Commit Main branch

LICENSE

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+MIT License
+
+Copyright (c) 2023 lmu-dbs
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+# GEDI
+**G**enerating **E**vent **D**ata with **I**ntentional Features for Benchmarking Process Mining
+
+## Table of Contents
+
+- [Requirements](#requirements)
+- [Installation](#installation)
+- [Usage](#usage)
+- [References](#references)
+
+## Requirements
+- [Miniconda](https://docs.conda.io/en/latest/miniconda.html)
+- Graphviz on your OS e.g.
+For MacOS:
+```console
+brew install graphviz
+brew install swig
+```
+- For smac:
+```console
+conda install pyrfr swig
+```
+## Installation
+- `conda env create -f .conda.yml`
+- Install [Feature Extractor for Event Data (feeed)](https://github.com/lmu-dbs/feeed) in the newly installed conda environment: `pip install feeed`
+
+### Startup
+```console
+conda activate gedi
+python main.py -o config_files/options/baseline.json -a config_files/algorithm/experiment_test.json
+```
+## Usage
+Our pipeline offers several pipeline steps, which can be run sequentially or partially:
+- feature_extraction
+- generation
+- benchmark
+- evaluation_plotter
+
+We also include two notebooks, which output experimental results as in our paper.
+
+To run different steps of the GEDI pipeline, please adapt the `.json` accordingly.
+```console
+conda activate gedi
+python main.py -o config_files/options/baseline.json -a config_files/algorithm/<pipeline-step>.json
+```
+For reference of possible keys and values for each step, please see `config_files/algorithm/experiment_test.json`.
+To run the whole pipeline please create a new `.json` file, specifying all steps you want to run and specify desired keys and values for each step. 
+
+## References
+The framework used by `GEDI` is taken directly from the original paper by [Maldonado](mailto:[email protected]), Frey, Tavares, Rehwald and Seidl. If you would like to discuss the paper, or corresponding research questions on benchmarking process mining tasks please email the authors.

config.py

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+import json
+import os
+import warnings
+
+from gedi.utils.io_helpers import sort_files
+from tqdm import tqdm
+from utils.param_keys import INPUT_NAME, FILENAME, FOLDER_PATH, PARAMS
+
+def get_model_params_list(alg_json_file: str) :#-> list[dict]:
+    """
+    Loads the list of model configurations given from a json file or the default list of dictionary from the code.
+    @param alg_json_file: str
+        Path to the json data with the running configuration
+    @return: list[dict]
+        list of model configurations
+    """
+    if alg_json_file is not None:
+        return json.load(open(alg_json_file))
+    else:
+        warnings.warn('The default model parameter list is used instead of a .json-file.\n'
+                      '  Use a configuration from the `config_files`-folder together with the args `-a`.')
+        return [
+            {ALGORITHM_NAME: 'pca', NDIM: TENSOR_NDIM},
+            ]
+def get_run_params(alg_params_json: str) -> dict:
+    """
+    Loads the running configuration given from a json file or the default dictionary from the code.
+    @param alg_params_json: str
+        Path to the json data with the running configuration
+    @return: dict
+        Running Configuration
+    """
+    if alg_params_json is not None:
+        return json.load(open(alg_params_json))
+    else:
+        warnings.warn('The default run option is used instead of a .json-file.\n'
+                      '  Use a configuration from the `config_files`-folder together with the args `-o`.')
+        return {
+            RUN_OPTION: COMPARE,
+            PLOT_TYPE: COLOR_MAP,  # 'heat_map', 'color_map', '3d_map', 'explained_var_plot'
+            PLOT_TICS: True,
+            N_COMPONENTS: 2,
+            INPUT_NAME: 'runningExample',
+            SAVE_RESULTS: True,
+            LOAD_RESULTS: True
+        }
+
+def get_files_and_kwargs(params: dict):
+    """
+    This method returns the filename list of the trajectory and generates the kwargs for the DataTrajectory.
+    The method is individually created for the available data set.
+    Add new trajectory options, if different data set are used.
+    @param params: dict
+        running configuration
+    @return: tuple
+        list of filenames of the trajectories AND
+        kwargs with the important arguments for the classes
+    """
+    try:
+        input_name = params[INPUT_NAME]
+    except KeyError as e:
+        raise KeyError(f'Run option parameter is missing the key: `{e}`. This parameter is mandatory.')
+
+    #TODO: generate parent directories if they don't exist
+    if input_name == 'test':
+        filename_list = list(tqdm(sort_files(os.listdir('data/test'))))
+        kwargs = {FILENAME: filename_list, FOLDER_PATH: 'data/test'}
+    elif input_name == 'realLogs':
+        filename_list = list(tqdm(sort_files(os.listdir('data/real_event_logs'))))
+        kwargs = {FILENAME: filename_list, FOLDER_PATH: 'data/real_event_logs'}
+    elif input_name == 'gen5':
+        filename_list = list(tqdm(sort_files(os.listdir('data/event_log'))))[:5]
+        kwargs = {FILENAME: filename_list, FOLDER_PATH: 'data/event_log'}
+    elif input_name == 'gen20':
+        filename_list = list(tqdm(sort_files(os.listdir('data/event_log'))))[:20]
+        kwargs = {FILENAME: filename_list, FOLDER_PATH: 'data/event_log'}
+    elif input_name == 'runningExample':
+        filename_list = ['running-example.xes']
+        kwargs = {FILENAME: filename_list[0], FOLDER_PATH: 'data/'}
+    elif input_name == 'metaFeatures':
+        filename_list = ['log_features.csv']
+        kwargs = {FILENAME: filename_list[0], FOLDER_PATH: 'results/'}
+    else:
+        raise ValueError(f'No data trajectory was found with the name `{input_name}`.')
+
+    #filename_list.pop(file_element)
+    kwargs[PARAMS] = params
+    return filename_list, kwargs

