diff --git a/README.md b/README.md
index c0c9101885ab4e70e74301414ea89a6ba551003e..e1d53032fa70989173cb1cbc8b8c207ef4e35f2f 100644
--- a/README.md
+++ b/README.md
@@ -1,13 +1,35 @@
-# GEDI
+---
+title: Gedi
+emoji: 🌖
+colorFrom: indigo
+colorTo: blue
+sdk: streamlit
+sdk_version: 1.37.1
+app_file: utils/config_fabric.py
+pinned: false
+license: mit
+---
+
+<p>
+  <img src="gedi/utils/logo.png" alt="Logo" width="100" align="left" />
+  <h1 style="display: inline;">GEDI</h1>
+</p>
+
 **G**enerating **E**vent **D**ata with **I**ntentional Features for Benchmarking Process Mining
 
 ## Table of Contents
 
+- [Interactive Web Application](#interactive-web-application)
 - [Requirements](#requirements)
 - [Installation](#installation)
-- [Usage](#usage)
+- [General Usage](#general-usage)
+- [Experiments](#experiments)
 - [References](#references)
 
+## Interactive Web Application
+Our [interactive web application](https://huggingface.co/spaces/andreamalhera/gedi) (iGEDI) guides you through the specification process, runs GEDI for you. You can directly download the resulting generated logs or the configuration file to run GEDI locally.
+![Interface Screenshot](gedi/utils/iGEDI_interface.png)
+
 ## Requirements
 - [Miniconda](https://docs.conda.io/en/latest/miniconda.html)
 - Graphviz on your OS e.g.
@@ -22,29 +44,161 @@ 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
+python main.py -a config_files/test/experiment_test.json
 ```
-## Usage
-Our pipeline offers several pipeline steps, which can be run sequentially or partially:
-- feature_extraction
-- generation
-- benchmark
-- evaluation_plotter
+The last step should take only a few minutes to run.
 
-We also include two notebooks, which output experimental results as in our paper.
+## General Usage
+Our pipeline offers several pipeline steps, which can be run sequentially or partially ordered:
+- [Feature Extraction](#feature-extraction)
+- [Generation](#generation)
+- [Benchmark](#benchmark)
+- [Evaluation Plotter](https://github.com/lmu-dbs/gedi/blob/16-documentation-update-readme/README.md#evaluation-plotting)
 
 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
+python main.py -a config_files/pipeline_steps/<pipeline-step>.json
+```
+For reference of possible keys and values for each step, please see `config_files/test/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.
+To reproduce results from our paper, please refer to [Experiments](#experiments).
+
+### Feature Extraction
+---
+To extract the features on the event-log level and use them for hyperparameter optimization, we employ the following script:
+```console
+conda activate gedi
+python main.py -a config_files/pipeline_steps/feature_extraction.json
+```
+The JSON file consists of the following key-value pairs:
+
+- pipeline_step: denotes the current step in the pipeline (here: feature_extraction)
+- input_path: folder to the input files
+- feature params: defines a dictionary, where the inner dictionary consists of a key-value pair 'feature_set' with a list of features being extracted from the references files. A list of valid features can be looked up from the FEEED extractor
+- output_path: defines the path, where plots are saved to
+- real_eventlog_path: defines the file with the features extracted from the real event logs
+- plot_type: defines the style of the output plotting (possible values: violinplot, boxplot)
+- font_size: label font size of the output plot
+- boxplot_width: width of the violinplot/boxplot
+
+
+### Generation
+---
+After having extracted meta features from the files, the next step is to generate event log data accordingly. Generally, there are two settings on how the targets are defined: i) meta feature targets are defined by the meta features from the real event log data; ii) a configuration space is defined which resembles the feasible meta features space. 
+
+The command to execute the generation step is given by a exemplarily generation.json file:
+
+```console
+conda activate gedi
+python main.py -a config_files/pipeline_steps/generation.json
+```
+
+In the `generation.json`, we have the following key-value pairs:
+
+* pipeline_step: denotes the current step in the pipeline (here: event_logs_generation)
+* output_path: defines the output folder
+* generator_params: defines the configuration of the generator itself. For the generator itself, we can set values for the general 'experiment', 'config_space', 'n_trials', and a specific 'plot_reference_feature' being used for plotting
+
+    - experiment: defines the path to the input file which contains the features that are used for the optimization step. The 'objectives' define the specific features, which are the optimization criteria.
+    - config_space: here, we define the configuration of the generator module (here: process tree generator). The process tree generator can process input information which defines characteristics for the generated data (a more thorough overview of the params can be found [here](https://github.com/tjouck/PTandLogGenerator):
+
+        - mode: most frequent number of visible activities
+        - sequence: the probability of adding a sequence operator to the tree
+        - choice: the probability of adding a choice operator to the tree
+        - parallel: the probability of adding a parallel operator to the tree
+        - loop: the probability of adding a loop operator to the tree
+        - silent: probability to add silent activity to a choice or loop operator
+        - lt_dependency: the probability of adding a random dependency to the tree
+        - num_traces: the number of traces in the event log
+        - duplicate: the probability of duplicating an activity label
+        - or: probability to add an or operator to the tree
+
+    - n_trials: the maximum number of trials for the hyperparameter optimization to find a feasible solution to the specific configuration being used as the target
+
+    - plot_reference_feature: defines the feature, which is used on the x-axis on the output plots, i.e., each feature defined in the 'objectives' of the 'experiment' is plotted against the reference feature being defined in this value
+
+### Benchmark
+The benchmarking defines the downstream task which is used for evaluating the goodness of the synthesized event log datasets with the metrics of real-world datasets. The command to execute a benchmarking is shown in the following script:
+
+```console
+conda activate gedi
+python main.py -a config_files/pipeline_steps/benchmark.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. 
+
+In the `benchmark.json`, we have the following key-value pairs:
+
+* pipeline_step: denotes the current step in the pipeline (here: benchmark_test)
+* benchmark_test: defines the downstream task. Currently (in v 1.0), only `discovery` for process discovery is implemented
+* input_path: defines the input folder where the synthesized event log data are stored
+* output_path: defines the output folder
+* miners: defines the miners for the downstream task 'discovery' which are used in the benchmarking. In v 1.0 the miners 'inductive' for inductive miner, 'heuristics' for heuristics miner, 'imf' for inductive miner infrequent, as well as 'ilp' for integer linear programming are implemented
+
+
+### Evaluation Plotting
+The purpose of the evaluation plotting step is used just for visualization. Some examples of how the plotter can be used is shown in the following exemplarily script:
+
+
+```console
+conda activate gedi
+python main.py -a config_files/pipeline_steps/evaluation_plotter.json
+```
+
+Generally, in the `evaluation_plotter.json`, we have the following key-value pairs:
+
+* pipeline_step: denotes the current step in the pipeline (here: evaluation_plotter)
+* input_path: defines the input file or the input folder which is considered for the visualizations. If a single file is specified, only the features in that file are considered whereas in the case of specifying a folder, the framework iterates over all files and uses them for plotting
+* plot_reference_feature: defines the feature that is used on the x-axis on the output plots, i.e., each feature defined in the input file is plotted against the reference feature being defined in this value
+* targets: defines the target values which are also used as reference. Likewise to the input_path, the targets can be specified by a single file or by a folder
+* output_path: defines where to store the plots
+
+## Experiments
+In this repository, experiments can be run selectively or from scratch, as preferred. For this purpose, we linked both inputs and outputs for each stage. In this section, we present the reproduction of generated event data, as in our paper, as well as the [visualization of evaluation figures](#visualizations).
+We present two settings for generating intentional event logs, using [real targets](#generating-data-with-real-targets) or using [grid targets](#generating-data-with-grid-targets). Both settings output `.xes` event logs, `.json` and `.csv` files containing feature values, as well as evaluation results, from running a [process discovery benchmark](#benchmark), for the generated event logs.
+
+### Generating data with real targets
+To execute the experiments with real targets, we employ the [experiment_real_targets.json](config_files/experiment_real_targets.json). The script's pipeline will output the [generated event logs (GenBaselineED)](data/event_logs/GenBaselineED), which optimize their feature values towards [real-world event data features](data/BaselineED_feat.csv), alongside their respectively measured [feature values](data/GenBaselineED_feat.csv) and [benchmark metrics values](data/GenBaselineED_bench.csv).
