Module learnrdr :: Class RDRLearnerMDL |
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[frames] | no frames]

# Class RDRLearnerMDL

source code

    rules_adhoc.RDRLearner --+
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                            RDRLearnerMDL

Class for learning ripple-down rules from data using the minimum description length principle.

Instance Methods | [hide private]
learn(self, data_file, features_file, max_depth, c, pos_symbol, neg_symbol, min_length, max_length, regenerate) Learn RDR from data using MDL and display the results. | source code
Ruleset |
greedy_set_cover_mdl(self, data, pos_data, ruleset, possible_rules, datapoint_bits, covered_data) Find greedy set cover using MDL. | source code
(list of Rules, Rule, integer) |
find_possible_rules(self, features, data, depth, covered_data, pos_length, datapoint_bits) Find possible rules given a set of data points and features. | source code
Ruleset |
find_rules(self, data, features, depth) Find rules from data. | source code
Inherited from `rules_adhoc.RDRLearner`: `check_data`, `count_errors`, `data_for_rule`, `greedy_set_cover`, `pos_data_for_depth`

Method Details | [hide private]

### learn(self, data_file, features_file, max_depth, c, pos_symbol, neg_symbol, min_length, max_length, regenerate) | source code
Learn RDR from data using MDL and display the results. Parameters: `data_file` (string) - file containing the training data. `features_file` (string) - file containing the features or "all" if all possible substrings of given lengths are to be generated. `max_depth` (integer) - maximum depth of rules. `c` (float) - the value of parameter c in MDL. `pos_symbol` (string) - symbol signifying positive example in data. `neg_symbol` (string) - symbol signifying negative example in data. `min_length` (integer) - minimum length for substrings to be generated. `max_length` (integer) - maximum length for substrings to be generated. `regenerate` (boolean) - whether to regenerate possible rules after each time a rule is added to the ruleset Overrides: rules_adhoc.RDRLearner.learn

### greedy_set_cover_mdl(self, data, pos_data, ruleset, possible_rules, datapoint_bits, covered_data) | source code
Find greedy set cover using MDL. Parameters: `data` (dict) - data points and their classifications. `pos_data` (dict) - data points to be classified by the rules and their classifications. `ruleset` (L(Ruleset)) - rules already found. `possible_rules` (list) - possible rules that can be used for classification. `datapoint_bits` (float) - bits needed to encode one data point. `covered_data` (dict) - data points already covered by a rule. Returns: Ruleset Ruleset that was found.

### find_possible_rules(self, features, data, depth, covered_data, pos_length, datapoint_bits) | source code
Find possible rules given a set of data points and features. Parameters: `data` (dict) - data points and their classifications. `depth` (integer) - maximum depth for exceptions. `covered_data` (dict) - data points already covered by a rule. `pos_length` (integer) - number of unclassified elements. `datapoint_bits` (float) - bits needed to encode one data point. `features` (list) Returns: (list of Rules, Rule, integer) Possible rules, best rule and its index. Overrides: rules_adhoc.RDRLearner.find_possible_rules

### find_rules(self, data, features, depth) | source code
Find rules from data. Parameters: `data` (dict) - data points and their classifications. `features` (list) - possible features for classification `depth` (integer) - maximum depth for exceptions for the rules to be found. Returns: Ruleset Ruleset that was found. Overrides: rules_adhoc.RDRLearner.find_rules