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| ''' | |
| This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de). | |
| PM4Py is free software: you can redistribute it and/or modify | |
| it under the terms of the GNU General Public License as published by | |
| the Free Software Foundation, either version 3 of the License, or | |
| (at your option) any later version. | |
| PM4Py is distributed in the hope that it will be useful, | |
| but WITHOUT ANY WARRANTY; without even the implied warranty of | |
| MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the | |
| GNU General Public License for more details. | |
| You should have received a copy of the GNU General Public License | |
| along with PM4Py. If not, see <https://www.gnu.org/licenses/>. | |
| ''' | |
| from pm4py.algo.conformance.tokenreplay import algorithm as tr_algorithm | |
| from pm4py.util.colors import get_string_from_int_below_255 | |
| from collections import Counter | |
| from copy import copy | |
| import matplotlib as mpl | |
| import matplotlib.cm as cm | |
| import math | |
| from typing import Optional, Dict, Any, Union | |
| from pm4py.objects.log.obj import EventLog | |
| from pm4py.objects.petri_net.obj import PetriNet, Marking | |
| from pm4py.objects.conversion.log import converter as log_converter | |
| import pandas as pd | |
| def give_color_to_direction_dynamic(dir): | |
| """ | |
| Assigns a color to the direction (dynamic-defined colors) | |
| Parameters | |
| -------------- | |
| dir | |
| Direction | |
| Returns | |
| -------------- | |
| col | |
| Color | |
| """ | |
| dir = 0.5 + 0.5 * dir | |
| norm = mpl.colors.Normalize(vmin=0, vmax=1) | |
| nodes = [0.0, 0.01, 0.25, 0.4, 0.45, 0.55, 0.75, 0.99, 1.0] | |
| colors = ["deepskyblue", "skyblue", "lightcyan", "lightgray", "gray", "lightgray", "mistyrose", "salmon", "tomato"] | |
| cmap = mpl.colors.LinearSegmentedColormap.from_list("mycmap2", list(zip(nodes, colors))) | |
| #cmap = cm.plasma | |
| m = cm.ScalarMappable(norm=norm, cmap=cmap) | |
| rgba = m.to_rgba(dir) | |
| r = get_string_from_int_below_255(math.ceil(rgba[0] * 255.0)) | |
| g = get_string_from_int_below_255(math.ceil(rgba[1] * 255.0)) | |
| b = get_string_from_int_below_255(math.ceil(rgba[2] * 255.0)) | |
| return "#" + r + g + b | |
| def give_color_to_direction_static(dir): | |
| """ | |
| Assigns a color to the direction (static-defined colors) | |
| Parameters | |
| -------------- | |
| dir | |
| Direction | |
| Returns | |
| -------------- | |
| col | |
| Color | |
| """ | |
| direction_colors = [[-0.5, "#4444FF"], [-0.1, "#AAAAFF"], [0.0, "#CCCCCC"], [0.5, "#FFAAAA"], [1.0, "#FF4444"]] | |
| for col in direction_colors: | |
| if col[0] >= dir: | |
| return col[1] | |
| def compare_element_usage_two_logs(net: PetriNet, im: Marking, fm: Marking, log1: Union[EventLog, pd.DataFrame], log2: Union[EventLog, pd.DataFrame], parameters: Optional[Dict[Any, Any]] = None) -> Dict[Any, Any]: | |
| """ | |
| Returns some statistics (also visual) about the comparison of the usage | |
| of the elements in two logs given an accepting Petri net | |
| Parameters | |
| ------------- | |
| net | |
| Petri net | |
| im | |
| Initial marking | |
| fm | |
| Final marking | |
| log1 | |
