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

process-intelligence-solutions · Apr 12, 2024 · 104d2db · 104d2db
2 parents 55870f9 + 22609f4
commit 104d2db
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Showing 9 changed files with 244 additions and 196 deletions.
diff --git a/examples/timestamp_granularity.py b/examples/timestamp_granularity.py
@@ -12,17 +12,15 @@ def execute_script():
 
     # Here are some common options that you can use as a granularity:
     #
-    # 'D': Day
-    # 'H': Hour
-    # 'T' or 'min': Minute
-    # 'S': Second
-    # 'L' or 'ms': Millisecond
-    # 'U': Microsecond
-    # 'N': Nanosecond
+    # 'h': Hour
+    # 'min': Minute
+    # 's': Second
+    # 'ms': Millisecond
+    # 'ns': Nanosecond
 
     st = time.time_ns()
     # cast on the minute
-    dataframe["time:timestamp"] = dataframe["time:timestamp"].dt.floor('T')
+    dataframe["time:timestamp"] = dataframe["time:timestamp"].dt.floor(freq='min')
     ct = time.time_ns()
 
     print("required time for the timestamp casting: %.2f seconds" % ((ct-st)/10**9))

diff --git a/tests/dec_tree_test.py b/tests/dec_tree_test.py
@@ -1,5 +1,6 @@
 import os
 import unittest
+import importlib.util
 
 from pm4py.objects.log.importer.xes import importer as xes_importer
 from pm4py.objects.log.util import  get_class_representation
@@ -8,43 +9,45 @@
 
 class DecisionTreeTest(unittest.TestCase):
     def test_decisiontree_evattrvalue(self):
-        from pm4py.util import ml_utils
-        from pm4py.visualization.decisiontree import visualizer as dt_vis
-
-        # 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_path = os.path.join("input_data", "roadtraffic50traces.xes")
-        log = xes_importer.apply(log_path)
-        data, feature_names = log_to_features.apply(log, variant=log_to_features.Variants.TRACE_BASED,
-                                                    parameters={"str_tr_attr": [], "str_ev_attr": ["concept:name"],
-                                                                "num_tr_attr": [], "num_ev_attr": ["amount"]})
-        target, classes = get_class_representation.get_class_representation_by_str_ev_attr_value_value(log,
-                                                                                                       "concept:name")
-        clf = ml_utils.DecisionTreeClassifier(max_depth=7)
-        clf.fit(data, target)
-        gviz = dt_vis.apply(clf, feature_names, classes,
-                            parameters={dt_vis.Variants.CLASSIC.value.Parameters.FORMAT: "svg"})
-        del gviz
+        if importlib.util.find_spec("sklearn"):
+            from pm4py.util import ml_utils
+            from pm4py.visualization.decisiontree import visualizer as dt_vis
+
+            # 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_path = os.path.join("input_data", "roadtraffic50traces.xes")
+            log = xes_importer.apply(log_path)
+            data, feature_names = log_to_features.apply(log, variant=log_to_features.Variants.TRACE_BASED,
+                                                        parameters={"str_tr_attr": [], "str_ev_attr": ["concept:name"],
+                                                                    "num_tr_attr": [], "num_ev_attr": ["amount"]})
+            target, classes = get_class_representation.get_class_representation_by_str_ev_attr_value_value(log,
+                                                                                                           "concept:name")
+            clf = ml_utils.DecisionTreeClassifier(max_depth=7)
+            clf.fit(data, target)
+            gviz = dt_vis.apply(clf, feature_names, classes,
+                                parameters={dt_vis.Variants.CLASSIC.value.Parameters.FORMAT: "svg"})
+            del gviz
 
     def test_decisiontree_traceduration(self):
-        from pm4py.util import ml_utils
-        from pm4py.visualization.decisiontree import visualizer as dt_vis
-
-        # 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_path = os.path.join("input_data", "roadtraffic50traces.xes")
-        log = xes_importer.apply(log_path)
-        data, feature_names = log_to_features.apply(log, variant=log_to_features.Variants.TRACE_BASED,
-                                                    parameters={"str_tr_attr": [], "str_ev_attr": ["concept:name"],
-                                                                "num_tr_attr": [], "num_ev_attr": ["amount"]})
-        target, classes = get_class_representation.get_class_representation_by_trace_duration(log, 2 * 8640000)
-        clf = ml_utils.DecisionTreeClassifier(max_depth=7)
-        clf.fit(data, target)
-        gviz = dt_vis.apply(clf, feature_names, classes,
-                            parameters={dt_vis.Variants.CLASSIC.value.Parameters.FORMAT: "svg"})
-        del gviz
+        if importlib.util.find_spec("sklearn"):
+            from pm4py.util import ml_utils
+            from pm4py.visualization.decisiontree import visualizer as dt_vis
+
+            # 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_path = os.path.join("input_data", "roadtraffic50traces.xes")
+            log = xes_importer.apply(log_path)
+            data, feature_names = log_to_features.apply(log, variant=log_to_features.Variants.TRACE_BASED,
+                                                        parameters={"str_tr_attr": [], "str_ev_attr": ["concept:name"],
+                                                                    "num_tr_attr": [], "num_ev_attr": ["amount"]})
+            target, classes = get_class_representation.get_class_representation_by_trace_duration(log, 2 * 8640000)
+            clf = ml_utils.DecisionTreeClassifier(max_depth=7)
+            clf.fit(data, target)
+            gviz = dt_vis.apply(clf, feature_names, classes,
+                                parameters={dt_vis.Variants.CLASSIC.value.Parameters.FORMAT: "svg"})
+            del gviz
 
 
 if __name__ == "__main__":