+
+```console
+conda activate gedi
+python main.py -a config_files/experiment_real_targets.json
+```
+
+### Generating data with grid targets
+To execute the experiments with grid targets, a single [configuration](config_files/grid_2obj) can be selected or all [grid objectives](data/grid_2obj) can be run with one command using the following script. This script will output the [generated event logs (GenED)](data/event_logs/GenED), alongside their respectively measured [feature values](data/GenED_feat.csv) and [benchmark metrics values](data/GenED_bench.csv).
+```
+conda activate gedi
+python execute_grid_experiments.py config_files/grid_2obj
+```
+We employ the [experiment_grid_2obj_configfiles_fabric.ipynb](notebooks/experiment_grid_2obj_configfiles_fabric.ipynb) to create all necessary [configuration](config_files/grid_2obj) and [objective](data/grid_2obj) files for this experiment.
+For more details about these config_files, please refer to [Feature Extraction](#feature-extraction), [Generation](#generation), and [Benchmark](#benchmark).
+To create configuration files for grid objectives interactively, you can use the start the following dashboard:
+```
+streamlit run utils/config_fabric.py # To tunnel to local machine add: --server.port 8501 --server.headless true
+
+# In local machine (only in case you are tunneling):
+ssh -N -f -L 9000:localhost:8501 <user@remote_machine.com>
+open "http://localhost:9000/"
+```
+### Visualizations
+To run the visualizations, we employ [jupyter notebooks](https://jupyter.org/install) and [add the installed environment to the jupyter notebook](https://medium.com/@nrk25693/how-to-add-your-conda-environment-to-your-jupyter-notebook-in-just-4-steps-abeab8b8d084). We then start all visualizations by running e.g.: `jupyter noteboook`. In the following, we describe the `.ipynb`-files in the folder `\notebooks` to reproduce the figures from our paper. 
+
+#### [Fig. 4 and fig. 5 Representativeness](notebooks/gedi_figs4and5_representativeness.ipynb)
+To visualize the coverage of the feasible feature space of generated event logs compared to existing real-world benchmark datasets, in this notebook, we conduct a principal component analysis on the features of both settings. The first two principal components are utilized to visualize the coverage which is further highlighted by computing a convex hull of the 2D mapping.Additionally, we visualize the distribution of each meta feature we used in the paper as a boxplot. Additional features can be extracted with FEEED. Therefore, the notebook contains the figures 4 and 5 in the paper.
+
+#### [Fig. 6 Benchmark Boxplots](notebooks/gedi_fig6_benchmark_boxplots.ipynb)
+This notebook is used to visualize the metric distribution of real event logs compared to the generated ones. It shows 5 different metrics on 3 various process discovery techniques. We use 'fitness,', 'precision', 'fscore', 'size', 'cfc' (control-flow complexity) as metrics and as 'heuristic miner', 'ilp' (integer linear programming), and 'imf' (inductive miner infrequent) as miners. The notebook outputs the visualization shown in Fig.6 in the paper.
+
+#### [Fig. 7 and fig. 8 Benchmark's Statistical Tests](notebooks/gedi_figs7and8_benchmarking_statisticalTests.ipynb)
+
+This notebook is used to answer the question if there is a statistically significant relation between feature similarity and performance metrics for the downstream tasks of process discovery. For that, we compute the pearson coefficient, as well as the kendall's tau coefficient. This elucidates the correlation between the features with metric scores being used for process discovery. Each coefficient is calculated for three different settings: i) real-world datasets; ii) synthesized event log data with real-world targets; iii) synthesized event log data with grid objectives. Figures 7 and 8 shown in the paper refer to this notebook.
+
+#### [Fig. 9 Consistency and fig. 10 Limitations](notebooks/gedi_figs9and10_consistency.ipynb)
+Likewise to the evaluation on the statistical tests in notebook `gedi_figs7and8_benchmarking_statisticalTests.ipynb`, this notebook is used to compute the differences between two correlation matrices $\Delta C = C_1 - C_2$. This logic is employed to evaluate and visualize the distance of two correlation matrices. Furthermore, we show how significant scores are retained from the correlations being evaluated on real-world datasets coompared to synthesized event log datasets with real-world targets. In Fig. 9 and 10 in the paper, the results of the notebook are shown. 
 
 ## References
 The framework used by `GEDI` is taken directly from the original paper by [Maldonado](mailto:andreamalher.works@gmail.com), 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.
diff --git a/config.py b/config.py
index a67752b41de7e0f424ea183f1e1b0fc9156395f1..2e8e65e00c59271f1964aa188f8d6804a67c7c8f 100644
--- a/config.py
+++ b/config.py
@@ -1,10 +1,8 @@
 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
+from utils.param_keys import PIPELINE_STEP, INPUT_PATH, OUTPUT_PATH
+from utils.param_keys.features import FEATURE_SET, FEATURE_PARAMS
 
 def get_model_params_list(alg_json_file: str) :#-> list[dict]:
     """
@@ -20,69 +18,8 @@ def get_model_params_list(alg_json_file: str) :#-> list[dict]:
         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},
+            {PIPELINE_STEP: 'feature_extraction', INPUT_PATH: 'data/test',
+             FEATURE_PARAMS: {FEATURE_SET: ['ratio_unique_traces_per_trace',
+                                            'ratio_most_common_variant']},
+             OUTPUT_PATH: 'output/plots'}
             ]
-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
diff --git a/config_files/algorithm/augmentation.json b/config_files/algorithm/augmentation.json
deleted file mode 100644
index 8b15c2d832a2b420e3aabaf539ddcc2b4a77b745..0000000000000000000000000000000000000000
--- a/config_files/algorithm/augmentation.json
+++ /dev/null
@@ -1,12 +0,0 @@
-[
-  {
-    "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"
-  }
-]
diff --git a/config_files/algorithm/feature_extraction.json b/config_files/algorithm/feature_extraction.json
deleted file mode 100644
index f8444d2f636599ac3d39a9a348feceb23147e49c..0000000000000000000000000000000000000000
--- a/config_files/algorithm/feature_extraction.json
+++ /dev/null
@@ -1,12 +0,0 @@
-[
-  {
-    "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
-  }
-]
diff --git a/config_files/config_layout.json b/config_files/config_layout.json
new file mode 100644
index 0000000000000000000000000000000000000000..86c412a565675e65c8025a90b08be78390923ec8
--- /dev/null
+++ b/config_files/config_layout.json
@@ -0,0 +1,48 @@
+[
+  {
