seq2pat 1.2.1

Seq2Pat: Sequence-to-Pattern Generation Library

Seq2Pat: Sequence-to-Pattern Generation Library

Seq2Pat is a research library for sequence-to-pattern generation to discover sequential patterns that occur frequently in large sequence databases. The library supports constraint-based reasoning to specify desired properties over patterns.

From an algorithmic perspective, the library takes advantage of multi-valued decision diagrams. It is based on the state-of-the-art approach for sequential pattern mining from Hosseininasab et. al. AAAI 2019.

From an implementation perspective, the library is written in Cython that brings together the efficiency of a low-level C++ backend and the expressiveness of a high-level Python public interface.

Seq2Pat is developed as a joint collaboration between Fidelity Investments and the Tepper School of Business at CMU. Documentation is available at fidelity.github.io/seq2pat.

Quick Start

# 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)

Available Constraints

The library offers various constraint types, including a number of non-monotone constraints.

• Average: This constraint specifies the average value of an attribute across all events in a pattern.
• Gap: This constraint specifies the difference between the attribute values of every two consecutive events in a pattern.
• Median: This constraint specifies the median value of an attribute across all events in a pattern.
• Span: This constraint specifies the difference between the maximum and the minimum value of an attribute across all events in a pattern.

Usage Examples

Examples on how to use the available constraints can be found in the Jupyter Notebook.

Installation

Seq2Pat can be installed from PyPI using pip install seq2pat. It can also be installed from source by following the instructions in our documentation.

Requirements

The library requires Python 3.6+, the Cython package, and a C++ compiler. See requirements.txt for dependencies.

Support

Please submit bug reports and feature requests as Issues.

License

Seq2Pat is licensed under the GNU GPL License 2.0.