evidential_support.md

the class for computing evidential support

class EvidentialSupport(variables,features,method,evidence_interval_count,interval_evidence_count) [source]
the set of computing evidential support

Parameters：

• variables - variables
• features - factors
• method - The method of computing evidential support, the default value is 'regression'
• evidence_interval_count - The number of intervals, the default value is 10
• interval_evidence_count - The number of variables in each interval, the default value is 200

This class currently provides the following methods：

1. get_unlabeled_var_feature_evi()[source]

   Function: Calculate the ratio of 0 to 1 in the evidential variables associated with each unary feature of each latent variable, and the variable id at the other end of the binary feature

2. separate_feature_value()[source]

   Function: Select the easy feature value of each feature for linear regression

3. create_csr_matrix()[source]

   Function: Create a sparse matrix to store featureValue of all variables for subsequent calculation of Evidential Support Return：featureValue Return type：sparse matrix

4. influence_modeling()[source]

   Function: Perform linear regression on the updated feature, save all the results of the regression back to the feature, the key is 'regression' Parameter： · update_feature_set - the set of updated feature

5. init_tau_and_alpha()[source]

   Function: calculate tau and alpha for feature
   Parameter： · feature_set - the set of feature_id

6. computer_unary_feature_es()[source]

   Function: calculate the essential support of each latent variable in a given set of latent variables, suitable for Aspect-level sentiment analysis Parameter：
   · variable_set - a set of latent variables

7. evidential_support()[source]

   Function: Calculate the Evidential Support of all latent variables Parameter：
   · update_feature_set - the set of updated feature · variable_set -The set of variables to be calculated the evidence support.

8. get_dict_rel_acc()[source]

   Function: Calculate the accuracy of different types of relationships

9. construct_mass_function_for_propensity()[source]

   Function: Build a mass function for Evidential Support calculation Parameter：
   · uncertain_degree - Feature uncertainty
   · label_prob - The probability of label matching, which represents the proportion of positive instances for word features and the accuracy of relationship features for relationship features
   · unlabel_prob - The probability of label unmatch, which represents the proportion of negative instances for word features and 1 minus the accuracy of relationship features for relationship features Return : MassFunction
   Return type : function

10. construct_mass_function_for_ER()[source]

    Function: Build a mass function for Evidential Support calculation Parameter：
    · theta - Feature uncertainty
    Return : MassFunction
    Return type : function

11. labeling_propensity_with_ds()[source]

    Function: Combine different methods for different types of evidences for aspect-level sentiment analysis

12. evidential_support_by_custom()[source]

    Function: User-defined method for calculating essential support Parameter：
    · variable_set - a set of latent variables

the class of related Linear regression

class Regression(each_feature_easys, n_job, effective_training_count_threshold)[source]
The class of related Linear regression, perform linear regression on all features, used for the essential support calculation of Entity Resolution

Parameter：

• each_feature_easys - The feature_value of the easy variable owned by each feature
• n_job - calculate the number of threads
• effective_training_count_threshold - The minimum number of effective samples, the default value is 2

This class currently provides the following methods:

1. perform()[source]

   Function: Perform linear regression method, suitable for Entity Resolution