Merge remote-tracking branch 'upstream/hotfixes' into release

Author: fit-alessandro-berti

docs/01_handling_event_data.md

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+Supported/Described Version(s): pm4py 2.7.11.11
+ 
+This documentation assumes that the reader has a basic understanding of process
+mining
+and python concepts.
+
+
+# Handling Event Data
+
+
+
+
+## Importing IEEE XES files
+
+
+IEEE XES is a standard format describing how event logs are stored.
+For more information about the format, please study the 
+IEEE XES Website (http://www.xes-standard.org)
+.
+A simple synthetic event log (
+running-example.xes
+) can be downloaded from 
+here (static/assets/examples/running-example.xes)
+.
+Note that several real event logs have been made available, over the past few
+years.
+You can find them 
+here (https://data.4tu.nl/search?q=:keyword:%20real%20life%20event%20logs)
+.
+
+ 
+ 
+The example code on the right shows how to import an event log, stored in the IEEE
+XES format, given a file path to the log file.
+The code fragment uses the standard importer (iterparse, described in a later
+paragraph).
+Note that IEEE XES Event Logs are imported into a Pandas dataframe object.
+
+
+```python
+import pm4py
+if __name__ == "__main__":
+	log = pm4py.read_xes('tests/input_data/running-example.xes')
+```
+
+
+
+
+## Importing CSV files
+
+
+Apart from the IEEE XES standard, a lot of event logs are actually stored in a 
+CSV
+file (https://en.wikipedia.org/wiki/Comma-separated_values)
+.
+In general, there is two ways to deal with CSV files in pm4py:
+,
+
+- Import the CSV into a 
+pandas (https://pandas.pydata.org)
+ 
+DataFrame (https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html#pandas.read_csv)
+;
+In general, most existing algorithms in pm4py are coded to be flexible in terms
+of their
+input, i.e., if a certain event log object is provided that is not in the right
+form, we
+translate it to the appropriate form for you.
+Hence, after importing a dataframe, most algorithms are directly able to work
+with the
+data frame.
+,
+
+- Convert the CSV into an event log object (similar to the result of the IEEE XES
+importer
+presented in the previous section);
+In this case, the first step is to import the CSV file using pandas (similar to
+the
+previous bullet) and subsequently converting it to the event log object.
+In the remainder of this section, we briefly highlight how to convert a pandas
+DataFrame
+to an event log.
+Note that most algorithms use the same type of conversion, in case a given
+event data
+object is not of the right type.
+ 
+ 
+The example code on the right shows how to convert a CSV file into the pm4py
+internal event data object types.
+By default, the converter converts the dataframe to an Event Log object (i.e., not
+an Event Stream).
+
+
+```python
+import pandas as pd
+import pm4py
+
+if __name__ == "__main__":
+	dataframe = pd.read_csv('tests/input_data/running-example.csv', sep=',')
+	dataframe = pm4py.format_dataframe(dataframe, case_id='case:concept:name', activity_key='concept:name', timestamp_key='time:timestamp')
+	event_log = pm4py.convert_to_event_log(dataframe)
+```
+
+
+Note that the example code above does not directly work in a lot of cases. Let us consider a very simple example event log, and, assume it is stored
+as a 
+`csv`,
+
+-file:
+
+|CaseID|Activity|Timestamp|clientID|
+|---|---|---|---|
+|1|register request|20200422T0455|1337|
+|2|register request|20200422T0457|1479|
+|1|submit payment|20200422T0503|1337|
+|||||
+In this small example table, we observe four columns, i.e., 
+`CaseID`
+,
+`Activity`
+,
+`Timestamp`
+ and 
+`clientID`
+.
+Clearly, when importing the data and converting it to an Event Log object, we aim to
+combine all rows (events) with the same value for the 
+`CaseID`
+ column
+together.
+Another interesting phenomenon in the example data is the fourth column, i.e.,
+`clientID`
+.
