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| import os | |
| from typing import List, Optional, Tuple, Any | |
| from collections import OrderedDict | |
| import pandas as pd | |
| from loguru import logger | |
| import pm4py | |
| import plotly.graph_objects as go | |
| import networkx as nx | |
| import matplotlib.pyplot as plt | |
| from PIL import Image | |
| from pydantic import BaseModel | |
| class ProcessMap(BaseModel): | |
| net: Any | |
| start_activities: List | None | |
| end_activities: List | None | |
| img: Any | None | |
| def dfg2networkx( dfg, start, end): | |
| """Dfg to networkx | |
| Argument | |
| dfg: a list of dict of edges from directly-follow-graph | |
| start: a dict of start activities | |
| end: a dict of end activities | |
| Return | |
| nx: networkx graph object | |
| """ | |
| PROCESS_START = '#Start#' | |
| PROCESS_END = '#End#' | |
| nodes = { PROCESS_START: 0, PROCESS_END: 1} | |
| node_idx = 2 | |
| for activity in start: | |
| assert activity not in nodes, f"#ERROR: {activity} exists" | |
| nodes[activity] = node_idx | |
| node_idx += 1 | |
| for activity in end: | |
| assert activity not in nodes, f"#ERROR: {activity} exists" | |
| nodes[activity] = node_idx | |
| node_idx += 1 | |
| for node in dfg: | |
| left_activity = node[0] | |
| if left_activity not in nodes: | |
| nodes[left_activity] = node_idx | |
| node_idx +=1 | |
| right_activity = node[1] | |
| if right_activity not in nodes: | |
| nodes[right_activity] = node_idx | |
| node_idx +=1 | |
| nodes = list(nodes.keys()) | |
| edges = [] | |
| for activity in start: | |
| from_id = str(PROCESS_START) | |
| to_id = str(activity) | |
| edges.append( ( PROCESS_START, activity) ) | |
| for activity in end: | |
| from_id = str(activity) | |
| to_id = str(PROCESS_END) | |
| edges.append( ( activity, PROCESS_END) ) | |
| for transition in dfg: | |
| edges.append( ( transition[0], transition[1]) ) | |
| nx_graph = nx.DiGraph() | |
| nx_graph.add_nodes_from( nodes) | |
| nx_graph.add_edges_from(edges) | |
| return nx_graph | |
| def discover_process_map_variants( df, top_k: int = 0, type: str = 'dfg'): | |
| """Discover process map from data frame (raw event log) | |
| Argument | |
| df: a pandas dataframe | |
| top_k: top k variants | |
| type: dfg or petri | |
| Return | |
| dfg, start_activities, end_activities | |
| """ | |
| event_log = pm4py.format_dataframe( df, case_id='case_id', activity_key='activity', timestamp_key='timestamp') | |
| if top_k > 0: | |
| event_log = pm4py.filter_variants_top_k( event_log, k = top_k) | |
| dfg, start_activities, end_activities = pm4py.discover_dfg(event_log) | |
| pm4py.view_dfg(dfg, start_activities=start_activities, end_activities=end_activities) | |
| return dfg, start_activities, end_activities | |
| def discover_process_map_activities_connections( df, activity_rank: int = 0, connection_rank: int = 0, state: dict = {}, type: str = 'dfg'): | |
| """Discover process map from data frame (raw event log) | |
| Argument | |
| df: a pandas dataframe | |
| top_k: top k variants | |
| type: dfg or petri | |
| Return | |
| dfg, start_activities, end_activities | |
| """ | |
| event_log = pm4py.format_dataframe( df, case_id='case_id', activity_key='activity', timestamp_key='timestamp') | |
| full_dfg, _, __ = pm4py.discover_dfg(event_log) | |
| ranked_connections = OrderedDict(sorted(full_dfg.items(), key=lambda item: item[1], reverse=True)) | |
| if activity_rank > 0: | |
| pass | |
| if connection_rank > 0: | |
| top_variant_connections = state.get('top_variant_connections', []) | |
| filtered_connections = list(ranked_connections.keys())[ : (connection_rank+ len(ranked_connections))] | |
| else: | |
| filtered_connections = list(ranked_connections.keys()) | |