config_files/algorithm/augmentation.json

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+[
+  {
+    "pipeline_step": "instance_augmentation",
+    "augmentation_params":
+    {
+        "method":"SMOTE", "no_samples":20,
+        "feature_selection": ["n_traces", "n_unique_traces", "ratio_unique_traces_per_trace", "trace_len_min", "trace_len_max", "trace_len_mean", "trace_len_median", "trace_len_mode", "trace_len_std", "trace_len_variance", "trace_len_q1", "trace_len_q3", "trace_len_iqr", "trace_len_geometric_mean", "trace_len_geometric_std", "trace_len_harmonic_mean", "trace_len_skewness", "trace_len_kurtosis", "trace_len_coefficient_variation", "trace_len_entropy", "trace_len_hist1", "trace_len_hist2", "trace_len_hist3", "trace_len_hist4", "trace_len_hist5", "trace_len_hist6", "trace_len_hist7", "trace_len_hist8", "trace_len_hist9", "trace_len_hist10", "trace_len_skewness_hist", "trace_len_kurtosis_hist", "ratio_most_common_variant", "ratio_top_1_variants", "ratio_top_5_variants", "ratio_top_10_variants", "ratio_top_20_variants", "ratio_top_50_variants", "ratio_top_75_variants", "mean_variant_occurrence", "std_variant_occurrence", "skewness_variant_occurrence", "kurtosis_variant_occurrence", "n_unique_activities", "activities_min", "activities_max", "activities_mean", "activities_median", "activities_std", "activities_variance", "activities_q1", "activities_q3", "activities_iqr", "activities_skewness", "activities_kurtosis", "n_unique_start_activities", "start_activities_min", "start_activities_max", "start_activities_mean", "start_activities_median", "start_activities_std", "start_activities_variance", "start_activities_q1", "start_activities_q3", "start_activities_iqr", "start_activities_skewness", "start_activities_kurtosis", "n_unique_end_activities", "end_activities_min", "end_activities_max", "end_activities_mean", "end_activities_median", "end_activities_std", "end_activities_variance", "end_activities_q1", "end_activities_q3", "end_activities_iqr", "end_activities_skewness", "end_activities_kurtosis", "entropy_trace", "entropy_prefix", "entropy_global_block", "entropy_lempel_ziv", "entropy_k_block_diff_1", "entropy_k_block_diff_3", "entropy_k_block_diff_5", "entropy_k_block_ratio_1", "entropy_k_block_ratio_3", "entropy_k_block_ratio_5", "entropy_knn_3", "entropy_knn_5", "entropy_knn_7", "epa_variant_entropy", "epa_normalized_variant_entropy", "epa_sequence_entropy", "epa_normalized_sequence_entropy", "epa_sequence_entropy_linear_forgetting", "epa_normalized_sequence_entropy_linear_forgetting", "epa_sequence_entropy_exponential_forgetting", "epa_normalized_sequence_entropy_exponential_forgetting"]
+    },
+    "input_path": "data/bpic_features.csv",
+    "output_path": "output"
+  }
+]

config_files/algorithm/benchmark.json

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+[
+  {
+    "pipeline_step": "benchmark_test",
+    "benchmark_test": "discovery",
+    "input_path":"data/test",
+    "output_path":"output",
+    "miners" : ["inductive", "heuristics", "imf", "ilp"]
+  }
+]

config_files/algorithm/evaluation_plotter.json

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+[
+    {
+        "pipeline_step": "evaluation_plotter",
+        "input_path": "output/features/generated/34_bpic_features/",
+        "input_path": "output/features/generated/grid_2obj/",
+        "input_path": ["output/features/generated/grid_1obj/", "output/features/generated/grid_2obj/"],
+        "input_path": "output/features/generated/grid_1obj/1_enve_feat.csv",
+        "input_path": "data/test/plotter/1_enve_feat.csv",
+        "reference_feature": "epa_normalized_sequence_entropy",
+        "reference_feature": "epa_normalized_sequence_entropy_exponential_forgetting",
+        "reference_feature": "epa_normalized_variant_entropy",
+        "targets": "data/34_bpic_features.csv",
+        "targets": "data/grid_experiments/grid_2obj/",
+        "targets": ["data/grid_experiments/grid_1obj/", "data/grid_experiments/grid_2obj/"],
+        "targets": "data/grid_experiments/grid_1obj/grid_1objectives_enve.csv",
+        "targets": "data/test/plotter/grid_1objectives_enve.csv",
+        "output_path": "output/plots"
+    }
+]

