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| import os | |
| import unittest | |
| import importlib.util | |
| from pm4py.algo.organizational_mining.sna import algorithm as sna_alg, util as sna_util, util | |
| from pm4py.objects.log.importer.xes import importer as xes_importer | |
| from pm4py.util import constants, pandas_utils | |
| from pm4py.objects.log.util import dataframe_utils | |
| class SnaTests(unittest.TestCase): | |
| 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 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 = pandas_utils.read_csv(os.path.join("..", "tests", "input_data", "running-example.csv")) | |
| log = dataframe_utils.convert_timestamp_columns_in_df(log, timest_format=constants.DEFAULT_TIMESTAMP_PARSE_FORMAT) | |
| 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_log_orgmining_local_attr(self): | |
| from pm4py.algo.organizational_mining.local_diagnostics import algorithm | |
| log = xes_importer.apply(os.path.join("input_data", "receipt.xes")) | |
| algorithm.apply_from_group_attribute(log) | |
| def test_log_orgmining_local_clustering(self): | |
| if importlib.util.find_spec("sklearn"): | |
| from pm4py.algo.organizational_mining.local_diagnostics import algorithm | |
| from pm4py.algo.organizational_mining.sna.variants.log import jointactivities | |
| log = xes_importer.apply(os.path.join("input_data", "receipt.xes")) | |
| ja = jointactivities.apply(log) | |
| clustering = util.cluster_affinity_propagation(ja) | |
| algorithm.apply_from_clustering_or_roles(log, clustering) | |
| def test_log_orgmining_local_roles(self): | |
| from pm4py.algo.organizational_mining.local_diagnostics import algorithm | |
| from pm4py.algo.organizational_mining.roles import algorithm as roles_detection | |
| log = xes_importer.apply(os.path.join("input_data", "receipt.xes")) | |
| roles = roles_detection.apply(log) | |
| algorithm.apply_from_clustering_or_roles(log, roles) | |
| def test_sna_clustering(self): | |
| if importlib.util.find_spec("sklearn"): | |
| 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) | |
| def test_res_profiles_log(self): | |
| from pm4py.algo.organizational_mining.resource_profiles import algorithm | |
| log = xes_importer.apply(os.path.join("..", "tests", "input_data", "running-example.xes")) | |
| algorithm.distinct_activities(log, "2010-12-30 00:00:00", "2011-01-25 00:00:00", "Sara") | |
| algorithm.activity_frequency(log, "2010-12-30 00:00:00", "2011-01-25 00:00:00", "Sara", "decide") | |
| algorithm.activity_completions(log, "2010-12-30 00:00:00", "2011-01-25 00:00:00", "Sara") | |
| algorithm.case_completions(log, "2010-12-30 00:00:00", "2011-01-25 00:00:00", "Pete") | |
| algorithm.fraction_case_completions(log, "2010-12-30 00:00:00", "2011-01-25 00:00:00", "Pete") | |
| algorithm.average_workload(log, "2010-12-30 00:00:00", "2011-01-15 00:00:00", "Mike") | |
| algorithm.multitasking(log, "2010-12-30 00:00:00", "2011-01-25 00:00:00", "Mike") | |
| algorithm.average_duration_activity(log, "2010-12-30 00:00:00", "2011-01-25 00:00:00", "Sue", | |
| "examine thoroughly") | |
| algorithm.average_case_duration(log, "2010-12-30 00:00:00", "2011-01-25 00:00:00", "Sue") | |
| algorithm.interaction_two_resources(log, "2010-12-30 00:00:00", "2011-01-25 00:00:00", "Mike", "Pete") | |
| algorithm.social_position(log, "2010-12-30 00:00:00", "2011-01-25 00:00:00", "Sue") | |
| def test_res_profiles_df(self): | |
| from pm4py.algo.organizational_mining.resource_profiles import algorithm | |
| log = pandas_utils.read_csv(os.path.join("..", "tests", "input_data", "running-example.csv")) | |
| log = dataframe_utils.convert_timestamp_columns_in_df(log, timest_format=constants.DEFAULT_TIMESTAMP_PARSE_FORMAT) | |
| algorithm.distinct_activities(log, "2010-12-30 00:00:00", "2011-01-25 00:00:00", "Sara") | |
| algorithm.activity_frequency(log, "2010-12-30 00:00:00", "2011-01-25 00:00:00", "Sara", "decide") | |
| algorithm.activity_completions(log, "2010-12-30 00:00:00", "2011-01-25 00:00:00", "Sara") | |
| algorithm.case_completions(log, "2010-12-30 00:00:00", "2011-01-25 00:00:00", "Pete") | |
| algorithm.fraction_case_completions(log, "2010-12-30 00:00:00", "2011-01-25 00:00:00", "Pete") | |
| algorithm.average_workload(log, "2010-12-30 00:00:00", "2011-01-15 00:00:00", "Mike") | |
| algorithm.multitasking(log, "2010-12-30 00:00:00", "2011-01-25 00:00:00", "Mike") | |
| algorithm.average_duration_activity(log, "2010-12-30 00:00:00", "2011-01-25 00:00:00", "Sue", | |
| "examine thoroughly") | |
| algorithm.average_case_duration(log, "2010-12-30 00:00:00", "2011-01-25 00:00:00", "Sue") | |
| algorithm.interaction_two_resources(log, "2010-12-30 00:00:00", "2011-01-25 00:00:00", "Mike", "Pete") | |
| algorithm.social_position(log, "2010-12-30 00:00:00", "2011-01-25 00:00:00", "Sue") | |
| if __name__ == "__main__": | |
| unittest.main() | |