gml.md

the main class of gml

class GML [source]
The main process of progressive machine learning, which runs through Essential Support, Approximate Probability Estimation, select topm, select topk, construct subgraph, inference subgraph process

This class currently provides the following methods:

1. evidential_support(variable_set,update_feature_set) [source]

   Function: calculate essential support
   parameter:
   · variable_set - the set of target variables
   · update_feature_set - the set of target features
   Return: none
   Return type：none

2. approximate_probability_estimation(variable_set) [source]

   Function: Calculate approximate probability
   parameter:
   · variable_set - the set of target variables
   Return: none
   Return type：none

3. select_top_m_by_es(m) [source]

   Function: Select the first m latent variables according to the calculated Evidential Support (large to small) parameter:
   · m - The number of latent variables to be selected
   Return: a list containing m variable ids
   Return type：list

4. select_top_k_by_entropy(var_id_list, k) [source]

   Function: calculate entropy, select top_k latent variables with small entropy
   parameter:
   · mvar_id_list - Choose range
   · k - The number of latent variables to be selected
   Return: a list containing k ids
   Return type：list

5. select_evidence(var_id) [source]

   Function: Select the edges, variables and features which needed for subsequent subgraph construction
   parameter:
   · var_id - The id of the target variable
   Return: Edges, variables and features needed for subsequent subgraph construction
   Return type：set

6. construct_subgraph(var_id) [source]

   Function: Select topk latnet variables and create subgraph
   parameter:
   · var_id - The id of the target variable
   Return: According to the factor graph requirement of numbskull,return weight, variable, factor, fmap, domain_mask, edges
   Return type: multiple types

7. inference_subgraph(var_id) [source]

   Function: inference subgraph
   parameter:
   · var_id - For entity recognition, var_id is a variable id, and var_id is a set of k variables for sentiment analysis
   Return: none
   Return type：none

8. label(var_id_list) [source]

   Function: Compare the entropy of k latent variables, select the one with the smallest entropy and label it, and write the parameters learned from this graph back to self.features parameter:
   · var_id_list - A list of k ids, the probability corresponding to each variable is taken from variables
   Return: No output, directly update the label and entropy in vairables, by the way, you can update observed_variables_id and potential_variables_id
   Return type：dict

9. inference() [source]

   Function: Main flow
   parameter:
   · none
   Return: none
   Return type：none

10. score() [source]

    Function: Calculate the accuracy rate, precision rate, recall rate, f1 value of inference results, etc.
    parameter:
    · none
    Return: none
    Return type：none

11. save_results() [source]

    Function: Save the variable and feature information after inference.
    parameter:
    · none
    Return: none
    Return type：none