config_files/algorithm/experiment_test.json

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+[
+  {
+    "pipeline_step": "instance_augmentation",
+    "augmentation_params":{"method":"SMOTE", "no_samples":2,
+        "feature_selection": ["ratio_top_20_variants", "epa_normalized_sequence_entropy_linear_forgetting"]},
+    "input_path": "data/bpic_features.csv",
+    "output_path": "output"
+  },
+  {
+    "pipeline_step": "event_logs_generation",
+    "output_path": "output/features/2_bpic_features/2_ense_rmcv_feat.csv",
+    "output_path": "data/test",
+    "generator_params": {
+      "experiment": "data/grid_objectives.csv",
+      "experiment": {"input_path": "data/2_bpic_features.csv",
+        "objectives": ["ratio_top_20_variants", "epa_normalized_sequence_entropy_linear_forgetting"]},
+      "experiment": [
+          {"epa_normalized_sequence_entropy_linear_forgetting": 0.2, "ratio_top_20_variants": 0.4},
+          {"epa_normalized_sequence_entropy_linear_forgetting": 0.4, "ratio_top_20_variants": 0.7}
+        ],
+      "experiment": {"epa_normalized_sequence_entropy_linear_forgetting": 0.2, "ratio_top_20_variants": 0.4},
+      "config_space": {
+        "mode": [5, 20],
+        "sequence": [0.01, 1],
+        "choice": [0.01, 1],
+        "parallel": [0.01, 1],
+        "loop": [0.01, 1],
+        "silent": [0.01, 1],
+        "lt_dependency": [0.01, 1],
+        "num_traces": [10, 100],
+        "duplicate": [0],
+        "or": [0]
+      },
+      "n_trials": 2
+    }
+  },
+  {
+    "pipeline_step": "feature_extraction",
+    "input_path": "data/test",
+    "feature_params": {"feature_set":["trace_length"]},
+    "output_path": "output/plots",
+    "real_eventlog_path": "data/bpic_features.csv",
+    "plot_type": "boxplot"
+  },
+  {
+    "pipeline_step": "benchmark_test",
+    "benchmark_test": "discovery",
+    "input_path":"data/test",
+    "output_path":"output",
+    "miners" : ["inductive", "heuristics", "imf", "ilp"]
+  }
+]

config_files/algorithm/feature_extraction.json

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+[
+  {
+    "pipeline_step": "feature_extraction",
+    "input_path": "data/test",
+    "feature_params": {"feature_set":["simple_stats", "trace_length", "trace_variant", "activities", "start_activities", "end_activities", "eventropies", "epa_based"]},
+    "output_path": "output/plots",
+    "real_eventlog_path": "data/BaselineED_feat.csv",
+    "plot_type": "boxplot",
+    "font_size": 24,
+    "boxplot_width":10
+  }
+]

config_files/algorithm/fix_24.json

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+[
+  {
+    "pipeline_step": "event_logs_generation",
+    "output_path":"data/generated",
+    "generator_params": {
+      "objectives": {
+      "normalized_sequence_entropy_linear_forgetting": 0.05,
+      "ratio_top_20_variants": 0.4
+      },
+      "config_space": {
+        "mode": [5, 40],
+        "sequence": [0.01, 1],
+        "choice": [0.01, 1],
+        "parallel": [0.01, 1],
+        "loop": [0.01, 1],
+        "silent": [0.01, 1],
+        "lt_dependency": [0.01, 1],
+        "num_traces": [100, 1001],
+        "duplicate": [0],
+        "or": [0]
+      },
+      "n_trials": 20
+    }
+  },  
+  {
+    "pipeline_step": "feature_extraction",
+    "input_path": "data/generated",
+    "feature_params": {"feature_set":["simple_stats", "trace_length", "trace_variant", "activities", "start_activities", "end_activities", "entropies", "complexity"]},
+    "feature_params": {"feature_set":["trace_length"]},
+    "output_path": "output/plots",
+    "real_eventlog_path": "data/log_meta_features.csv",
+    "plot_type": "boxplot"
+  }
+]

config_files/algorithm/generation.json

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+[
+  {
+    "pipeline_step": "event_logs_generation",
+    "output_path": "output",
+    "generator_params": {
+      "experiment": {"input_path": "data/test/grid_feat.csv",
+        "objectives": ["ratio_top_20_variants", "epa_normalized_sequence_entropy_linear_forgetting"]
+      },
+      "config_space": {
+        "mode": [5, 20],
+        "sequence": [0.01, 1],
+        "choice": [0.01, 1],
+        "parallel": [0.01, 1],
+        "loop": [0.01, 1],
+        "silent": [0.01, 1],
+        "lt_dependency": [0.01, 1],
+        "num_traces": [10, 10001],
+        "duplicate": [0],
+        "or": [0]
+      },
+      "n_trials": 50,
+      "plot_reference_feature": "epa_normalized_sequence_entropy",
+      "plot_reference_feature": "epa_normalized_sequence_entropy_linear_forgetting",
+      "plot_reference_feature": ""
+    }
+  }
+]

config_files/algorithm/test/generator_2bpic_2objectives_ense_enseef.json

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+[{"pipeline_step": "event_logs_generation",
+"output_path": "output/generated",
+"generator_params": {"experiment":
+	{"input_path": "data/2_bpic_features.csv",
+	"objectives": ["epa_normalized_sequence_entropy",
+	"epa_normalized_sequence_entropy_exponential_forgetting"]},
+	"config_space": {"mode": [5, 20], "sequence": [0.01, 1],
+	"choice": [0.01, 1], "parallel": [0.01, 1], "loop": [0.01, 1],
+	"silent": [0.01, 1], "lt_dependency": [0.01, 1], "num_traces": [10, 10001],
+	"duplicate": [0], "or": [0]}, "n_trials": 2}},
+	{"pipeline_step": "feature_extraction",
+	"input_path": "output/features/generated/2_bpic_features/2_ense_enseef",
+	"feature_params": {"feature_set": ["simple_stats", "trace_length", "trace_variant", "activities",
+	"start_activities", "end_activities", "eventropies", "epa_based"]}, "output_path": "output/plots",
+	"real_eventlog_path": "data/2_bpic_features.csv", "plot_type": "boxplot"}]