| event_log = pm4py.filter_directly_follows_relation( event_log, relations = filtered_connections) | |
| dfg, start_activities, end_activities = pm4py.discover_dfg(event_log) | |
| pm4py.view_dfg(dfg, start_activities=start_activities, end_activities=end_activities) | |
| return dfg, start_activities, end_activities | |
| def discover_process_map( df: pd.DataFrame, type: str = 'dfg'): | |
| """ | |
| """ | |
| event_log = pm4py.format_dataframe( df, case_id='case_id', activity_key='activity', timestamp_key='timestamp') | |
| if type=='dfg': | |
| dfg, start_activities, end_activities = pm4py.discover_dfg(event_log) | |
| pm4py.view_dfg(dfg, start_activities=start_activities, end_activities=end_activities) | |
| return dfg, start_activities, end_activities | |
| elif type=='petrinet': | |
| net, im, fm = pm4py.discover_petri_net_inductive(event_log) | |
| pm4py.view_petri_net( petri_net=net, initial_marking=im, final_marking=fm) | |
| file_path = 'output/petri_net.png' | |
| pm4py.save_vis_petri_net( net, im, fm, file_path) | |
| img = Image.open(file_path) | |
| return net, img | |
| elif type=='bpmn': | |
| net = pm4py.discover_bpmn_inductive(event_log) | |
| pm4py.view_bpmn(net, format='png') | |
| file_path = 'output/bpmn.png' | |
| pm4py.save_vis_bpmn( net, file_path) | |
| img = Image.open(file_path) | |
| return net, img | |
| else: | |
| raise Exception(f"Invalid type: {type}") | |
| def view_networkx( nx_graph, layout): | |
| """ | |
| Argument | |
| nx_graph | |
| Return | |
| graph object | |
| fig.update_xaxes(showticklabels=False) | |
| fig.update_yaxes(showticklabels=False) | |
| """ | |
| # Create node scatter plot | |
| node_trace = go.Scatter( | |
| x=[layout[n][0] for n in nx_graph.nodes], | |
| y=[layout[n][1] for n in nx_graph.nodes], | |
| text=list(nx_graph.nodes), | |
| mode='markers+text', | |
| hovertext = [n for n in nx_graph.nodes], | |
| textposition='top center', | |
| marker=dict(size=20, color='LightSkyBlue', line=dict(width=2),opacity=0.5) | |
| ) | |
| # Create edge lines | |
| edge_trace = go.Scatter( | |
| x=(), | |
| y=(), | |
| line=dict(width=1.5, color='#888'), | |
| hoverinfo='none', | |
| mode='lines' | |
| ) | |
| # Add arrows for directed edges | |
| annotations = [] | |
| for edge in nx_graph.edges: | |
| x0, y0 = layout[edge[0]] | |
| x1, y1 = layout[edge[1]] | |
| edge_trace['x'] += (x0, x1, None) | |
| edge_trace['y'] += (y0, y1, None) | |
| # Calculate direction of the arrow | |
| annotations.append( | |
| dict( | |
| ax=x0, | |
| ay=y0, | |
| axref='x', | |
| ayref='y', | |
| x=x1, | |
| y=y1, | |
| xref='x', | |
| yref='y', | |
| showarrow=True, | |
| arrowhead=2, | |
| arrowsize=1, | |
| arrowwidth=5, # 增加箭头的宽度 | |
| arrowcolor='rgba(128, 128, 128, 0.5)' | |
| ) | |
| ) | |
| # Draw the figure | |
| fig = go.Figure(data=[edge_trace, node_trace], | |
| layout=go.Layout( | |
| showlegend=False, | |
| hovermode='closest', | |
| margin=dict(b=0, l=0, r=0, t=0), | |
| annotations=annotations, | |
| xaxis=dict(showgrid=False, zeroline=False), | |
| yaxis=dict(showgrid=False, zeroline=False) | |
| )) | |
| fig = fig.update_xaxes(showticklabels=False) | |
| fig = fig.update_yaxes(showticklabels=False) | |
| return fig | |
| def view_process_map( nx_graph, process_type: str = 'dfg', layout_type: str = 'sfdp'): | |
| """ | |
| """ | |
| layout = nx.nx_agraph.graphviz_layout( nx_graph, prog=layout_type) | |
| # min_x, max_x = min([ node_loc[0] for node, node_loc in layout.items()]), max([ node_loc[0] for node, node_loc in layout.items()]) | |
| # min_y, max_y = min([ node_loc[1] for node, node_loc in layout.items()]), max([ node_loc[1] for node, node_loc in layout.items()]) | |
| # layout['#Start#'] = ( min_x, min_y) | |
| # layout['#End#'] = (max_x, max_y) | |
| fig = view_networkx(nx_graph, layout) | |
| return fig |