+    "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/test/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/frontend/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": {"n_traces":832, "n_unique_traces":828, "ratio_variants_per_number_of_traces":0.99, "trace_len_min":1, "trace_len_max":132, "trace_len_mean":53.31, "trace_len_median":54, "trace_len_mode":61, "trace_len_std":19.89, "trace_len_variance":395.81, "trace_len_q1":44, "trace_len_q3":62, "trace_len_iqr":18, "trace_len_geometric_mean":48.15, "trace_len_geometric_std":1.69, "trace_len_harmonic_mean":37.58, "trace_len_skewness":0.0541, "trace_len_kurtosis":0.81, "trace_len_coefficient_variation":0.37, "trace_len_entropy":6.65, "trace_len_hist1":0.004, "trace_len_hist2":0.005, "trace_len_hist3":0.005, "trace_len_hist4":0.024, "trace_len_hist5":0.024, "trace_len_hist6":0.008, "trace_len_hist7":0.005, "trace_len_hist8":0.001, "trace_len_hist9":0.0, "trace_len_hist10":0.00, "trace_len_skewness_hist":0.05, "trace_len_kurtosis_hist":0.8, "ratio_most_common_variant":0.0, "ratio_top_1_variants":0.01, "ratio_top_5_variants":0.05, "ratio_top_10_variants":0.10, "ratio_top_20_variants":0.2, "ratio_top_50_variants":0.5, "ratio_top_75_variants":0.75, "mean_variant_occurrence":1.0, "std_variant_occurrence":0.07, "skewness_variant_occurrence":14.28, "kurtosis_variant_occurrence":202.00, "n_unique_activities":410, "activities_min":1, "activities_max":830, "activities_mean":108.18, "activities_median":12, "activities_std":187.59, "activities_variance":35189, "activities_q1":3, "activities_q3":125, "activities_iqr":122, "activities_skewness":2.13, "activities_kurtosis":3.81, "n_unique_start_activities":14, "start_activities_min":1, "start_activities_max":731, "start_activities_mean":59.43, "start_activities_median":1, "start_activities_std":186.72, "start_activities_variance":34863, "start_activities_q1":1, "start_activities_q3":8, "start_activities_iqr":7, "start_activities_skewness":3, "start_activities_kurtosis":9.0, "n_unique_end_activities":82, "end_activities_min":1, "end_activities_max":216, "end_activities_mean":10, "end_activities_median":1, "end_activities_std":35, "end_activities_variance":1247, "end_activities_q1":1, "end_activities_q3":3, "end_activities_iqr":2, "end_activities_skewness":5, "end_activities_kurtosis":26, "eventropy_trace":10, "eventropy_prefix":15, "eventropy_global_block":19, "eventropy_lempel_ziv":4, "eventropy_k_block_diff_1":7.1, "eventropy_k_block_diff_3":7.1, "eventropy_k_block_diff_5":7.1, "eventropy_k_block_ratio_1":7.1, "eventropy_k_block_ratio_3":7.1, "eventropy_k_block_ratio_5":7.1, "eventropy_knn_3":5.54, "eventropy_knn_5":5.04, "eventropy_knn_7":4.72, "epa_variant_entropy":240512, "epa_normalized_variant_entropy":0.68, "epa_sequence_entropy":285876, "epa_normalized_sequence_entropy":0.60, "epa_sequence_entropy_linear_forgetting":150546, "epa_normalized_sequence_entropy_linear_forgetting":0.32, "epa_sequence_entropy_exponential_forgetting":185312, "epa_normalized_sequence_entropy_exponential_forgetting":0.39},
+      "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": 50
+    }
+  },
+  {
+    "pipeline_step": "feature_extraction",
+    "input_path": "data/test",
+    "feature_params": {"feature_set": ["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", "eventropy_trace", "eventropy_prefix", "eventropy_prefix_flattened", "eventropy_global_block", "eventropy_global_block_flattened", "eventropy_lempel_ziv", "eventropy_lempel_ziv_flattened", "eventropy_k_block_diff_1", "eventropy_k_block_diff_3", "eventropy_k_block_diff_5", "eventropy_k_block_ratio_1", "eventropy_k_block_ratio_3", "eventropy_k_block_ratio_5", "eventropy_knn_3", "eventropy_knn_5", "eventropy_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"]},
+    "output_path": "output/plots",
+    "real_eventlog_path": "data/BaselineED_feat.csv",
+    "plot_type": "boxplot"
+  },
+  {
+    "pipeline_step": "benchmark_test",
+    "benchmark_task": "discovery",
+    "input_path":"data/test",
+    "output_path":"output",
+    "miners" : ["inductive", "heu", "imf", "ilp"]
+  }
+]
diff --git a/config_files/experiment_real_targets.json b/config_files/experiment_real_targets.json
new file mode 100644
index 0000000000000000000000000000000000000000..933de3bb1b8ef8203ea58841a4c19b2f09de379a
--- /dev/null
+++ b/config_files/experiment_real_targets.json
@@ -0,0 +1,41 @@
+[
+   {
+    "pipeline_step": "event_logs_generation",
+    "output_path": "output",
+    "generator_params": {
+      "experiment": {
+        "input_path": "data/BaselineED_feat.csv",
+        "objectives":["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"]},
+        "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": 200,
+      "plot_reference_feature": ""
+    }
+  },
+  {
+    "pipeline_step": "feature_extraction",
+    "input_path": "output/BaselineED_feat/7_ense_enseef_enself_enve_rmcv_rt10v_rutpt/",
+    "input_path": "output/features/BaselineED_feat/7_ense_enseef_enself_enve_rmcv_rt10v_rutpt/",
+    "feature_params": {"feature_set":["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"]},
+    "output_path": "output/plots",
+    "real_eventlog_path": "data/BaselineED_feat.csv",
+    "plot_type": "boxplot"
+  },
+  {
+    "pipeline_step": "benchmark_test",
+    "benchmark_test": "discovery",
+    "input_path": "output/BaselineED_feat/7_ense_enseef_enself_enve_rmcv_rt10v_rutpt/",
+    "output_path":"output",
+    "miners" : ["heu", "imf", "ilp"]
+  }
+]
diff --git a/config_files/grid_2obj/generator_grid_2objectives_ense_enseef.json b/config_files/grid_2obj/generator_grid_2objectives_ense_enseef.json
new file mode 100644
index 0000000000000000000000000000000000000000..5039aadc89076d851465bf33f93a695b5dde3e0e
--- /dev/null
+++ b/config_files/grid_2obj/generator_grid_2objectives_ense_enseef.json
@@ -0,0 +1 @@
+[{"pipeline_step": "event_logs_generation", "output_path": "output/generated/grid_2obj", "generator_params": {"experiment": {"input_path": "data/grid_2obj/grid_2objectives_ense_enseef.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": 200}}, {"pipeline_step": "feature_extraction", "input_path": "output/features/generated/grid_2obj/grid_2objectives_ense_enseef/2_ense_enseef", "feature_params": {"feature_set": ["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"]}, "output_path": "output/plots", "real_eventlog_path": "data/BaselineED_feat.csv", "plot_type": "boxplot"}, {"pipeline_step": "benchmark_test", "benchmark_test": "discovery", "input_path": "output/generated/grid_2obj/grid_2objectives_ense_enseef/2_ense_enseef", "output_path": "output", "miners": ["heu", "imf", "ilp"]}]
\ No newline at end of file
diff --git a/config_files/grid_2obj/generator_grid_2objectives_ense_enself.json b/config_files/grid_2obj/generator_grid_2objectives_ense_enself.json
new file mode 100644
index 0000000000000000000000000000000000000000..38fa26dd3c238bc4b140b33ac740bd7a9a3fad3c
--- /dev/null
+++ b/config_files/grid_2obj/generator_grid_2objectives_ense_enself.json
@@ -0,0 +1 @@
+[{"pipeline_step": "event_logs_generation", "output_path": "output/generated/grid_2obj", "generator_params": {"experiment": {"input_path": "data/grid_2obj/grid_2objectives_ense_enself.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": 200}}, {"pipeline_step": "feature_extraction", "input_path": "output/features/generated/grid_2obj/grid_2objectives_ense_enself/2_ense_enself", "feature_params": {"feature_set": ["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"]}, "output_path": "output/plots", "real_eventlog_path": "data/BaselineED_feat.csv", "plot_type": "boxplot"}, {"pipeline_step": "benchmark_test", "benchmark_test": "discovery", "input_path": "output/generated/grid_2obj/grid_2objectives_ense_enself/2_ense_enself", "output_path": "output", "miners": ["heu", "imf", "ilp"]}]
\ No newline at end of file
diff --git a/config_files/grid_2obj/generator_grid_2objectives_ense_enve.json b/config_files/grid_2obj/generator_grid_2objectives_ense_enve.json
new file mode 100644
index 0000000000000000000000000000000000000000..2a7a983cda36f2d93e76bc4a8b50d3a6992971da
--- /dev/null
+++ b/config_files/grid_2obj/generator_grid_2objectives_ense_enve.json
@@ -0,0 +1 @@