config_files/algorithm/test/generator_grid_1objectives_rt10v.json

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+[{"pipeline_step": "event_logs_generation",
+"output_path": "output/generated/grid_1obj",
+"generator_params": {"experiment":
+	{"input_path": "data/grid_experiments/grid_1objectives_rt10v.csv",
+	"objectives": ["ratio_top_10_variants"]},
+	"config_space": {"mode": [5, 20], "sequence": [0.01, 1],
+	"choice": [0.01, 1], "parallel": [0.01, 1], "loop": [0.01, 1],
+	"silent": [0.01, 1], "lt_dependency": [0.01, 1],
+	"num_traces": [10, 10001], "duplicate": [0],
+	"or": [0]}, "n_trials": 2}},
+ {"pipeline_step": "feature_extraction",
+ "input_path": "output/features/generated/grid_1obj/grid_1objectives_rt10v/1_rt10v",
+ "feature_params": {"feature_set": ["simple_stats", "trace_length", "trace_variant",
+ "activities", "start_activities", "end_activities", "eventropies", "epa_based"]},
+ "output_path": "output/plots", "real_eventlog_path": "data/2_bpic_features.csv",
+ "plot_type": "boxplot"}]

config_files/algorithm/test/generator_grid_2objectives_ense_enself.json

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+[{"pipeline_step": "event_logs_generation",
+"output_path": "output/generated/grid_2obj",
+"generator_params": {"experiment":
+	{"input_path": "data/2_grid_test.csv",
+	"objectives": ["epa_normalized_sequence_entropy",
+		"epa_normalized_sequence_entropy_linear_forgetting"]},
+	"config_space": {"mode": [5, 20], "sequence": [0.01, 1],
+	"choice": [0.01, 1], "parallel": [0.01, 1], "loop": [0.01, 1],
+	"silent": [0.01, 1], "lt_dependency": [0.01, 1],
+	"num_traces": [10, 10001], "duplicate": [0], "or": [0]}, "n_trials": 3}},
+	{"pipeline_step": "feature_extraction",
+	"input_path": "output/features/generated/2_grid_test/2_ense_enself",
+	"feature_params": {"feature_set": ["epa_normalized_sequence_entropy",
+	"epa_normalized_sequence_entropy_linear_forgetting"]},
+	"feature_params": {"feature_set": ["simple_stats", "trace_length", "trace_variant",
+		"activities", "start_activities", "end_activities", "eventropies", "epa_based"]},
+	"output_path": "output/plots",
+	"real_eventlog_path": "data/2_bpic_features.csv",
+	"plot_type": "boxplot"}]

config_files/options/baseline.json

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+{
+  "run_option": "baseline",
+  "plot_type": "color_map",
+  "plot_tics": true,
+  "n_components": 2,
+  "input_name": "test",
+  "save_results": false,
+  "load_results": false
+}

config_files/options/run_params.json

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+{
+  "run_option": "compare",
+  "plot_type": "color_map",
+  "plot_tics": true,
+  "n_components": 2,
+  "input_name": "gen20",
+  "save_results": false,
+  "load_results": true
+}

