mpdfg 0.0.3

Package for multi perspective DFG visualization

Multi Perspective DFG Visualizer

A package to visualize multi perspective Directly-Follows Graphs (DFG) based on event logs. It aims to provide insights into the relationships and dependencies between different perspectives in a given process. The diagrams are generated via Graphviz and Mermaid.

Installation

This package runs under Python 3.9+, use pip to install.

pip install mpdfg

To render and save generated diagrams, you will also need to install Graphviz

Quickstart

Format event log

Using mpdfg.log_formatter you can format your own initial event log with the corresponding column names, based on pm4py standard way of naming logs columns.

The format dictionary to pass as argument to this function needs to have the following structure:

{
    "case:concept:name": <Case Id>, # required
    "concept:name": <Activity Id>, # required
    "time:timestamp": <Timestamp>, # required
    "start_timestamp": <Start Timestamp>, # optional
    "org:resource": <Resource>, # optional
    "cost:total": <Cost>, # optional
}

Each value of the dictionary needs to match the corresponding column name of the initial event log. If start_timestamp, org:resource and cost:total are not present in your event log, you can leave its values as blank strings.

import mpdfg
import pandas as pd

raw_event_log = pd.read_csv("raw_event_log.csv")

format_dictionary = {
    "case:concept:name": "Case ID",
    "concept:name": "Activity",
    "time:timestamp": "Complete",
    "start_timestamp": "Start",
    "org:resource": "Resource",
    "cost:total": "Cost",
}

event_log = mpdfg.log_formatter(raw_event_log, format_dictionary)

Discover Multi Perspective DFG

(
    multi_perspective_dfg,
    start_activities,
    end_activities,
) = mpdfg.discover_multi_perspective_dfg(
    event_log,
    calculate_cost=True,
    calculate_frequency=True,
    calculate_time=True,
    frequency_statistic="absolute-activity", # or absolute-case, relative-activity, relative-case
    time_statistic="mean", # or sum, max, min, stdev, median
    cost_statistic="mean", # or sum, max, min, stdev, median
)

Get the DFG diagram string representation

mpdfg_string = mpdfg.get_multi_perspective_dfg_string(
    multi_perspective_dfg,
    start_activities,
    end_activities,
    visualize_frequency=True,
    visualize_time=True,
    visualize_cost=True,
    rankdir="TB", # or BT, LR, RL, etc.
    diagram_tool="graphviz", # or mermaid
)

View the generated DFG diagram

Allows the user to view the diagram in interactive Python environments like Jupyter and Google Colab.

mpdfg.view_multi_perspective_dfg(
    multi_perspective_dfg,
    start_activities,
    end_activities,
    file_path="diagram",
    visualize_frequency=True,
    visualize_time=True,
    visualize_cost=True,
    rankdir="TB", # or BT, LR, RL, etc.
    diagram_tool="graphviz", # only graphviz available for this function
)

Save the generated DFG diagram

mpdfg.save_vis_multi_perspective_dfg(
    multi_perspective_dfg,
    start_activities,
    end_activities,
    file_path="diagram",
    visualize_frequency=True,
    visualize_time=True,
    visualize_cost=True,
    format="png", # or pdf, webp, svg, etc.
    rankdir="TB", # or BT, LR, RL, etc.
    diagram_tool="graphviz", # or mermaid
)

Examples

Checkout Examples to see the package being used to visualize an event log of a mining process.