def execute_script():
log = xes_importer.apply(
os.path.join("..", "tests", "input_data", "running-example.xes"))
hw_values = sna_algorithm.apply(
log, variant=sna_algorithm.Variants.HANDOVER_LOG)
wt_values = sna_algorithm.apply(
log, variant=sna_algorithm.Variants.WORKING_TOGETHER_LOG)
sub_values = sna_algorithm.apply(
log, variant=sna_algorithm.Variants.SUBCONTRACTING_LOG)
ja_values = sna_algorithm.apply(
log, variant=sna_algorithm.Variants.JOINTACTIVITIES_LOG)
gviz_sub = pn_vis.apply(
sub_values,
variant=pn_vis.Variants.NETWORKX,
parameters={pn_vis.Variants.NETWORKX.value.Parameters.FORMAT: "svg"})
gviz_hw = pn_vis.apply(hw_values, variant=pn_vis.Variants.PYVIS)
gviz_wt = pn_vis.apply(
wt_values,
variant=pn_vis.Variants.NETWORKX,
parameters={pn_vis.Variants.NETWORKX.value.Parameters.FORMAT: "svg"})
gviz_ja = pn_vis.apply(ja_values, variant=pn_vis.Variants.PYVIS)
pn_vis.view(gviz_sub, variant=pn_vis.Variants.NETWORKX)
pn_vis.view(gviz_hw, variant=pn_vis.Variants.PYVIS)
pn_vis.view(gviz_wt, variant=pn_vis.Variants.NETWORKX)
pn_vis.view(gviz_ja, variant=pn_vis.Variants.PYVIS)
def discover_subcontracting_network(log: Union[EventLog, pd.DataFrame], n=2):
"""
Calculates the subcontracting network of the process.
Parameters
---------------
log
Event log or Pandas dataframe
n
n parameter for Subcontracting metric
Returns
---------------
metric_values
Values of the metric
"""
if type(log) not in [pd.DataFrame, EventLog, EventStream]: raise Exception("the method can be applied only to a traditional event log!")
from pm4py.algo.organizational_mining.sna import algorithm as sna
parameters = get_properties(log)
parameters["n"] = n
if check_is_pandas_dataframe(log):
check_pandas_dataframe_columns(log)
return sna.apply(log, variant=sna.Variants.SUBCONTRACTING_PANDAS, parameters=parameters)
else:
return sna.apply(log, variant=sna.Variants.SUBCONTRACTING_LOG, parameters=parameters)
def discover_handover_of_work_network(log: Union[EventLog, pd.DataFrame], beta=0):
"""
Calculates the handover of work network of the event log.
The handover of work network is essentially the DFG of the event log, however, using the
resource as a node of the graph, instead of the activity.
As such, to use this, resource information should be present in the event log.
Parameters
---------------
log
Event log or Pandas dataframe
beta
beta parameter for Handover metric
Returns
---------------
metric_values
Values of the metric
"""
if type(log) not in [pd.DataFrame, EventLog, EventStream]: raise Exception("the method can be applied only to a traditional event log!")
from pm4py.algo.organizational_mining.sna import algorithm as sna
parameters = get_properties(log)
parameters["beta"] = beta
if check_is_pandas_dataframe(log):
check_pandas_dataframe_columns(log)
return sna.apply(log, variant=sna.Variants.HANDOVER_PANDAS, parameters=parameters)
else:
return sna.apply(log, variant=sna.Variants.HANDOVER_LOG, parameters=parameters)
def discover_handover_of_work_network(log: Union[EventLog, pd.DataFrame], beta=0):
"""
Calculates the handover of work network of the event log.
The handover of work network is essentially the DFG of the event log, however, using the
resource as a node of the graph, instead of the activity.
As such, to use this, resource information should be present in the event log.