dashboard.py

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+from copy import deepcopy
+from meta_feature_extraction.simple_stats import simple_stats
+from meta_feature_extraction.trace_length import trace_length
+from meta_feature_extraction.trace_variant import trace_variant
+from meta_feature_extraction.activities import activities
+from meta_feature_extraction.start_activities import start_activities
+from meta_feature_extraction.end_activities import end_activities
+from meta_feature_extraction.entropies import entropies
+from pm4py import discover_petri_net_inductive as inductive_miner
+from pm4py import generate_process_tree
+from pm4py import save_vis_petri_net, save_vis_process_tree
+from pm4py.algo.filtering.log.variants import variants_filter
+from pm4py.algo.simulation.tree_generator import algorithm as tree_generator
+from pm4py.algo.simulation.playout.process_tree import algorithm as playout
+from pm4py.objects.conversion.log import converter as log_converter
+from pm4py.objects.log.exporter.xes import exporter as xes_exporter
+from pm4py.objects.log.importer.xes import importer as xes_importer
+from pm4py.objects.log.util import dataframe_utils
+from pm4py.sim import play_out
+
+import matplotlib.image as mpimg
+import os
+import pandas as pd
+import streamlit as st
+
+OUTPUT_PATH = "output"
+SAMPLE_EVENTS = 500
+
+@st.cache(allow_output_mutation=True)
+def load_from_xes(uploaded_file):
+    bytes_data = uploaded_file.getvalue()
+    log1 = xes_importer.deserialize(bytes_data)
+    get_stats(log1)
+    return log1
+
+@st.cache
+def load_from_csv(uploaded_file, sep):
+    if uploaded_file is not None:
+        df = pd.read_csv(uploaded_file, sep=sep, index_col=False)
+        return df
+
+def get_stats(log, save=True):
+    """Returns the statistics of an event log."""
+    num_traces = len(log)
+    num_events = sum([len(c) for c in log])
+    num_utraces = len(variants_filter.get_variants(log))
+    if save:
+        st.session_state["num_traces"] = num_traces
+        st.session_state["num_events"] = num_events
+        st.session_state["num_utraces"] = num_utraces
+    return num_utraces, num_traces, num_events
+
+#@st.cache
+def df_to_log(df, case_id, activity, timestamp):
+    df.rename(columns={case_id: 'case:concept:name',
+                       activity: 'concept:name',
+                       timestamp: "time:timestamp"}, inplace=True)
+    temp = dataframe_utils.convert_timestamp_columns_in_df(df)
+    #temp = temp.sort_values(timestamp)
+    log = log_converter.apply(temp)
+    return log, 'concept:name', "time:timestamp"
+
+def read_uploaded_file(uploaded_file):
+    extension = uploaded_file.name.split('.')[-1]
+    log_name = uploaded_file.name.split('.')[-2]
+
+    st.sidebar.write("Loaded ", extension.upper(), '-File: ', uploaded_file.name)
+    if extension == "xes":
+        event_log = load_from_xes(uploaded_file)
+        log_columns = [*list(event_log[0][0].keys())]
+        convert_button = False
+        case_id = "case:concept:name"
+        activity = "concept:name"
+        timestamp = "time:timestamp"
+        default_act_id = log_columns.index("concept:name")
+        default_tst_id = log_columns.index("time:timestamp")
+
+        event_df = log_converter.apply(event_log, variant=log_converter.Variants.TO_DATA_FRAME)
+        df_path = OUTPUT_PATH+"/"+log_name+".csv"
+        event_df.to_csv(df_path, sep =";", index=False)
+        return event_log, event_df, case_id, activity
+
+    elif extension == "csv":
+        sep = st.sidebar.text_input("Columns separator", ";")
+        event_df = load_from_csv(uploaded_file, sep)
+        old_df = deepcopy(event_df)
+        log_columns = event_df.columns
+
+        case_id = st.sidebar.selectbox("Choose 'case' column:", log_columns)
+        activity = st.sidebar.selectbox("Choose 'activity' column:", log_columns, index=0)
+        timestamp = st.sidebar.selectbox("Choose 'timestamp' column:", log_columns, index=0)
+
+        convert_button = st.sidebar.button('Confirm selection')
+        if convert_button:
+            temp = deepcopy(event_df)
+            event_log, activity, timestamp = df_to_log(temp, case_id, activity, timestamp)
+            #xes_exporter.apply(event_log, INPUT_XES)
+            log_columns = [*list(event_log[0][0].keys())]
+            st.session_state['log'] = event_log
+            return event_log, event_df, case_id, activity
+
+def sample_log_traces(complete_log, sample_size):
+    '''
+    Samples random traces out of logs.
+    So that number of events is slightly over SAMPLE_SIZE.
+    :param complete_log: Log extracted from xes
+    '''
+
+    log_traces = variants_filter.get_variants(complete_log)
+    keys = list(log_traces.keys())
+    sample_traces = {}
+    num_evs = 0
+    while num_evs < sample_size:
+        if len(keys) == 0:
+            break
+        random_trace = keys.pop()
+        sample_traces[random_trace] = log_traces[random_trace]
+        evs = sum([len(case_id) for case_id in sample_traces[random_trace]])
+        num_evs += evs
+    log1 = variants_filter.apply(complete_log, sample_traces)
+    return log1
+
+def show_process_petrinet(event_log, filter_info, OUTPUT_PATH):
+            OUTPUT_PLOT = f"{OUTPUT_PATH}_{filter_info}".replace(":","").replace(".","")+".png" # OUTPUT_PATH is OUTPUT_PATH+INPUT_FILE
+
+            try:
+                fig_pt = mpimg.imread(OUTPUT_PLOT)
+                st.write("Loaded from memory")
+            except FileNotFoundError:
+                net, im, fm = inductive_miner(event_log)
+                           # parameters={heuristics_miner.Variants.CLASSIC.value.Parameters.DEPENDENCY_THRESH: 0.99,
+                           #     pn_visualizer.Variants.FREQUENCY.value.Parameters.FORMAT: "png"})
+                #parameters = {pn_visualizer.Variants.FREQUENCY.value.Parameters.FORMAT: "png"}
+                save_vis_petri_net(net, im, fm, OUTPUT_PLOT)
+                st.write("Saved in: ", OUTPUT_PLOT)
+            fig_pt = mpimg.imread(OUTPUT_PLOT)
+            st.image(fig_pt)
+
+def show_loaded_event_log(event_log, event_df):
+        get_stats(event_log)
+        st.write("### Loaded event-log")
+        col1, col2 = st.columns(2)
+        with col2:
+            st.dataframe(event_df)
+        with col1:
+            show_process_petrinet(event_log, None, OUTPUT_PATH+"running-example")
+
+def extract_meta_features(log, log_name):
+    mtf_cols = ["log", "n_traces", "n_unique_traces", "ratio_unique_traces_per_trace", "n_events", "trace_len_min", "trace_len_max",
+                "trace_len_mean", "trace_len_median", "trace_len_mode", "trace_len_std", "trace_len_variance", "trace_len_q1",
+                "trace_len_q3", "trace_len_iqr", "trace_len_geometric_mean", "trace_len_geometric_std", "trace_len_harmonic_mean",
+                "trace_len_skewness", "trace_len_kurtosis", "trace_len_coefficient_variation", "trace_len_entropy", "trace_len_hist1",
+                "trace_len_hist2", "trace_len_hist3", "trace_len_hist4", "trace_len_hist5", "trace_len_hist6", "trace_len_hist7",
+                "trace_len_hist8", "trace_len_hist9", "trace_len_hist10", "trace_len_skewness_hist", "trace_len_kurtosis_hist",
+                "ratio_most_common_variant", "ratio_top_1_variants", "ratio_top_5_variants", "ratio_top_10_variants", "ratio_top_20_variants",
+                "ratio_top_50_variants", "ratio_top_75_variants", "mean_variant_occurrence", "std_variant_occurrence", "skewness_variant_occurrence",
+                "kurtosis_variant_occurrence", "n_unique_activities", "activities_min", "activities_max", "activities_mean", "activities_median",
