LPatternIdentification 0.0.1

Identify a set of L patterns from a binary class dataset

LPatternIdentification

The formal mathematical definition of the l-Pattern Identification Problem is as follows:

Input:

A finite alphabet Σ, two disjoint sets Good, Bad ⊆ Σn of strings and an integer l > 0

Problem question:

Is there a set of P patterns such that: |P| ≤ l and P → (Good, Bad)?

1. Install:

''' pip install LPatternIdentification '''

2. Load:

''' from LPatternIdentification import feature_set, split_data, get_patterns_from_feature_set, reduce_pattern_set '''

3. Prepare data:

Sort dataset by class labels

Separate observations into numpy ndarray

Separate labels into list

4. Find set of features

''' features = feature_set(observations, labels) '''

5. Split observations by classes

Here, classes are named 'Good' and 'Bad', the 'Good' class being the class of our interest.

''' split_point, Good, Bad = split_data(elections_X, elections_y) '''

6. Return a set of patterns that contain the features and are Good

''' Patterns = get_patterns_from_feature_set(Good, elections_feature_set) '''

7. Identify 'L' number of patterns such that all patterns are uniquely Good and not similar to Bad patterns

''' Patterns_identified = reduce_pattern_set(Patterns, Bad, 7) '''