+[{"pipeline_step": "event_logs_generation", "output_path": "output/generated/grid_2obj", "generator_params": {"experiment": {"input_path": "data/grid_2obj/grid_2objectives_ense_enve.csv", "objectives": ["epa_normalized_sequence_entropy", "epa_normalized_variant_entropy"]}, "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": 200}}, {"pipeline_step": "feature_extraction", "input_path": "output/features/generated/grid_2obj/grid_2objectives_ense_enve/2_ense_enve", "feature_params": {"feature_set": ["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"]}, "output_path": "output/plots", "real_eventlog_path": "data/BaselineED_feat.csv", "plot_type": "boxplot"}, {"pipeline_step": "benchmark_test", "benchmark_test": "discovery", "input_path": "output/generated/grid_2obj/grid_2objectives_ense_enve/2_ense_enve", "output_path": "output", "miners": ["heu", "imf", "ilp"]}]
\ No newline at end of file
diff --git a/config_files/grid_2obj/generator_grid_2objectives_ense_rmcv.json b/config_files/grid_2obj/generator_grid_2objectives_ense_rmcv.json
new file mode 100644
index 0000000000000000000000000000000000000000..adc34aafeb458febc30115af69caaa2a9cd08e4e
--- /dev/null
+++ b/config_files/grid_2obj/generator_grid_2objectives_ense_rmcv.json
@@ -0,0 +1 @@
+[{"pipeline_step": "event_logs_generation", "output_path": "output/generated/grid_2obj", "generator_params": {"experiment": {"input_path": "data/grid_2obj/grid_2objectives_ense_rmcv.csv", "objectives": ["epa_normalized_sequence_entropy", "ratio_most_common_variant"]}, "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": 200}}, {"pipeline_step": "feature_extraction", "input_path": "output/features/generated/grid_2obj/grid_2objectives_ense_rmcv/2_ense_rmcv", "feature_params": {"feature_set": ["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"]}, "output_path": "output/plots", "real_eventlog_path": "data/BaselineED_feat.csv", "plot_type": "boxplot"}, {"pipeline_step": "benchmark_test", "benchmark_test": "discovery", "input_path": "output/generated/grid_2obj/grid_2objectives_ense_rmcv/2_ense_rmcv", "output_path": "output", "miners": ["heu", "imf", "ilp"]}]
\ No newline at end of file
diff --git a/config_files/grid_2obj/generator_grid_2objectives_ense_rt10v.json b/config_files/grid_2obj/generator_grid_2objectives_ense_rt10v.json
new file mode 100644
index 0000000000000000000000000000000000000000..5f93648067d25982b5a0b81ef8e482d356034194
--- /dev/null
+++ b/config_files/grid_2obj/generator_grid_2objectives_ense_rt10v.json
@@ -0,0 +1 @@
+[{"pipeline_step": "event_logs_generation", "output_path": "output/generated/grid_2obj", "generator_params": {"experiment": {"input_path": "data/grid_2obj/grid_2objectives_ense_rt10v.csv", "objectives": ["epa_normalized_sequence_entropy", "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": 200}}, {"pipeline_step": "feature_extraction", "input_path": "output/features/generated/grid_2obj/grid_2objectives_ense_rt10v/2_ense_rt10v", "feature_params": {"feature_set": ["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"]}, "output_path": "output/plots", "real_eventlog_path": "data/BaselineED_feat.csv", "plot_type": "boxplot"}, {"pipeline_step": "benchmark_test", "benchmark_test": "discovery", "input_path": "output/generated/grid_2obj/grid_2objectives_ense_rt10v/2_ense_rt10v", "output_path": "output", "miners": ["heu", "imf", "ilp"]}]
\ No newline at end of file
diff --git a/config_files/grid_2obj/generator_grid_2objectives_ense_rvpnot.json b/config_files/grid_2obj/generator_grid_2objectives_ense_rvpnot.json
new file mode 100644
index 0000000000000000000000000000000000000000..7987bec7503ded8d32644f5f15bd847a70044e73
--- /dev/null
+++ b/config_files/grid_2obj/generator_grid_2objectives_ense_rvpnot.json
@@ -0,0 +1 @@
+[{"pipeline_step": "event_logs_generation", "output_path": "output/generated/grid_2obj", "generator_params": {"experiment": {"input_path": "data/grid_2obj/grid_2objectives_ense_rvpnot.csv", "objectives": ["epa_normalized_sequence_entropy", "ratio_variants_per_number_of_traces"]}, "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": 200}}, {"pipeline_step": "feature_extraction", "input_path": "output/features/generated/grid_2obj/grid_2objectives_ense_rvpnot/2_ense_rvpnot", "feature_params": {"feature_set": ["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"]}, "output_path": "output/plots", "real_eventlog_path": "data/BaselineED_feat.csv", "plot_type": "boxplot"}, {"pipeline_step": "benchmark_test", "benchmark_test": "discovery", "input_path": "output/generated/grid_2obj/grid_2objectives_ense_rvpnot/2_ense_rvpnot", "output_path": "output", "miners": ["heu", "imf", "ilp"]}]
\ No newline at end of file
diff --git a/config_files/grid_2obj/generator_grid_2objectives_enseef_enself.json b/config_files/grid_2obj/generator_grid_2objectives_enseef_enself.json
new file mode 100644
index 0000000000000000000000000000000000000000..7a9ca360a93d04ee36fd87890af16d704b6b075c
--- /dev/null
+++ b/config_files/grid_2obj/generator_grid_2objectives_enseef_enself.json
@@ -0,0 +1 @@
+[{"pipeline_step": "event_logs_generation", "output_path": "output/generated/grid_2obj", "generator_params": {"experiment": {"input_path": "data/grid_2obj/grid_2objectives_enseef_enself.csv", "objectives": ["epa_normalized_sequence_entropy_exponential_forgetting", "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": 200}}, {"pipeline_step": "feature_extraction", "input_path": "output/features/generated/grid_2obj/grid_2objectives_enseef_enself/2_enseef_enself", "feature_params": {"feature_set": ["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"]}, "output_path": "output/plots", "real_eventlog_path": "data/BaselineED_feat.csv", "plot_type": "boxplot"}, {"pipeline_step": "benchmark_test", "benchmark_test": "discovery", "input_path": "output/generated/grid_2obj/grid_2objectives_enseef_enself/2_enseef_enself", "output_path": "output", "miners": ["heu", "imf", "ilp"]}]
\ No newline at end of file
diff --git a/config_files/grid_2obj/generator_grid_2objectives_enseef_enve.json b/config_files/grid_2obj/generator_grid_2objectives_enseef_enve.json
new file mode 100644
index 0000000000000000000000000000000000000000..5f7cd252f1a131ed0fbd0863e00540dfca3f2e53
--- /dev/null
+++ b/config_files/grid_2obj/generator_grid_2objectives_enseef_enve.json
@@ -0,0 +1 @@
+[{"pipeline_step": "event_logs_generation", "output_path": "output/generated/grid_2obj", "generator_params": {"experiment": {"input_path": "data/grid_2obj/grid_2objectives_enseef_enve.csv", "objectives": ["epa_normalized_sequence_entropy_exponential_forgetting", "epa_normalized_variant_entropy"]}, "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": 200}}, {"pipeline_step": "feature_extraction", "input_path": "output/features/generated/grid_2obj/grid_2objectives_enseef_enve/2_enseef_enve", "feature_params": {"feature_set": ["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"]}, "output_path": "output/plots", "real_eventlog_path": "data/BaselineED_feat.csv", "plot_type": "boxplot"}, {"pipeline_step": "benchmark_test", "benchmark_test": "discovery", "input_path": "output/generated/grid_2obj/grid_2objectives_enseef_enve/2_enseef_enve", "output_path": "output", "miners": ["heu", "imf", "ilp"]}]
\ No newline at end of file
diff --git a/config_files/grid_2obj/generator_grid_2objectives_enseef_rmcv.json b/config_files/grid_2obj/generator_grid_2objectives_enseef_rmcv.json
new file mode 100644
index 0000000000000000000000000000000000000000..9757055987b142297033c98b8e44efa9825a4f0e
--- /dev/null