+                "activities_std", "activities_variance", "activities_q1", "activities_q3", "activities_iqr", "activities_skewness",
+                "activities_kurtosis", "n_unique_start_activities", "start_activities_min", "start_activities_max", "start_activities_mean",
+                "start_activities_median", "start_activities_std", "start_activities_variance", "start_activities_q1", "start_activities_q3",
+                "start_activities_iqr", "start_activities_skewness", "start_activities_kurtosis", "n_unique_end_activities", "end_activities_min",
+                "end_activities_max", "end_activities_mean", "end_activities_median", "end_activities_std", "end_activities_variance",
+                "end_activities_q1", "end_activities_q3", "end_activities_iqr", "end_activities_skewness", "end_activities_kurtosis", "entropy_trace",
+                "entropy_prefix", "entropy_global_block", "entropy_lempel_ziv", "entropy_k_block_diff_1", "entropy_k_block_diff_3",
+                "entropy_k_block_diff_5", "entropy_k_block_ratio_1", "entropy_k_block_ratio_3", "entropy_k_block_ratio_5", "entropy_knn_3",
+                "entropy_knn_5", "entropy_knn_7"]
+    features = [log_name]
+    features.extend(simple_stats(log))
+    features.extend(trace_length(log))
+    features.extend(trace_variant(log))
+    features.extend(activities(log))
+    features.extend(start_activities(log))
+    features.extend(end_activities(log))
+    features.extend(entropies(log_name, OUTPUT_PATH))
+
+    mtf = pd.DataFrame([features], columns=mtf_cols)
+
+    st.dataframe(mtf)
+    return mtf
+
+def generate_pt(mtf):
+    OUTPUT_PLOT = f"{OUTPUT_PATH}/generated_pt".replace(":","").replace(".","")#+".png" # OUTPUT_PATH is OUTPUT_PATH+INPUT_FILE
+
+    st.write("### PT Gen configurations")
+    col1, col2, col3, col4, col5, col6 = st.columns(6)
+    with col1:
+            param_mode = st.text_input('Mode', str(round(mtf['activities_median'].iat[0]))) #?
+            st.write("Sum of probabilities must be one")
+    with col2:
+            param_min = st.text_input('Min', str(mtf['activities_min'].iat[0]))
+            param_seq = st.text_input('Probability Sequence', 0.25)
+    with col3:
+            param_max = st.text_input('Max', str(mtf['activities_max'].iat[0]))
+            param_cho = st.text_input('Probability Choice (XOR)', 0.25)
+    with col4:
+            param_nmo = st.text_input('Number of models', 1)
+            param_par = st.text_input('Probability Parallel', 0.25)
+    with col5:
+            param_dup = st.text_input('Duplicates', 0)
+            param_lop = st.text_input('Probability Loop', 0.25)
+    with col6:
+            param_sil = st.text_input('Silent', 0.2)
+            param_or = st.text_input('Probability Or', 0.0)
+
+    PT_PARAMS = {tree_generator.Variants.PTANDLOGGENERATOR.value.Parameters.MODE: round(float(param_mode)), #most frequent number of visible activities
+            tree_generator.Variants.PTANDLOGGENERATOR.value.Parameters.MIN: int(param_min), #minimum number of visible activities
+            tree_generator.Variants.PTANDLOGGENERATOR.value.Parameters.MAX: int(param_max), #maximum number of visible activities
+            tree_generator.Variants.PTANDLOGGENERATOR.value.Parameters.SEQUENCE: float(param_seq), #probability to add a sequence operator to tree
+            tree_generator.Variants.PTANDLOGGENERATOR.value.Parameters.CHOICE: float(param_cho), #probability to add a choice (XOR) operator to tree
+            tree_generator.Variants.PTANDLOGGENERATOR.value.Parameters.PARALLEL: float(param_par), #probability to add a parallel operator to tree
+            tree_generator.Variants.PTANDLOGGENERATOR.value.Parameters.LOOP: float(param_lop), #probability to add a loop operator to tree
+            tree_generator.Variants.PTANDLOGGENERATOR.value.Parameters.OR: float(param_or), #probability to add an or operator to tree
+            tree_generator.Variants.PTANDLOGGENERATOR.value.Parameters.SILENT: float(param_sil), #probability to add silent activity to a choice or loop operator
+            tree_generator.Variants.PTANDLOGGENERATOR.value.Parameters.DUPLICATE: int(param_dup), #probability to duplicate an activity label
+            tree_generator.Variants.PTANDLOGGENERATOR.value.Parameters.NO_MODELS: int(param_nmo)} #number of trees to generate from model population
+
+    process_tree = generate_process_tree(parameters=PT_PARAMS)
+    save_vis_process_tree(process_tree, OUTPUT_PLOT+"_tree.png")
+
+    st.write("### Playout configurations")
+
+    param_ntraces = st.text_input('Number of traces', str(mtf['n_traces'].iat[0]))
+    PO_PARAMS = {playout.Variants.BASIC_PLAYOUT.value.Parameters.NO_TRACES : int(param_ntraces)}
+
+    ptgen_log = play_out(process_tree, parameters=PO_PARAMS)
+
+    net, im, fm = inductive_miner(ptgen_log)
+    save_vis_petri_net(net, im, fm, OUTPUT_PLOT+".png")
+    st.write("Saved in: ", OUTPUT_PLOT)
+    fig_pt_net = mpimg.imread(OUTPUT_PLOT+".png")
+    fig_pt_tree = mpimg.imread(OUTPUT_PLOT+"_tree.png")
+
+    fcol1, fcol2 = st.columns(2)
+    with fcol1:
+        st.image(fig_pt_tree)
+    with fcol2:
+        st.image(fig_pt_net)
+    extract_meta_features(ptgen_log, "gen_pt")
+
+
+if __name__ == '__main__':
+    st.set_page_config(layout='wide')
+    """
+    # Event Log Generator
+    """
+    start_options =  ['Event-Log', 'Meta-features']
+    start_preference = st.sidebar.selectbox("Do you want to start with a log or with metafeatures?", start_options,0)
+    #lets_start = st.sidebar.button("Let's start with "+start_preference+'!')
+
+    if start_preference==start_options[0]:
+        st.sidebar.write("Upload a dataset in csv or xes-format:")
+        uploaded_file = st.sidebar.file_uploader("Pick a logfile")
+
+        bar = st.progress(0)
+
+        os.makedirs(OUTPUT_PATH, exist_ok=True)
+        event_log = st.session_state['log'] if "log" in st.session_state else None
+        if uploaded_file:
+            event_log, event_df, case_id, activity_id = read_uploaded_file(uploaded_file)
+            #event_log = deepcopy(event_log)
+
+            use_sample = st.sidebar.checkbox('Use random sample', True)
+            if use_sample:
+                sample_size = st.sidebar.text_input('Sample size of approx number of events', str(SAMPLE_EVENTS))
+                sample_size = int(sample_size)
+
+                event_log = sample_log_traces(event_log, sample_size)
+                sample_cases = [event_log[i].attributes['concept:name'] for i in range(0, len(event_log))]
+                event_df = event_df[event_df[case_id].isin(sample_cases)]
+
+            show_loaded_event_log(event_log, event_df)
+            ext_mtf = extract_meta_features(event_log, "running-example")
+            generate_pt(ext_mtf)
+
+    elif start_preference==start_options[1]:
+        LOG_COL = 'log'
+        st.sidebar.write("Upload a dataset in csv-format")
+        uploaded_file = st.sidebar.file_uploader("Pick a file containing meta-features")
+
+        bar = st.progress(0)
+
+        os.makedirs(OUTPUT_PATH, exist_ok=True)
+        event_log = st.session_state[LOG_COL] if "log" in st.session_state else None
+        if uploaded_file:
+            sep = st.sidebar.text_input("Columns separator", ";")
+            mtf = load_from_csv(uploaded_file, sep)
+            st.dataframe(mtf)
+
+            log_options = mtf['log'].unique()
+            log_preference = st.selectbox("What log should we use for generating a new event-log?", log_options,1)
+            mtf_selection = mtf[mtf[LOG_COL]==log_preference]
+            generate_pt(mtf_selection)
+            st.write("##### Original")
+            st.write(mtf_selection)
+