| First log | |
| log2 | |
| Second log | |
| parameters | |
| Parameters of the algorithm (to be passed to the token-based replay) | |
| Returns | |
| ---------------- | |
| aggregated_statistics | |
| Statistics about the usage of places, transitions and arcs in the net | |
| """ | |
| if parameters is None: | |
| parameters = {} | |
| log1 = log_converter.apply(log1, variant=log_converter.Variants.TO_EVENT_LOG, parameters=parameters) | |
| log2 = log_converter.apply(log2, variant=log_converter.Variants.TO_EVENT_LOG, parameters=parameters) | |
| tr_parameters = copy(parameters) | |
| tr_parameters[tr_algorithm.Variants.TOKEN_REPLAY.value.Parameters.ENABLE_PLTR_FITNESS] = True | |
| rep_traces1, pl_fit_trace1, tr_fit_trace1, ne_act_model1 = tr_algorithm.apply(log1, net, im, fm, | |
| parameters=tr_parameters) | |
| rep_traces2, pl_fit_trace2, tr_fit_trace2, ne_act_model2 = tr_algorithm.apply(log2, net, im, fm, | |
| parameters=tr_parameters) | |
| tr_occ1 = Counter([y for x in rep_traces1 for y in x["activated_transitions"]]) | |
| tr_occ2 = Counter([y for x in rep_traces2 for y in x["activated_transitions"]]) | |
| pl_occ1 = Counter({p: pl_fit_trace1[p]["c"] + pl_fit_trace1[p]["r"] for p in pl_fit_trace1}) | |
| pl_occ2 = Counter({p: pl_fit_trace2[p]["c"] + pl_fit_trace2[p]["r"] for p in pl_fit_trace2}) | |
| all_replayed_transitions = set(tr_occ1.keys()).union(set(tr_occ2.keys())) | |
| all_replayed_places = set(pl_occ1.keys()).union(set(pl_occ2.keys())) | |
| all_transitions = all_replayed_transitions.union(set(net.transitions)) | |
| all_places = all_replayed_places.union(set(net.places)) | |
| aggregated_statistics = {} | |
| for place in all_places: | |
| aggregated_statistics[place] = {"log1_occ": pl_occ1[place], "log2_occ": pl_occ2[place], | |
| "total_occ": pl_occ1[place] + pl_occ2[place]} | |
| aggregated_statistics[place]["label"] = "(%d/%d/%d)" % ( | |
| pl_occ1[place], pl_occ2[place], pl_occ1[place] + pl_occ2[place]) | |
| dir = (pl_occ2[place] - pl_occ1[place]) / (pl_occ1[place] + pl_occ2[place]) if (pl_occ1[place] + pl_occ2[ | |
| place]) > 0 else 0 | |
| aggregated_statistics[place]["direction"] = dir | |
| aggregated_statistics[place]["color"] = give_color_to_direction_dynamic(dir) | |
| for trans in all_transitions: | |
| aggregated_statistics[trans] = {"log1_occ": tr_occ1[trans], "log2_occ": tr_occ2[trans], | |
| "total_occ": tr_occ1[trans] + tr_occ2[trans]} | |
| if trans.label is not None: | |
| aggregated_statistics[trans]["label"] = trans.label+" " | |
| else: | |
| aggregated_statistics[trans]["label"] = "" | |
| aggregated_statistics[trans]["label"] = aggregated_statistics[trans]["label"] + "(%d/%d/%d)" % ( | |
| tr_occ1[trans], tr_occ2[trans], tr_occ1[trans] + tr_occ2[trans]) | |
| dir = (tr_occ2[trans] - tr_occ1[trans]) / (tr_occ1[trans] + tr_occ2[trans]) if (tr_occ1[trans] + tr_occ2[ | |
| trans]) > 0 else 0 | |
| aggregated_statistics[trans]["direction"] = dir | |
| aggregated_statistics[trans]["color"] = give_color_to_direction_dynamic(dir) | |
| for arc in trans.in_arcs: | |
| aggregated_statistics[arc] = aggregated_statistics[trans] | |
| for arc in trans.out_arcs: | |
| aggregated_statistics[arc] = aggregated_statistics[trans] | |
| return aggregated_statistics | |