Quick Start

Constraint-based Sequential Pattern Mining

# Example to show how to find frequent sequential patterns
# from a given sequence database subject to constraints
from sequential.seq2pat import Seq2Pat, Attribute

# Seq2Pat over 3 sequences
seq2pat = Seq2Pat(sequences=[["A", "A", "B", "A", "D"],
                             ["C", "B", "A"],
                             ["C", "A", "C", "D"]])

# Price attribute corresponding to each item
price = Attribute(values=[[5, 5, 3, 8, 2],
                          [1, 3, 3],
                          [4, 5, 2, 1]])

# Average price constraint
seq2pat.add_constraint(3 <= price.average() <= 4)

# Patterns that occur at least twice (A-D)
patterns = seq2pat.get_patterns(min_frequency=2)

Dichotomic Pattern Mining

# Example to show how to run Dichotomic Pattern Mining
# on sequences with positive and negative outcomes
from sequential.seq2pat import Seq2Pat
from sequential.pat2feat import Pat2Feat
from sequential.dpm import dichotomic_pattern_mining, DichotomicAggregation

# Create seq2pat model for positive sequences
sequences_pos = [["A", "A", "B", "A", "D"]]
seq2pat_pos = Seq2Pat(sequences=sequences_pos)

# Create seq2pat model for negative sequences
sequences_neg = [["C", "B", "A"], ["C", "A", "C", "D"]]
seq2pat_neg = Seq2Pat(sequences=sequences_neg)

# Run DPM to get mined patterns
aggregation_to_patterns = dichotomic_pattern_mining(seq2pat_pos, seq2pat_neg,
                                                    min_frequency_pos=1,
                                                    min_frequency_neg=2)

# DPM patterns with Union aggregation
dpm_patterns = aggregation_to_patterns[DichotomicAggregation.union]

# Encodings of all sequences
sequences = sequences_pos + sequences_neg
pat2feat = Pat2Feat()
encodings = pat2feat.get_features(sequences, dpm_patterns,
                                  drop_pattern_frequency=False)