+++ b/config_files/grid_2obj/generator_grid_2objectives_enseef_rmcv.json
@@ -0,0 +1 @@
+[{"pipeline_step": "event_logs_generation", "output_path": "output/generated/grid_2obj", "generator_params": {"experiment": {"input_path": "data/grid_2obj/grid_2objectives_enseef_rmcv.csv", "objectives": ["epa_normalized_sequence_entropy_exponential_forgetting", "ratio_most_common_variant"]}, "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": 200}}, {"pipeline_step": "feature_extraction", "input_path": "output/features/generated/grid_2obj/grid_2objectives_enseef_rmcv/2_enseef_rmcv", "feature_params": {"feature_set": ["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"]}, "output_path": "output/plots", "real_eventlog_path": "data/BaselineED_feat.csv", "plot_type": "boxplot"}, {"pipeline_step": "benchmark_test", "benchmark_test": "discovery", "input_path": "output/generated/grid_2obj/grid_2objectives_enseef_rmcv/2_enseef_rmcv", "output_path": "output", "miners": ["heu", "imf", "ilp"]}]
\ No newline at end of file
diff --git a/config_files/grid_2obj/generator_grid_2objectives_enseef_rt10v.json b/config_files/grid_2obj/generator_grid_2objectives_enseef_rt10v.json
new file mode 100644
index 0000000000000000000000000000000000000000..f5ec07098e70c557d096270b7e5ad495e076984e
--- /dev/null
+++ b/config_files/grid_2obj/generator_grid_2objectives_enseef_rt10v.json
@@ -0,0 +1 @@
+[{"pipeline_step": "event_logs_generation", "output_path": "output/generated/grid_2obj", "generator_params": {"experiment": {"input_path": "data/grid_2obj/grid_2objectives_enseef_rt10v.csv", "objectives": ["epa_normalized_sequence_entropy_exponential_forgetting", "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": 200}}, {"pipeline_step": "feature_extraction", "input_path": "output/features/generated/grid_2obj/grid_2objectives_enseef_rt10v/2_enseef_rt10v", "feature_params": {"feature_set": ["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"]}, "output_path": "output/plots", "real_eventlog_path": "data/BaselineED_feat.csv", "plot_type": "boxplot"}, {"pipeline_step": "benchmark_test", "benchmark_test": "discovery", "input_path": "output/generated/grid_2obj/grid_2objectives_enseef_rt10v/2_enseef_rt10v", "output_path": "output", "miners": ["heu", "imf", "ilp"]}]
\ No newline at end of file
diff --git a/config_files/grid_2obj/generator_grid_2objectives_enseef_rvpnot.json b/config_files/grid_2obj/generator_grid_2objectives_enseef_rvpnot.json
new file mode 100644
index 0000000000000000000000000000000000000000..a352f0ba11b78c6791bffcb74ce32e0da64e75ce
--- /dev/null
+++ b/config_files/grid_2obj/generator_grid_2objectives_enseef_rvpnot.json
@@ -0,0 +1 @@
+[{"pipeline_step": "event_logs_generation", "output_path": "output/generated/grid_2obj", "generator_params": {"experiment": {"input_path": "data/grid_2obj/grid_2objectives_enseef_rvpnot.csv", "objectives": ["epa_normalized_sequence_entropy_exponential_forgetting", "ratio_variants_per_number_of_traces"]}, "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": 200}}, {"pipeline_step": "feature_extraction", "input_path": "output/features/generated/grid_2obj/grid_2objectives_enseef_rvpnot/2_enseef_rvpnot", "feature_params": {"feature_set": ["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"]}, "output_path": "output/plots", "real_eventlog_path": "data/BaselineED_feat.csv", "plot_type": "boxplot"}, {"pipeline_step": "benchmark_test", "benchmark_test": "discovery", "input_path": "output/generated/grid_2obj/grid_2objectives_enseef_rvpnot/2_enseef_rvpnot", "output_path": "output", "miners": ["heu", "imf", "ilp"]}]
\ No newline at end of file
diff --git a/config_files/grid_2obj/generator_grid_2objectives_enself_enve.json b/config_files/grid_2obj/generator_grid_2objectives_enself_enve.json
new file mode 100644
index 0000000000000000000000000000000000000000..04c9eeb3d37e62937499a5a4396675b981982267
--- /dev/null
+++ b/config_files/grid_2obj/generator_grid_2objectives_enself_enve.json
@@ -0,0 +1 @@
+[{"pipeline_step": "event_logs_generation", "output_path": "output/generated/grid_2obj", "generator_params": {"experiment": {"input_path": "data/grid_2obj/grid_2objectives_enself_enve.csv", "objectives": ["epa_normalized_sequence_entropy_linear_forgetting", "epa_normalized_variant_entropy"]}, "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": 200}}, {"pipeline_step": "feature_extraction", "input_path": "output/features/generated/grid_2obj/grid_2objectives_enself_enve/2_enself_enve", "feature_params": {"feature_set": ["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"]}, "output_path": "output/plots", "real_eventlog_path": "data/BaselineED_feat.csv", "plot_type": "boxplot"}, {"pipeline_step": "benchmark_test", "benchmark_test": "discovery", "input_path": "output/generated/grid_2obj/grid_2objectives_enself_enve/2_enself_enve", "output_path": "output", "miners": ["heu", "imf", "ilp"]}]
\ No newline at end of file
diff --git a/config_files/grid_2obj/generator_grid_2objectives_enself_rmcv.json b/config_files/grid_2obj/generator_grid_2objectives_enself_rmcv.json
new file mode 100644
index 0000000000000000000000000000000000000000..4be66efb29f60c6499683a5a31dbc3fdf457be45
--- /dev/null
+++ b/config_files/grid_2obj/generator_grid_2objectives_enself_rmcv.json
@@ -0,0 +1 @@
+[{"pipeline_step": "event_logs_generation", "output_path": "output/generated/grid_2obj", "generator_params": {"experiment": {"input_path": "data/grid_2obj/grid_2objectives_enself_rmcv.csv", "objectives": ["epa_normalized_sequence_entropy_linear_forgetting", "ratio_most_common_variant"]}, "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": 200}}, {"pipeline_step": "feature_extraction", "input_path": "output/features/generated/grid_2obj/grid_2objectives_enself_rmcv/2_enself_rmcv", "feature_params": {"feature_set": ["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"]}, "output_path": "output/plots", "real_eventlog_path": "data/BaselineED_feat.csv", "plot_type": "boxplot"}, {"pipeline_step": "benchmark_test", "benchmark_test": "discovery", "input_path": "output/generated/grid_2obj/grid_2objectives_enself_rmcv/2_enself_rmcv", "output_path": "output", "miners": ["heu", "imf", "ilp"]}]
\ No newline at end of file
diff --git a/config_files/grid_2obj/generator_grid_2objectives_enself_rt10v.json b/config_files/grid_2obj/generator_grid_2objectives_enself_rt10v.json
new file mode 100644
index 0000000000000000000000000000000000000000..ed9c9c25c5bd0c2a24348b7accb43d2f730dabfb
--- /dev/null
+++ b/config_files/grid_2obj/generator_grid_2objectives_enself_rt10v.json
@@ -0,0 +1 @@
+[{"pipeline_step": "event_logs_generation", "output_path": "output/generated/grid_2obj", "generator_params": {"experiment": {"input_path": "data/grid_2obj/grid_2objectives_enself_rt10v.csv", "objectives": ["epa_normalized_sequence_entropy_linear_forgetting", "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": 200}}, {"pipeline_step": "feature_extraction", "input_path": "output/features/generated/grid_2obj/grid_2objectives_enself_rt10v/2_enself_rt10v", "feature_params": {"feature_set": ["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"]}, "output_path": "output/plots", "real_eventlog_path": "data/BaselineED_feat.csv", "plot_type": "boxplot"}, {"pipeline_step": "benchmark_test", "benchmark_test": "discovery", "input_path": "output/generated/grid_2obj/grid_2objectives_enself_rt10v/2_enself_rt10v", "output_path": "output", "miners": ["heu", "imf", "ilp"]}]
\ No newline at end of file