+In fact, the client ID is an attribute that will not change over the course of
+execution
+a process instance, i.e., it is a 
+case-level attribute
+.
+pm4py allows us to specify that a column actually describes a case-level attribute
+(under the assumption that the attribute does not change during the execution of a
+process).
+
+The example code on the right shows how to convert the previously examplified csv
+data file.
+After loading the csv file of the example table, we rename the 
+`clientID`
+column to 
+`case:clientID`
+ (this is a specific operation provided by
+pandas!).
+
+
+
+```python
+import pandas as pd
+import pm4py
+
+if __name__ == "__main__":
+	dataframe = pd.read_csv('tests/input_data/running-example-transformed.csv', sep=',')
+	dataframe = dataframe.rename(columns={'clientID': 'case:clientID'})
+	dataframe = pm4py.format_dataframe(dataframe, case_id='CaseID', activity_key='Activity', timestamp_key='Timestamp')
+	event_log = pm4py.convert_to_event_log(dataframe)
+```
+
+
+
+
+## Converting Event Data
+
+
+In this section, we describe how to convert event log objects from one object type
+to another object type.
+There are three objects, which we are able to 'switch' between, i.e., Event Log,
+Event Stream and Data Frame objects.
+Please refer to the previous code snippet for an example of applying log conversion
+(applied when importing a CSV object).
+Finally, note that most algorithms internally use the converters, in order to be
+able to handle an input event data object of any form.
+In such a case, the default parameters are used.
+To convert from any object to an event log, the following method can be used:
+
+
+```python
+import pm4py
+if __name__ == "__main__":
+	event_log = pm4py.convert_to_event_log(dataframe)
+```
+
+
+To convert from any object to an event stream, the following method can be used:
+
+
+```python
+import pm4py
+if __name__ == "__main__":
+	event_stream = pm4py.convert_to_event_stream(dataframe)
+```
+
+
+To convert from any object to a dataframe, the following method can be used:
+
+
+```python
+import pm4py
+if __name__ == "__main__":
+	dataframe = pm4py.convert_to_dataframe(dataframe)
+```
+
+
+
+
+## Exporting IEEE XES files
+
+
+Exporting an Event Log object to an IEEE Xes file is fairly straightforward in pm4py.
+Consider the example code fragment on the right, which depicts this
+functionality.
+
+
+```python
+import pm4py
+if __name__ == "__main__":
+	pm4py.write_xes(log, 'exported.xes')
+```
+
+
+In the example, the 
+`log`
+ object is assumed to be an Event Log object.
+The exporter also accepts an Event Stream or DataFrame object as an input.
+However, the exporter will first convert the given input object into an Event Log.
+Hence, in this case, standard parameters for the conversion are used.
+Thus, if the user wants more control, it is advisable to apply the conversion to
+Event Log, prior to exporting.
+
+
+
+## Exporting logs to CSV
+
+
+To export an event log to a 
+`csv`,
+
+-file, pm4py uses Pandas.
+Hence, an event log is first converted to a Pandas Data Frame, after which it is
+written to disk.
+
+
+
+```python
+import pandas as pd
+import pm4py
+
+if __name__ == "__main__":
+	dataframe = pm4py.convert_to_dataframe(log)
+	dataframe.to_csv('exported.csv')
+```
+
+
+ 
+In case an event log object is provided that is not a dataframe, i.e., an Event Log
+or Event Stream, the conversion is applied, using the default parameter values,
+i.e., as presented in the 
+Converting
+Event Data (#item-convert-logs)
+ section.
+Note that exporting event data to as csv file has no parameters.
+In case more control over the conversion is needed, please apply a conversion to
+dataframe first, prior to exporting to csv.
+
+
+
+## I/O with Other File Types
+
+
+At this moment, I/O of any format supported by Pandas (dataframes) is implicitly
+supported.
+As long as data can be loaded into a Pandas dataframe, pm4py is reasonably able to work
+with such files.