () |
virtual void | adapt_params (CFeatures *features) |
index_t | get_num_features (CFeatures *features) const |
virtual void | load_serializable_pre () throw (ShogunException) |
virtual void | load_serializable_post () throw (ShogunException) |
virtual void | save_serializable_pre () throw (ShogunException) |
virtual void | save_serializable_post () throw (ShogunException) |
Default constructor
Definition at line 40 of file DependenceMaximization.cpp.
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Destructor
Definition at line 57 of file DependenceMaximization.cpp.
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Tunes the parameters which are required to compute the measure based on current features. Overridden in the subclasses. Here it does nothing.
features | the features based on which parameters are needed to be tuned for computing measures |
Generic interface for applying the feature selection preprocessor. Acts as a wrapper which decides which actual method to call based on the algorithm specified.
features | the input features |
Implements CPreprocessor.
Applies backward elimination algorithm for performing feature selection. After performing necessary precomputing (defined by subclasses), it iteratively eliminates a number of features based on a measure until target dimension is reached.
features | the input features |
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Builds a dictionary of all parameters in SGObject as well of those of SGObjects that are parameters of this object. Dictionary maps parameters to the objects that own them.
dict | dictionary of parameters to be built. |
Definition at line 597 of file SGObject.cpp.
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performs cleanup
Implements CPreprocessor.
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Creates a clone of the current object. This is done via recursively traversing all parameters, which corresponds to a deep copy. Calling equals on the cloned object always returns true although none of the memory of both objects overlaps.
Definition at line 714 of file SGObject.cpp.
Method that computes the measures using test statistic computed by an instance of CIndependenceTest wiht the provided features and the labels
features | the features on which the measure has to be computed |
idx | the index that decides which features should we compute the measure on |
Implements CFeatureSelection< float64_t >.
Definition at line 106 of file DependenceMaximization.cpp.
Helper method which removes the dimension specified via the index. It copies the rest of the features into a separate object via copy_dimension_subset() call.
features | the features |
idx | index of the dimension which is required to be removed |
Definition at line 78 of file DependenceMaximization.cpp.
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A deep copy. All the instance variables will also be copied.
Definition at line 198 of file SGObject.cpp.
Recursively compares the current SGObject to another one. Compares all registered numerical parameters, recursion upon complex (SGObject) parameters. Does not compare pointers!
May be overwritten but please do with care! Should not be necessary in most cases.
other | object to compare with |
accuracy | accuracy to use for comparison (optional) |
tolerant | allows linient check on float equality (within accuracy) |
Definition at line 618 of file SGObject.cpp.
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Implements CPreprocessor.
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Implements CPreprocessor.
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Definition at line 498 of file SGObject.cpp.
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Returns description of a given parameter string, if it exists. SG_ERROR otherwise
param_name | name of the parameter |
Definition at line 522 of file SGObject.cpp.
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Returns index of model selection parameter with provided index
param_name | name of model selection parameter |
Definition at line 535 of file SGObject.cpp.
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Reimplemented from CFeatureSelection< float64_t >.
Reimplemented in CBAHSIC, and CKernelDependenceMaximization.
Definition at line 136 of file DependenceMaximization.h.
Returns the number of features of the provided feature object. Since the number of features doesn't make sense for all types of features, this helper method checks whether obtaining a num_features is possible and then calls get_num_features() on those features after proper type-cast
features | the feature object |
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Initialize preprocessor from features
features | the features |
Implements CPreprocessor.
Definition at line 63 of file DependenceMaximization.cpp.
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If the SGSerializable is a class template then TRUE will be returned and GENERIC is set to the type of the generic.
generic | set to the type of the generic if returning TRUE |
Definition at line 296 of file SGObject.cpp.
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Load this object from file. If it will fail (returning FALSE) then this object will contain inconsistent data and should not be used!
file | where to load from |
prefix | prefix for members |
Definition at line 369 of file SGObject.cpp.
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protectedvirtualinherited |
Can (optionally) be overridden to post-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::LOAD_SERIALIZABLE_POST is called.
ShogunException | will be thrown if an error occurs. |
Reimplemented in CKernel, CWeightedDegreePositionStringKernel, CList, CAlphabet, CLinearHMM, CGaussianKernel, CInverseMultiQuadricKernel, CCircularKernel, and CExponentialKernel.
Definition at line 426 of file SGObject.cpp.
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Can (optionally) be overridden to pre-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::LOAD_SERIALIZABLE_PRE is called.
ShogunException | will be thrown if an error occurs. |
Reimplemented in CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, and CDynamicObjectArray.
Definition at line 421 of file SGObject.cpp.
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Definition at line 262 of file SGObject.cpp.
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Performs the tasks which can be computed beforehand before the actual algorithm begins. This method is overridden in the subclasses. Here it does nothing.
Reimplemented in CKernelDependenceMaximization.
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prints all parameter registered for model selection and their type
Definition at line 474 of file SGObject.cpp.