diff --git a/config_files/grid_2obj/generator_grid_2objectives_enself_rvpnot.json b/config_files/grid_2obj/generator_grid_2objectives_enself_rvpnot.json
new file mode 100644
index 0000000000000000000000000000000000000000..61dca57c5417fe443f800dd34f3f03a165ee0c03
--- /dev/null
+++ b/config_files/grid_2obj/generator_grid_2objectives_enself_rvpnot.json
@@ -0,0 +1 @@
+[{"pipeline_step": "event_logs_generation", "output_path": "output/generated/grid_2obj", "generator_params": {"experiment": {"input_path": "data/grid_2obj/grid_2objectives_enself_rvpnot.csv", "objectives": ["epa_normalized_sequence_entropy_linear_forgetting", "ratio_variants_per_number_of_traces"]}, "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": 200}}, {"pipeline_step": "feature_extraction", "input_path": "output/features/generated/grid_2obj/grid_2objectives_enself_rvpnot/2_enself_rvpnot", "feature_params": {"feature_set": ["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"]}, "output_path": "output/plots", "real_eventlog_path": "data/BaselineED_feat.csv", "plot_type": "boxplot"}, {"pipeline_step": "benchmark_test", "benchmark_test": "discovery", "input_path": "output/generated/grid_2obj/grid_2objectives_enself_rvpnot/2_enself_rvpnot", "output_path": "output", "miners": ["heu", "imf", "ilp"]}]
\ No newline at end of file
diff --git a/config_files/grid_2obj/generator_grid_2objectives_enve_mvo.json b/config_files/grid_2obj/generator_grid_2objectives_enve_mvo.json
new file mode 100644
index 0000000000000000000000000000000000000000..ecc49ca5a75e0fe3e80a6ca54de02ff42c66e6c9
--- /dev/null
+++ b/config_files/grid_2obj/generator_grid_2objectives_enve_mvo.json
@@ -0,0 +1 @@
+[{"pipeline_step": "event_logs_generation", "output_path": "output/shaining/grid_2obj", "generator_params": {"experiment": {"input_path": "data/grid_2obj/grid_2objectives_enve_mvo.csv", "objectives": ["epa_normalized_variant_entropy", "mean_variant_occurrence"]}, "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": 200}}, {"pipeline_step": "feature_extraction", "input_path": "output/features/shaining/grid_2obj/grid_2objectives_enve_mvo/2_enve_mvo", "feature_params": {"feature_set": ["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"]}, "output_path": "output/plots", "real_eventlog_path": "data/BaselineED_feat.csv", "plot_type": "boxplot"}, {"pipeline_step": "benchmark_test", "benchmark_test": "discovery", "input_path": "output/shaining/grid_2obj/grid_2objectives_enve_mvo/2_enve_mvo", "output_path": "output", "miners": ["heu", "imf", "ilp"]}]
\ No newline at end of file
diff --git a/config_files/grid_2obj/generator_grid_2objectives_enve_rmcv.json b/config_files/grid_2obj/generator_grid_2objectives_enve_rmcv.json
new file mode 100644
index 0000000000000000000000000000000000000000..86d5744b38a82275d5309219feac5b3c5ce1b4a0
--- /dev/null
+++ b/config_files/grid_2obj/generator_grid_2objectives_enve_rmcv.json
@@ -0,0 +1 @@
+[{"pipeline_step": "event_logs_generation", "output_path": "output/generated/grid_2obj", "generator_params": {"experiment": {"input_path": "data/grid_2obj/grid_2objectives_enve_rmcv.csv", "objectives": ["epa_normalized_variant_entropy", "ratio_most_common_variant"]}, "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": 200}}, {"pipeline_step": "feature_extraction", "input_path": "output/features/generated/grid_2obj/grid_2objectives_enve_rmcv/2_enve_rmcv", "feature_params": {"feature_set": ["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"]}, "output_path": "output/plots", "real_eventlog_path": "data/BaselineED_feat.csv", "plot_type": "boxplot"}, {"pipeline_step": "benchmark_test", "benchmark_test": "discovery", "input_path": "output/generated/grid_2obj/grid_2objectives_enve_rmcv/2_enve_rmcv", "output_path": "output", "miners": ["heu", "imf", "ilp"]}]
\ No newline at end of file
diff --git a/config_files/grid_2obj/generator_grid_2objectives_enve_rt10v.json b/config_files/grid_2obj/generator_grid_2objectives_enve_rt10v.json
new file mode 100644
index 0000000000000000000000000000000000000000..10572da6fbada7698ec2cda5cfbc0d9b22473d70
--- /dev/null
+++ b/config_files/grid_2obj/generator_grid_2objectives_enve_rt10v.json
@@ -0,0 +1 @@
+[{"pipeline_step": "event_logs_generation", "output_path": "output/generated/grid_2obj", "generator_params": {"experiment": {"input_path": "data/grid_2obj/grid_2objectives_enve_rt10v.csv", "objectives": ["epa_normalized_variant_entropy", "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": 200}}, {"pipeline_step": "feature_extraction", "input_path": "output/features/generated/grid_2obj/grid_2objectives_enve_rt10v/2_enve_rt10v", "feature_params": {"feature_set": ["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"]}, "output_path": "output/plots", "real_eventlog_path": "data/BaselineED_feat.csv", "plot_type": "boxplot"}, {"pipeline_step": "benchmark_test", "benchmark_test": "discovery", "input_path": "output/generated/grid_2obj/grid_2objectives_enve_rt10v/2_enve_rt10v", "output_path": "output", "miners": ["heu", "imf", "ilp"]}]
\ No newline at end of file
diff --git a/config_files/grid_2obj/generator_grid_2objectives_enve_rvpnot.json b/config_files/grid_2obj/generator_grid_2objectives_enve_rvpnot.json
new file mode 100644
index 0000000000000000000000000000000000000000..6800132f65332b32b30596cd47082795c0d8f44a
--- /dev/null
+++ b/config_files/grid_2obj/generator_grid_2objectives_enve_rvpnot.json
@@ -0,0 +1 @@
+[{"pipeline_step": "event_logs_generation", "output_path": "output/generated/grid_2obj", "generator_params": {"experiment": {"input_path": "data/grid_2obj/grid_2objectives_enve_rvpnot.csv", "objectives": ["epa_normalized_variant_entropy", "ratio_variants_per_number_of_traces"]}, "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": 200}}, {"pipeline_step": "feature_extraction", "input_path": "output/features/generated/grid_2obj/grid_2objectives_enve_rvpnot/2_enve_rvpnot", "feature_params": {"feature_set": ["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"]}, "output_path": "output/plots", "real_eventlog_path": "data/BaselineED_feat.csv", "plot_type": "boxplot"}, {"pipeline_step": "benchmark_test", "benchmark_test": "discovery", "input_path": "output/generated/grid_2obj/grid_2objectives_enve_rvpnot/2_enve_rvpnot", "output_path": "output", "miners": ["heu", "imf", "ilp"]}]
\ No newline at end of file
diff --git a/config_files/grid_2obj/generator_grid_2objectives_enve_sam.json b/config_files/grid_2obj/generator_grid_2objectives_enve_sam.json
new file mode 100644
index 0000000000000000000000000000000000000000..02b35a61de268c22083359b78e2c40ff0aff36c5
--- /dev/null
+++ b/config_files/grid_2obj/generator_grid_2objectives_enve_sam.json
@@ -0,0 +1 @@
+[{"pipeline_step": "event_logs_generation", "output_path": "output/shaining/grid_2obj", "generator_params": {"experiment": {"input_path": "data/grid_2obj/grid_2objectives_enve_sam.csv", "objectives": ["epa_normalized_variant_entropy", "start_activities_median"]}, "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": 200}}, {"pipeline_step": "feature_extraction", "input_path": "output/features/shaining/grid_2obj/grid_2objectives_enve_sam/2_enve_sam", "feature_params": {"feature_set": ["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"]}, "output_path": "output/plots", "real_eventlog_path": "data/BaselineED_feat.csv", "plot_type": "boxplot"}, {"pipeline_step": "benchmark_test", "benchmark_test": "discovery", "input_path": "output/shaining/grid_2obj/grid_2objectives_enve_sam/2_enve_sam", "output_path": "output", "miners": ["heu", "imf", "ilp"]}]
