Module learnrdr_adhoc :: Class RDRLearner

Class RDRLearner

Class for learning ripple-down rules from data using the greedy set covering.

Instance Methods

learn(self, data_file, features_file, max_depth, cutoff, pos_symbol, neg_symbol, min_length, max_length, regenerate)
Learn RDR from data using greedy set covering and display the results.

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boolean

check_data(self, data, depth)
Check if any of the data points need to be classified at a particular depth.

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integer

count_errors(self, data, depth)
Count the number of elements wrongly classified at a given depth.

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dict

pos_data_for_depth(self, data, depth)
Find data points to be classified at a particular depth.

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dict

data_for_rule(self, data, rule)
Find data points covered by rule.

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Ruleset

greedy_set_cover(self, data, pos_data, ruleset, possible_rules, covered_data)
Find greedy set covering.

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(list of Rules, Rule, integer)

find_possible_rules(self, features, data, depth, covered_data, pos_length)
Find possible rules given a set of data points and features.

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Ruleset

find_rules(self, data, features, depth)
Find rules from data.

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Method Details

[hide private]

learn(self, data_file, features_file, max_depth, cutoff, pos_symbol, neg_symbol, min_length, max_length, regenerate)

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Learn RDR from data using greedy set covering 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.
cutoff (float) - cutoff value for cost of sets to be added to the covering.
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

check_data(self, data, depth)

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Check if any of the data points need to be classified at a particular depth.

Parameters:

data (dict) - data points and their classifications.
depth (integer) - depth to look at.

Returns: boolean

True, if there exists a data point currently incorrectly classified

count_errors(self, data, depth)

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Count the number of elements wrongly classified at a given depth.

Parameters:

data (dict) - data points and their classifications.
depth (integer) - depth to look at.

Returns: integer

Number of wrongly classified elements.

pos_data_for_depth(self, data, depth)

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Find data points to be classified at a particular depth.

Parameters:

data (dict) - data points and their classifications.
depth (integer) - depth to look at.

Returns: dict

Data points to be classified at a particular depth.

data_for_rule(self, data, rule)

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Find data points covered by rule.

Parameters:

data (dict) - data points to look from.
rule (L(Rule))

Returns: dict

Data points covered by the rule.

greedy_set_cover(self, data, pos_data, ruleset, possible_rules, covered_data)

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Find greedy set covering.

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.
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)

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.
features (list)

Returns: (list of Rules, Rule, integer)

Possible rules, best rule and its index.

find_rules(self, data, features, depth)

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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.