Parameters
---------------
log
Event log or Pandas dataframe
beta
beta parameter for Handover metric
Returns
---------------
metric_values
Values of the metric
"""
from pm4py.algo.organizational_mining.sna import algorithm as sna
if check_is_dataframe(log):
check_dataframe_columns(log)
return sna.apply(log, variant=sna.Variants.HANDOVER_PANDAS, parameters={"beta": beta})
else:
return sna.apply(log, variant=sna.Variants.HANDOVER_LOG, parameters={"beta": beta})
def test_pandas(self):
# to avoid static method warnings in tests,
# that by construction of the unittest package have to be expressed in such way
self.dummy_variable = "dummy_value"
log = pd.read_csv(
os.path.join("..", "tests", "input_data", "running-example.csv"))
log = dataframe_utils.convert_timestamp_columns_in_df(log)
hw_values = sna_alg.apply(log,
variant=sna_alg.Variants.HANDOVER_PANDAS)
wt_values = sna_alg.apply(
log, variant=sna_alg.Variants.WORKING_TOGETHER_PANDAS)
sub_values = sna_alg.apply(
log, variant=sna_alg.Variants.SUBCONTRACTING_PANDAS)
def test_1(self):
# to avoid static method warnings in tests,
# that by construction of the unittest package have to be expressed in such way
self.dummy_variable = "dummy_value"
log = xes_importer.apply(
os.path.join("..", "tests", "input_data", "running-example.xes"))
hw_values = sna_alg.apply(log, variant=sna_alg.Variants.HANDOVER_LOG)
wt_values = sna_alg.apply(
log, variant=sna_alg.Variants.WORKING_TOGETHER_LOG)
sub_values = sna_alg.apply(log,
variant=sna_alg.Variants.SUBCONTRACTING_LOG)
ja_values = sna_alg.apply(log,
variant=sna_alg.Variants.JOINTACTIVITIES_LOG)
def discover_activity_based_resource_similarity(log: Union[EventLog, pd.DataFrame]):
"""
Calculates similarity between the resources in the event log, based on their activity profiles.
Parameters
---------------
log
Event log or Pandas dataframe
Returns
---------------
metric_values
Values of the metric
"""
from pm4py.algo.organizational_mining.sna import algorithm as sna
if check_is_dataframe(log):
check_dataframe_columns(log)
return sna.apply(log, variant=sna.Variants.JOINTACTIVITIES_PANDAS)
else:
return sna.apply(log, variant=sna.Variants.JOINTACTIVITIES_LOG)
def discover_working_together_network(log: Union[EventLog, pd.DataFrame]):
"""
Calculates the working together network of the process.
Two nodes resources are connected in the graph if the resources collaborate on an instance of the process.
Parameters
---------------
log
Event log or Pandas dataframe
Returns
---------------
metric_values
Values of the metric
"""
from pm4py.algo.organizational_mining.sna import algorithm as sna
if check_is_dataframe(log):
check_dataframe_columns(log)
return sna.apply(log, variant=sna.Variants.WORKING_TOGETHER_PANDAS)
else:
return sna.apply(log, variant=sna.Variants.WORKING_TOGETHER_LOG)
def discover_activity_based_resource_similarity(log: Union[EventLog, pd.DataFrame]):
"""
Calculates similarity between the resources in the event log, based on their activity profiles.
Parameters
---------------
log
Event log or Pandas dataframe
Returns
---------------
metric_values
Values of the metric
"""
if type(log) not in [pd.DataFrame, EventLog, EventStream]: raise Exception("the method can be applied only to a traditional event log!")
from pm4py.algo.organizational_mining.sna import algorithm as sna
if check_is_pandas_dataframe(log):
check_pandas_dataframe_columns(log)
return sna.apply(log, variant=sna.Variants.JOINTACTIVITIES_PANDAS, parameters=get_properties(log))
else:
return sna.apply(log, variant=sna.Variants.JOINTACTIVITIES_LOG, parameters=get_properties(log))
def discover_subcontracting_network(log: Union[EventLog, pd.DataFrame], n=2):
"""
Calculates the subcontracting network of the process.
Parameters
---------------
log
Event log or Pandas dataframe
n
n parameter for Subcontracting metric
Returns
---------------
metric_values
Values of the metric
"""
from pm4py.algo.organizational_mining.sna import algorithm as sna
if check_is_dataframe(log):
check_dataframe_columns(log)
return sna.apply(log, variant=sna.Variants.SUBCONTRACTING_PANDAS, parameters={"n": n})
else:
return sna.apply(log, variant=sna.Variants.SUBCONTRACTING_LOG, parameters={"n": n})
def discover_working_together_network(log: Union[EventLog, pd.DataFrame]):
"""
Calculates the working together network of the process.
Two nodes resources are connected in the graph if the resources collaborate on an instance of the process.
Parameters
---------------
log
Event log or Pandas dataframe
Returns
---------------
metric_values
Values of the metric
"""
if type(log) not in [pd.DataFrame, EventLog, EventStream]: raise Exception("the method can be applied only to a traditional event log!")
from pm4py.algo.organizational_mining.sna import algorithm as sna
if check_is_pandas_dataframe(log):
check_pandas_dataframe_columns(log)
return sna.apply(log, variant=sna.Variants.WORKING_TOGETHER_PANDAS, parameters=get_properties(log))
else:
return sna.apply(log, variant=sna.Variants.WORKING_TOGETHER_LOG, parameters=get_properties(log))
def test_sna_clustering(self):
log = xes_importer.apply(
os.path.join("..", "tests", "input_data", "running-example.xes"))
hw_values = sna_alg.apply(log, variant=sna_alg.Variants.HANDOVER_LOG)
clusters = sna_util.cluster_affinity_propagation(hw_values)