\ No newline at end of file
diff --git a/config_files/grid_2obj/generator_grid_2objectives_mvo_sam.json b/config_files/grid_2obj/generator_grid_2objectives_mvo_sam.json
new file mode 100644
index 0000000000000000000000000000000000000000..f983eb2b2f7f75c03ffb1265f0ef0454a83de8a9
--- /dev/null
+++ b/config_files/grid_2obj/generator_grid_2objectives_mvo_sam.json
@@ -0,0 +1 @@
+[{"pipeline_step": "event_logs_generation", "output_path": "output/shaining/grid_2obj", "generator_params": {"experiment": {"input_path": "data/grid_2obj/grid_2objectives_mvo_sam.csv", "objectives": ["mean_variant_occurrence", "start_activities_median"]}, "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": 200}}, {"pipeline_step": "feature_extraction", "input_path": "output/features/shaining/grid_2obj/grid_2objectives_mvo_sam/2_mvo_sam", "feature_params": {"feature_set": ["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"]}, "output_path": "output/plots", "real_eventlog_path": "data/BaselineED_feat.csv", "plot_type": "boxplot"}, {"pipeline_step": "benchmark_test", "benchmark_test": "discovery", "input_path": "output/shaining/grid_2obj/grid_2objectives_mvo_sam/2_mvo_sam", "output_path": "output", "miners": ["heu", "imf", "ilp"]}]
\ No newline at end of file
diff --git a/config_files/grid_2obj/generator_grid_2objectives_rmcv_rt10v.json b/config_files/grid_2obj/generator_grid_2objectives_rmcv_rt10v.json
new file mode 100644
index 0000000000000000000000000000000000000000..686fc945dd5dae6f72144bb90093ca3a32b0cb9b
--- /dev/null
+++ b/config_files/grid_2obj/generator_grid_2objectives_rmcv_rt10v.json
@@ -0,0 +1 @@
+[{"pipeline_step": "event_logs_generation", "output_path": "output/generated/grid_2obj", "generator_params": {"experiment": {"input_path": "data/grid_2obj/grid_2objectives_rmcv_rt10v.csv", "objectives": ["ratio_most_common_variant", "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": 200}}, {"pipeline_step": "feature_extraction", "input_path": "output/features/generated/grid_2obj/grid_2objectives_rmcv_rt10v/2_rmcv_rt10v", "feature_params": {"feature_set": ["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"]}, "output_path": "output/plots", "real_eventlog_path": "data/BaselineED_feat.csv", "plot_type": "boxplot"}, {"pipeline_step": "benchmark_test", "benchmark_test": "discovery", "input_path": "output/generated/grid_2obj/grid_2objectives_rmcv_rt10v/2_rmcv_rt10v", "output_path": "output", "miners": ["heu", "imf", "ilp"]}]
\ No newline at end of file
diff --git a/config_files/grid_2obj/generator_grid_2objectives_rmcv_rvpnot.json b/config_files/grid_2obj/generator_grid_2objectives_rmcv_rvpnot.json
new file mode 100644
index 0000000000000000000000000000000000000000..9891ae91aed0ca766ee60707e1965cd729694de6
--- /dev/null
+++ b/config_files/grid_2obj/generator_grid_2objectives_rmcv_rvpnot.json
@@ -0,0 +1 @@
+[{"pipeline_step": "event_logs_generation", "output_path": "output/generated/grid_2obj", "generator_params": {"experiment": {"input_path": "data/grid_2obj/grid_2objectives_rmcv_rvpnot.csv", "objectives": ["ratio_most_common_variant", "ratio_variants_per_number_of_traces"]}, "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": 200}}, {"pipeline_step": "feature_extraction", "input_path": "output/features/generated/grid_2obj/grid_2objectives_rmcv_rvpnot/2_rmcv_rvpnot", "feature_params": {"feature_set": ["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"]}, "output_path": "output/plots", "real_eventlog_path": "data/BaselineED_feat.csv", "plot_type": "boxplot"}, {"pipeline_step": "benchmark_test", "benchmark_test": "discovery", "input_path": "output/generated/grid_2obj/grid_2objectives_rmcv_rvpnot/2_rmcv_rvpnot", "output_path": "output", "miners": ["heu", "imf", "ilp"]}]
\ No newline at end of file
diff --git a/config_files/grid_2obj/generator_grid_2objectives_rt10v_rvpnot.json b/config_files/grid_2obj/generator_grid_2objectives_rt10v_rvpnot.json
new file mode 100644
index 0000000000000000000000000000000000000000..e0a3ce524433c7f3b47dc736e677ed46d08a03a2
--- /dev/null
+++ b/config_files/grid_2obj/generator_grid_2objectives_rt10v_rvpnot.json
@@ -0,0 +1 @@
+[{"pipeline_step": "event_logs_generation", "output_path": "output/generated/grid_2obj", "generator_params": {"experiment": {"input_path": "data/grid_2obj/grid_2objectives_rt10v_rvpnot.csv", "objectives": ["ratio_top_10_variants", "ratio_variants_per_number_of_traces"]}, "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": 200}}, {"pipeline_step": "feature_extraction", "input_path": "output/features/generated/grid_2obj/grid_2objectives_rt10v_rvpnot/2_rt10v_rvpnot", "feature_params": {"feature_set": ["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"]}, "output_path": "output/plots", "real_eventlog_path": "data/BaselineED_feat.csv", "plot_type": "boxplot"}, {"pipeline_step": "benchmark_test", "benchmark_test": "discovery", "input_path": "output/generated/grid_2obj/grid_2objectives_rt10v_rvpnot/2_rt10v_rvpnot", "output_path": "output", "miners": ["heu", "imf", "ilp"]}]
\ No newline at end of file
diff --git a/config_files/options/baseline.json b/config_files/options/baseline.json
deleted file mode 100644
index e14760b472c49f030b4f534fae9139b8ad10df6b..0000000000000000000000000000000000000000
--- a/config_files/options/baseline.json
+++ /dev/null
@@ -1,9 +0,0 @@
-{
-  "run_option": "baseline",
-  "plot_type": "color_map",
-  "plot_tics": true,
-  "n_components": 2,
-  "input_name": "test",
-  "save_results": false,
-  "load_results": false
-}
diff --git a/config_files/options/run_params.json b/config_files/options/run_params.json
deleted file mode 100644
index 8aebec23f03f1404a97e889eecd8d8ca6c813bac..0000000000000000000000000000000000000000
--- a/config_files/options/run_params.json
+++ /dev/null
@@ -1,9 +0,0 @@
-{
-  "run_option": "compare",
-  "plot_type": "color_map",
-  "plot_tics": true,
-  "n_components": 2,
-  "input_name": "gen20",
-  "save_results": false,
-  "load_results": true
-}
\ No newline at end of file
diff --git a/config_files/pipeline_steps/augmentation.json b/config_files/pipeline_steps/augmentation.json
new file mode 100644
index 0000000000000000000000000000000000000000..5f239cd964c160e462ad031883cb79b6db41c278
--- /dev/null
+++ b/config_files/pipeline_steps/augmentation.json
@@ -0,0 +1,12 @@
+[
+  {
+    "pipeline_step": "instance_augmentation",
+    "augmentation_params":
+    {
+        "method":"SMOTE", "no_samples":20,
+        "feature_selection": ["n_traces", "n_unique_traces", "ratio_variants_per_number_of_traces", "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/test/bpic_features.csv",
+    "output_path": "output"
+  }
+]
diff --git a/config_files/algorithm/benchmark.json b/config_files/pipeline_steps/benchmark.json
similarity index 71%
rename from config_files/algorithm/benchmark.json
rename to config_files/pipeline_steps/benchmark.json
index 891d8db31c2e6ee05451695823291cb25dd96e6c..33571691fe043daba4c91b9479bf91bbef5ee138 100644
--- a/config_files/algorithm/benchmark.json
+++ b/config_files/pipeline_steps/benchmark.json
@@ -4,6 +4,6 @@
     "benchmark_test": "discovery",
     "input_path":"data/test",
     "output_path":"output",
-    "miners" : ["inductive", "heuristics", "imf", "ilp"]
+    "miners" : ["ind", "heu", "imf", "ilp"]
   }
 ]
diff --git a/config_files/algorithm/evaluation_plotter.json b/config_files/pipeline_steps/evaluation_plotter.json
similarity index 88%
rename from config_files/algorithm/evaluation_plotter.json