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prints registered parameters out
prefix | prefix for members |
Definition at line 308 of file SGObject.cpp.
Method which handles the removal of features based on removal policy. see documentation of CFeatureSelection. For dependence maximization approach, the highest scoring features are removed. Therefore, only m_policy can only be shogun::N_LARGEST, shogun::PERCENTILE_LARGEST. See set_policy() method for specifying the exact policy
features | the features object from which specific features has to be removed |
ranks | the ranks of the features based on their measures, 0 being the lowest rank which corresponds to smallest measure. |
Implements CFeatureSelection< float64_t >.
Definition at line 130 of file DependenceMaximization.cpp.
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Save this object to file.
file | where to save the object; will be closed during returning if PREFIX is an empty string. |
prefix | prefix for members |
Definition at line 314 of file SGObject.cpp.
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Can (optionally) be overridden to post-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::SAVE_SERIALIZABLE_POST is called.
ShogunException | will be thrown if an error occurs. |
Reimplemented in CKernel.
Definition at line 436 of file SGObject.cpp.
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Can (optionally) be overridden to pre-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::SAVE_SERIALIZABLE_PRE is called.
ShogunException | will be thrown if an error occurs. |
Reimplemented in CKernel, CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, and CDynamicObjectArray.
Definition at line 431 of file SGObject.cpp.
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Abstract method which is overridden in the subclasses to set accepted feature selection algorithm
algorithm | the feature selection algorithm to use |
Implements CFeatureSelection< float64_t >.
Implemented in CKernelDependenceMaximization, and CBAHSIC.
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Definition at line 41 of file SGObject.cpp.
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Definition at line 46 of file SGObject.cpp.
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Definition at line 51 of file SGObject.cpp.
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Definition at line 56 of file SGObject.cpp.
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Definition at line 61 of file SGObject.cpp.
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Definition at line 66 of file SGObject.cpp.
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Definition at line 71 of file SGObject.cpp.
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Definition at line 76 of file SGObject.cpp.
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Definition at line 81 of file SGObject.cpp.
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Definition at line 86 of file SGObject.cpp.
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Definition at line 91 of file SGObject.cpp.
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Definition at line 96 of file SGObject.cpp.
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Definition at line 101 of file SGObject.cpp.
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Definition at line 106 of file SGObject.cpp.
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Definition at line 111 of file SGObject.cpp.
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set generic type to T
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set the parallel object
parallel | parallel object to use |
Definition at line 241 of file SGObject.cpp.
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set the version object
version | version object to use |
Definition at line 283 of file SGObject.cpp.
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Setter for labels. This method is overridden to internally convert the labels to a dense feature object and set this feature in the independence test estimator. These labels serve as samples \(\mathbf{Y}\sim q\) in the independence test
labels | the labels |
Reimplemented from CFeatureSelection< float64_t >.
Definition at line 187 of file DependenceMaximization.cpp.
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Use this method to set the number or percentile of features to be removed in each iteration.
num_remove | number or percentage of features to be removed in each iteration |
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policy | feature removal policy |
Implements CFeatureSelection< float64_t >.
Definition at line 179 of file DependenceMaximization.cpp.
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target_dim | the target dimension to achieve |
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A shallow copy. All the SGObject instance variables will be simply assigned and SG_REF-ed.
Reimplemented in CGaussianKernel.
Definition at line 192 of file SGObject.cpp.
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unset generic type
this has to be called in classes specializing a template class
Definition at line 303 of file SGObject.cpp.
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Updates the hash of current parameter combination
Definition at line 248 of file SGObject.cpp.
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io
Definition at line 369 of file SGObject.h.
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Wrapper algorithm for feature selection
Definition at line 302 of file FeatureSelection.h.
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The estimator for performing statistical tests for independence which is used for computing measures
Definition at line 157 of file DependenceMaximization.h.
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parameters wrt which we can compute gradients
Definition at line 384 of file SGObject.h.
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Hash of parameter values
Definition at line 387 of file SGObject.h.
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The labels for the feature vectors
Definition at line 316 of file FeatureSelection.h.
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The feature for the labels
Definition at line 160 of file DependenceMaximization.h.
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model selection parameters
Definition at line 381 of file SGObject.h.
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Number or percentage of features to be removed. When the policy is set as shogun::N_SMALLEST or shogun::N_LARGEST, this number decides how many features in an iteration. For shogun::PERCENTILE_SMALLEST or shogun::PERCENTILE_LARGEST, this decides the percentage of current number of features to be removed in each iteration
Definition at line 313 of file FeatureSelection.h.
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parameters
Definition at line 378 of file SGObject.h.
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Feature removal policy
Definition at line 305 of file FeatureSelection.h.
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The indices of features that are selected
Definition at line 319 of file FeatureSelection.h.
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Target dimension
Definition at line 299 of file FeatureSelection.h.
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parallel
Definition at line 372 of file SGObject.h.
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version
Definition at line 375 of file SGObject.h.