data/2_grid_test.csv

@@ -0,0 +1,3 @@
+log,ratio_top_20_variants,epa_normalized_sequence_entropy_linear_forgetting
+experiment1,0.2,0.4
+experiment2,0.4,0.7

data/BaselineED_bench.csv

@@ -0,0 +1,21 @@
+log,fitness_ilp,precision_ilp,fscore_ilp,size_ilp,pnsize_ilp,cfc_ilp,fitness_imf,precision_imf,fscore_imf,size_imf,pnsize_imf,cfc_imf,fitness_heu,precision_heu,fscore_heu,size_heu,pnsize_heu,cfc_heu
+BPIC12,,,,,,,0.999782450408571,0.106249999999999,0.192086381040032,69,41,37,,,,,,
+BPIC13cp,0.999955347339294,0.792379879879879,0.8841476594077591,20.0,8.0,6.0,0.990412853232678,0.9470205909661912,0.9682307987170752,15,10,9,0.989977119234364,0.8684298767708941,0.925228660364203,14.0,9.0,8.0
+BPIC13inc,0.99997694649763,0.625730547968199,0.7697770045565601,19.0,7.0,5.0,0.957240933170762,0.716391417907929,0.819486058514255,16,10,8,0.99128117000846,0.8850810072924521,0.935175678848088,14.0,8.0,8.0
+BPIC13op,0.99993033237412,0.9065645824471852,0.950961282086593,10.0,5.0,3.0,0.8513195049834781,0.9065645824471852,0.8780739493381781,17,10,8,0.990133346397138,0.9620563035495712,0.975892918274616,12.0,7.0,7.0
+BPIC14dc_p,,,,,,,0.9998326981312632,1.0,0.9999163420675672,606,366,364,0.92732126656531,1.0,0.962290286162716,547.0,364.0,364.0
+BPIC14di_p,,,,,,,0.999900009999,1.0,0.9999500024998752,10,4,2,1.0,1.0,1.0,10.0,2.0,2.0
+BPIC15f2,,,,,,,0.9677497565467512,0.010598531351998,0.0209674330962,381,134,115,,,,,,
+BPIC16c_p,0.999843623073484,0.75266316984805,0.8588217446396421,270.0,123.0,120.0,0.8853691071783161,0.9174262372560932,0.901112653845042,110,38,34,0.7688674244586541,0.9952442715088632,0.8675311223109071,92.0,50.0,49.0
+BPIC16wm_p,0.9999495832135112,1.0,0.999974790971276,4.0,3.0,1.0,0.999900004026629,1.0,0.999949999513391,5,4,2,0.999900004026629,1.0,0.999949999513391,5.0,4.0,2.0
+BPIC17,,,,,,,0.930672500139456,0.244851509976953,0.387702105600728,73,48,40,,,,,,
+BPIC17ol,0.999984636044501,0.6172893728926371,0.7633584481974761,39.0,18.0,15.0,0.9960693326660932,0.898064579352246,0.944531514451642,14,6,4,0.9107234276582472,1.0,0.9532760361602052,24.0,12.0,9.0
+BPIC20a,0.999962791752526,0.188093126224035,0.316628409088329,89.0,38.0,38.0,0.9368177153041932,0.375765199161425,0.5363828699729011,36,21,18,0.8903598625893641,0.867035609327888,0.878542955546676,40.0,19.0,18.0
+BPIC20b,0.99998483485473,0.11309976930835,0.203215557399531,193.0,94.0,90.0,0.8859445593469291,0.348704855833889,0.500438693033593,79,46,43,0.6970214666884511,0.9141924615708572,0.7909710302567481,124.0,62.0,55.0
+BPIC20c,,,,,,,0.7723547059308711,0.190996223166598,0.306257724619519,122,71,67,,,,,,
+BPIC20d,0.999976992746818,0.213233968166344,0.351511928441461,170.0,82.0,79.0,0.867127706306101,0.40344856566562,0.5506815089742241,78,45,41,0.778405152397002,0.8877260430015661,0.8294791282917191,110.0,57.0,55.0
+BPIC20e,0.9999625734194992,0.177946979285382,0.302129002909987,101.0,43.0,43.0,0.9184257431784232,0.38688423100734,0.544429207489319,46,29,25,0.8957327113789421,0.808290592116352,0.8497681013791021,48.0,23.0,22.0
+HD,0.999957093840268,0.412049000421671,0.583611250200463,67.0,29.0,26.0,0.9784476270770972,0.759636896649265,0.8552690146197981,45,29,27,0.7266871858430181,0.8474784912426241,0.782448466293276,61.0,33.0,26.0
+RTFMP,0.9999788763172012,0.589212029307434,0.7415088783783841,43.0,17.0,14.0,0.878359786969879,0.7802754349784181,0.8264174735665141,41,25,20,0.847745391902833,0.991356698750484,0.9139439048749932,47.0,25.0,22.0
+RWABOCSL,0.999985675961848,0.18194590014049,0.307874495646305,133.0,62.0,58.0,0.8277414379848941,0.252082243592322,0.386468499184599,77,45,43,0.7998506743994891,0.680938416422287,0.7356200217515501,83.0,43.0,38.0
+SEPSIS,0.9999870882139372,0.19811033775102,0.330703956956029,96.0,47.0,44.0,0.9605344308961652,0.443996632051641,0.6072831901523931,43,27,23,0.650269438232782,0.7023809523809521,0.675321384593596,64.0,33.0,29.0

