Różnice między wybraną wersją a wersją aktualną.
| Both sides previous revision Poprzednia wersja Nowa wersja | Poprzednia wersja | ||
|
pl:dydaktyka:dss:lab02 [2018/10/17 14:25] kkluza [Excercise] |
pl:dydaktyka:dss:lab02 [2020/10/18 19:42] (aktualna) kkluza [Excercise] |
||
|---|---|---|---|
| Linia 4: | Linia 4: | ||
| ===== Requirements ===== | ===== Requirements ===== | ||
| - | Python 3.x, opyenxes, pygraphviz. | + | Python 3.x, opyenxes, pygraphviz (or graphviz). |
| + | For this class you can use any Python environment available having the abovementioned libraries. \\ | ||
| + | It is also possible to use: https://colab.research.google.com. | ||
| + | |||
| + | The codes in this lab instruction are based on the codes from the book \\ | ||
| + | [[https://www.springer.com/gp/book/9783319564272|A Primer on Process Mining. Practical Skills with Python and Graphviz]]. \\ The codes are not optimized and they are supposed to show a step by step process mining solution. | ||
| ===== Implementing a simple heuristic miner ===== | ===== Implementing a simple heuristic miner ===== | ||
| - | Using the following excerpt of code import a ''repairExample.xes'' file into your Python script: | + | Using [[https://opyenxes.readthedocs.io/en/latest/_modules/opyenxes/data_in/XUniversalParser.html|XUniversalParser]] in the following excerpt of code, import a {{ :pl:dydaktyka:dss:lab:repairexample.txt |repairexample.xes}} file into your Python script: |
| <code python> | <code python> | ||
| Linia 72: | Linia 77: | ||
| | End | | | End | | ||
| + | ===== Visualizing results using Pygraphviz ===== | ||
| Using [[https://pygraphviz.github.io/|Pygraphviz]], we can render an image depicting the process: | Using [[https://pygraphviz.github.io/|Pygraphviz]], we can render an image depicting the process: | ||
| Linia 90: | Linia 96: | ||
| {{:pl:dydaktyka:dss:lab:simple_heuristic_net.png?550|}} | {{:pl:dydaktyka:dss:lab:simple_heuristic_net.png?550|}} | ||
| + | If you don't have pygraphviz, you can use graphviz ([[#graphviz_instead_of_pygraphviz|check instruction at the bottom of the page]]). | ||
| ===== Diagram enhancing ===== | ===== Diagram enhancing ===== | ||
| Linia 105: | Linia 112: | ||
| <code python> | <code python> | ||
| text = event + ' (' + str(ev_counter[event]) + ")" | text = event + ' (' + str(ev_counter[event]) + ")" | ||
| - | G.add_node(event, label=text, style="rounded,filled", fillcolor="#ffffcc") | + | G.add_node(event, label=text, style="rounded,filled", fillcolor="#ffffcc") # code for Pygraphviz |
| </code> | </code> | ||
| - | We can also change the transparency of the discovered tasks based on their frequencies: | + | We can also change the transparency of the discovered tasks based on their frequencies (code for Pygraphviz, so for graphviz, it should be adjusted): |
| <code python> | <code python> | ||
| Linia 153: | Linia 160: | ||
| {{:pl:dydaktyka:dss:lab:simple_heuristic_net_colors.png?570|}} | {{:pl:dydaktyka:dss:lab:simple_heuristic_net_colors.png?570|}} | ||
| + | ===== graphviz instead of pygraphviz ===== | ||
| + | |||
| + | It is possible to use graphviz instead of pygraphviz, but it has different syntax, e.g.: | ||
| + | |||
| + | <code python> | ||
| + | import graphviz | ||
| + | G = graphviz.Digraph() | ||
| + | for event in w_net: | ||
| + | G.node(event, style="rounded,filled", fillcolor="#ffffcc") | ||
| + | for preceding in w_net[event]: | ||
| + | G.edge(event, preceding) | ||
| + | |||
| + | G.graph_attr['rankdir'] = 'LR' | ||
| + | G.node_attr['shape'] = 'Mrecord' | ||
| + | G.edge_attr.update(penwidth='2') | ||
| + | G.node("End", shape="circle", label="") | ||
| + | G.render('simple_graphviz_graph') | ||
| + | display(G) | ||
| + | </code> | ||
| + | |||
| + | {{:pl:dydaktyka:dss:lab:graphviz-example.png?570|}} | ||
| ===== Excercise ===== | ===== Excercise ===== | ||
| Extend process discovery with additional features: | Extend process discovery with additional features: | ||
| - | * Try to discover frequency of each transition (flow) and render the number of occurrences both as a label and the thickness of the line. | + | - Try to discover the frequency of each transition (flow) and render the number of occurrences both as a label and the thickness of the line. |
| - | * Add some filtering option to show or hide tasks or flows according to the chosen threshold. | + | - Add some filtering option to show or hide tasks or flows according to the chosen threshold. |
| - | * 8-o Try to implement and discover relations according to the Alpha algorithm. | + | - Optimize code by avoiding creating additional lists, e.g. using ''itertools'', ''more_itertools'' or other Python tools. |
| + | - 8-o Only for interested students: Try to implement and discover relations according to the Alpha algorithm. | ||
| + | <fc #ff0000>There is no report required after this lab.</fc> However, it is possible to submit an additional report for 5 points (for a very good score) presenting the implementation of at least two of the above exercises. | ||
