mpvis 0.0.1

Package for Multi-Perspective Process Visualization

mpvis

A Python package for Multi-Perspective Process Visualization of event logs

Index

• Installation
• Documentation
  • Multi-Perspective Directly-Follows Graph (Discovery / Visualization)
  • Multi-Dimensional Directed-Rooted Tree (Discovery / Visualization)
• Examples

Installation

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

pip install mpvis

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

Documentation

This package has two main modules:

• mpdfg to discover and visualize Multi-Perspective Directly-Follows Graphs (DFG)
• mddrt to discover and visualize Multi-Dimensional Directed-Rooted Trees (DRT)

Multi-Perspective Directly-Follows Graph (Discovery / Visualization)

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.

from mpvis 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,
    visualize_frequency=True,
    visualize_time=True,
    visualize_cost=True,
    rankdir="TB", # or BT, LR, RL, etc.
)

Save the generated DFG diagram

mpdfg.save_vis_multi_perspective_dfg(
    multi_perspective_dfg,
    start_activities,
    end_activities,
    file_name="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
)

Multi-Dimensional Directed-Rooted Tree (Discovery / Visualization)

Format event log

Using mddrt.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.

from mpvis import mddrt
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 = mddrt.log_formatter(raw_event_log, format_dictionary)

Manual log grouping of activities

Groups specified activities in a process log into a single activity group.

activities_to_group = ["A", "B", "C"]

manual_grouped_log = mddrt.manual_log_grouping(
    event_log=event_log, 
    activities_to_group=activities_to_group,
    group_name="Grouped Activities" # Optional
    )

Log pruning by number of variants

This function filters the event log to keep only the top k variants based on their frequency.Variants are different sequences of activities in the event log.

#k is the number of variants to keep
pruned_log_by_variants = mddrt.prune_log_based_on_top_variants(event_log, k=3) 

Discover Multi-Dimensional DRT

drt = mddrt.discover_multi_dimensional_drt(
    event_log,
    calculate_cost=True,
    calculate_time=True,
    calculate_flexibility=True,
    calculate_quality=True,
    group_activities=False,
)

Automatic group of activities

grouped_drt = mddrt.group_drt_activities(drt)

Tree pruning by depth

Prunes the tree to the specified maximum depth.

#k is the specified maximum depth 
mddrt.prune_tree_to_depth(drt, k=3) 

Get the DRT diagram string representation

mddrt_string = mpdfg.get_multi_dimension_drt_string(
    multi_dimensional_drt,
    visualize_time=True,
    visualize_cost=True,
    visualize_quality=True,
    visualize_flexibility=True
)

View the generated DRT diagram

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

mpdfg.view_multi_dimensional_drt(
    multi_dimensional_drt
    visualize_time=True,
    visualize_cost=True,
    visualize_quality=True,
    visualize_flexibility=True,
    node_measures=["total"], # accepts also "consumed" and "remaining"
    arc_measures=[], # accepts "avg", "min" and "max", or you can keep this argument empty
    format="svg" # Format value should be a valid image extension like 'jpg', 'png', 'jpeq' or 'webp
)

WARNING Not all output file formats of Graphviz are available to display in environments like Jupyter Notebook or Google Colab.

Save the generated DRT diagram

mpdfg.save_vis_multi_dimensional_drt(
    multi_dimensional_drt
    file_path="diagram",
    visualize_time=True,
    visualize_cost=True,
    visualize_quality=True,
    visualize_flexibility=True,
    node_measures=["total"], # accepts also "consumed" and "remaining"
    arc_measures=[], # accepts "avg", "min" and "max", or you can keep this argument empty
    format="svg", # or pdf, webp, svg, etc.
)

Examples

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