data/BaselineED_feat.csv

@@ -0,0 +1,27 @@
+log,ratio_variants_per_number_of_traces,ratio_most_common_variant,ratio_top_10_variants,epa_normalized_variant_entropy,epa_normalized_sequence_entropy,epa_normalized_sequence_entropy_linear_forgetting,epa_normalized_sequence_entropy_exponential_forgetting
+BPIC16wm_p,0.002882363538101243,0.29580255809764006,0.7141055665645829,0.0,0.0,0.0,0.0
+BPIC15f5,0.9974048442906575,0.0017301038062283738,0.10207612456747404,0.648702019618582,0.6032598312788823,0.34240966430145864,0.4045799140620184
+BPIC15f1,0.97581317764804,0.006672226855713094,0.12176814011676397,0.6528546738228733,0.610294028540377,0.270241403634718,0.3639276823477533
+BPIC19,0.04756210921051586,0.1997584752159025,0.9463679916101917,0.6455299432616961,0.32802880845275467,0.32018493101652423,0.32028165937446473
+BPIC14dia_p,0.49684657628282136,0.03745495108975459,0.5528359361592586,0.7747431481404004,0.608349852096618,0.3056135491130337,0.3774158753612022
+BPIC15f2,0.9951923076923077,0.002403846153846154,0.10336538461538461,0.6279728735030676,0.6023712370019746,0.31721666706868984,0.3904728730604407
+BPIC15f3,0.957416607523066,0.0106458481192335,0.1376863023420866,0.6617814499249461,0.6056763319106016,0.34152054617721284,0.4049335030579143
+BPIC13cp,0.1230665770006725,0.33154001344989914,0.8406186953597848,0.7053828282294897,0.31093992648055435,0.28651535436398873,0.28838337474541165
+BPIC14dc_p,0.04844444444444444,0.07494444444444444,0.7650555555555556,0.47075846018381057,0.41926577160373285,0.31259860144334484,0.3267191725545549
+BPIC20a,0.009428571428571429,0.4398095238095238,0.9500952380952381,0.6964738317936644,0.1647576988066761,0.08543856869524794,0.10438932784381554
+BPIC14di_p,4.081521591249218e-05,0.7870806236564991,0.0,1.0,0.04401817427316338,0.03332176209081124,0.03468510107858211
+BPIC17ol,0.00037213629491801375,0.3806256541458309,0.3806256541458309,0.8134791926692313,0.10512996634096308,0.05267160481055786,0.06599985770279808
+BPIC13op,0.13186813186813187,0.21733821733821734,0.7692307692307693,0.7029596109025903,0.2767705997572258,0.26209382569914347,0.26302868574911575
+RTFMP,0.0015362106803218727,0.3756201369954113,0.993103677595265,0.7693527129114833,0.11193232156025523,0.05258608507717499,0.0684419557029657
+BPIC20d,0.09623630300142925,0.2710814673654121,0.822772748928061,0.7237852824924965,0.3170439544275999,0.1848793114880042,0.21438679470909938
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data/GenBaselineED_bench.csv

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data/GenBaselineED_feat.csv

@@ -0,0 +1,26 @@
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data/GenED_bench.csv

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data/grid_1obj/grid_1objectives_ense.csv

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data/grid_1obj/grid_1objectives_enseef.csv

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data/grid_1obj/grid_1objectives_enself.csv

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data/grid_1obj/grid_1objectives_enve.csv

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data/grid_1obj/grid_1objectives_rmcv.csv

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data/grid_1obj/grid_1objectives_rt10v.csv

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data/grid_1obj/grid_1objectives_rutpt.csv

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data/grid_2obj/grid_2objectives_ense_enseef.csv

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