rename to config_files/pipeline_steps/evaluation_plotter.json
index 98d78468d666c738782c9def42796711a47ea181..9c29ffb76564ca1d01b2784c1c4dc8ee650bfef8 100644
--- a/config_files/algorithm/evaluation_plotter.json
+++ b/config_files/pipeline_steps/evaluation_plotter.json
@@ -1,7 +1,7 @@
 [
     {
         "pipeline_step": "evaluation_plotter",
-        "input_path": "output/features/generated/34_bpic_features/",
+        "input_path": "output/features/generated/BaselineED_feat/",
         "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",
@@ -9,7 +9,7 @@
         "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/BaselineED_feat.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",
diff --git a/config_files/pipeline_steps/feature_extraction.json b/config_files/pipeline_steps/feature_extraction.json
new file mode 100644
index 0000000000000000000000000000000000000000..9002b97201a8f0b21269b6731927cdd701004f41
--- /dev/null
+++ b/config_files/pipeline_steps/feature_extraction.json
@@ -0,0 +1,12 @@
+[
+  {
+    "pipeline_step": "feature_extraction",
+    "input_path": "data/test",
+    "feature_params": {"feature_set":["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"]},
+    "output_path": "output/plots",
+    "real_eventlog_path": "data/BaselineED_feat.csv",
+    "plot_type": "boxplot",
+    "font_size": 24,
+    "boxplot_width":10
+  }
+]
diff --git a/config_files/algorithm/generation.json b/config_files/pipeline_steps/generation.json
similarity index 100%
rename from config_files/algorithm/generation.json
rename to config_files/pipeline_steps/generation.json
diff --git a/config_files/algorithm/experiment_test.json b/config_files/test/experiment_test.json
similarity index 91%
rename from config_files/algorithm/experiment_test.json
rename to config_files/test/experiment_test.json
index f66686ba93be5114405406de88581712a52048d4..ac50337eedca3113f6542667deb7aec8bd18ef66 100644
--- a/config_files/algorithm/experiment_test.json
+++ b/config_files/test/experiment_test.json
@@ -3,7 +3,7 @@
     "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",
+    "input_path": "data/test/bpic_features.csv",
     "output_path": "output"
   },
   {
@@ -39,7 +39,7 @@
     "input_path": "data/test",
     "feature_params": {"feature_set":["trace_length"]},
     "output_path": "output/plots",
-    "real_eventlog_path": "data/bpic_features.csv",
+    "real_eventlog_path": "data/BaselineED_feat.csv",
     "plot_type": "boxplot"
   },
   {
@@ -47,6 +47,6 @@
     "benchmark_test": "discovery",
     "input_path":"data/test",
     "output_path":"output",
-    "miners" : ["inductive", "heuristics", "imf", "ilp"]
+    "miners" : ["inductive", "heu", "imf", "ilp"]
   }
 ]
diff --git a/config_files/algorithm/test/generator_2bpic_2objectives_ense_enseef.json b/config_files/test/generator_2bpic_2objectives_ense_enseef.json
similarity index 85%
rename from config_files/algorithm/test/generator_2bpic_2objectives_ense_enseef.json
rename to config_files/test/generator_2bpic_2objectives_ense_enseef.json
index cad85c7ea5c156fba74eb0473534e95194c017c5..0803b6c945c827ab7a59bd996783a5166dc22c8a 100644
--- a/config_files/algorithm/test/generator_2bpic_2objectives_ense_enseef.json
+++ b/config_files/test/generator_2bpic_2objectives_ense_enseef.json
@@ -1,7 +1,7 @@
 [{"pipeline_step": "event_logs_generation",
 "output_path": "output/generated",
 "generator_params": {"experiment":
-	{"input_path": "data/2_bpic_features.csv",
+	{"input_path": "data/test/2_bpic_features.csv",
 	"objectives": ["epa_normalized_sequence_entropy",
 	"epa_normalized_sequence_entropy_exponential_forgetting"]},
 	"config_space": {"mode": [5, 20], "sequence": [0.01, 1],
@@ -12,4 +12,4 @@
 	"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"}]
\ No newline at end of file
+	"real_eventlog_path": "data/test/2_bpic_features.csv", "plot_type": "boxplot"}]
\ No newline at end of file
diff --git a/config_files/algorithm/test/generator_grid_1objectives_rt10v.json b/config_files/test/generator_grid_1objectives_rt10v.json
similarity index 82%
rename from config_files/algorithm/test/generator_grid_1objectives_rt10v.json
rename to config_files/test/generator_grid_1objectives_rt10v.json
index e406600fb361b39003b0a2ad283e719fcd7bd054..676fba8e7e9dd3d05e0f2bce2062a583ef4919c6 100644
--- a/config_files/algorithm/test/generator_grid_1objectives_rt10v.json
+++ b/config_files/test/generator_grid_1objectives_rt10v.json
@@ -1,7 +1,7 @@
 [{"pipeline_step": "event_logs_generation",
 "output_path": "output/generated/grid_1obj",
 "generator_params": {"experiment":
-	{"input_path": "data/grid_experiments/grid_1objectives_rt10v.csv",
+	{"input_path": "data/test/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],
@@ -12,5 +12,5 @@
  "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",
+ "output_path": "output/plots", "real_eventlog_path": "data/test/2_bpic_features.csv",
  "plot_type": "boxplot"}]
\ No newline at end of file
diff --git a/config_files/algorithm/test/generator_grid_2objectives_ense_enself.json b/config_files/test/generator_grid_2objectives_ense_enself.json
similarity index 90%
rename from config_files/algorithm/test/generator_grid_2objectives_ense_enself.json
rename to config_files/test/generator_grid_2objectives_ense_enself.json
index 4dd37b74fecf801b58a8d19c855e0f346efb7c46..203e311d5905b82f96e01859580ab443c831b796 100644
--- a/config_files/algorithm/test/generator_grid_2objectives_ense_enself.json
+++ b/config_files/test/generator_grid_2objectives_ense_enself.json
@@ -1,7 +1,7 @@
 [{"pipeline_step": "event_logs_generation",
 "output_path": "output/generated/grid_2obj",
 "generator_params": {"experiment":
-	{"input_path": "data/2_grid_test.csv",
+	{"input_path": "data/test/2_grid_test.csv",
 	"objectives": ["epa_normalized_sequence_entropy",
 		"epa_normalized_sequence_entropy_linear_forgetting"]},
 	"config_space": {"mode": [5, 20], "sequence": [0.01, 1],
@@ -15,5 +15,5 @@
 	"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",
+	"real_eventlog_path": "data/test/2_bpic_features.csv",
 	"plot_type": "boxplot"}]
\ No newline at end of file
diff --git a/data/GenED_bench.csv b/data/GenED_bench.csv
index a95497af413326410247b443c1112b07cf4aea8e..e52d8491d7dba99293e4ab8ca8a3c205b79759e8 100644
--- a/data/GenED_bench.csv
+++ b/data/GenED_bench.csv
@@ -1,25 +1,25 @@
 log,fitness_heu,precision_heu,fscore_heu,size_heu,pnsize_heu,cfc_heu,fitness_imf,precision_imf,fscore_imf,size_imf,pnsize_imf,cfc_imf,fitness_ilp,precision_ilp,fscore_ilp,size_ilp,pnsize_ilp,cfc_ilp
 2_ense_rmcv_genELtask_67_06_00,0.376214776532216,0.994733180959952,0.545948253307299,29.0,18.0,10.0,0.945685191537984,0.507638900441974,0.6606462982975451,28.0,22.0,8.0,,,,,,
 2_enself_rmcv_genELtask_13_01_01,0.63263614857424,0.858184089962515,0.7283484130513961,14.0,8.0,7.0,0.95097054618107,0.940229218047294,0.945569378691894,13.0,8.0,4.0,0.99997771174738,0.940229218047294,0.96918349021716,13.0,8.0,3.0
-2_rt10v_rutpt_genELtask_1_00_00,0.538653366583541,1.0,0.700162074554294,5.0,4.0,0.0,0.999932932799884,1.0,0.999966465275402,11.0,10.0,2.0,0.999955489786125,1.0,0.9999777443977612,15.0,8.0,4.0
+2_rt10v_rvpnot_genELtask_1_00_00,0.538653366583541,1.0,0.700162074554294,5.0,4.0,0.0,0.999932932799884,1.0,0.999966465275402,11.0,10.0,2.0,0.999955489786125,1.0,0.9999777443977612,15.0,8.0,4.0
 2_ense_enve_genELtask_2_00_01,0.497684336787392,1.0,0.6646051167964401,5.0,4.0,0.0,0.9999249375444692,1.0,0.999962467363588,11.0,8.0,4.0,0.999950027482386,0.888316761363636,0.940833546600142,9.0,6.0,2.0
-2_rmcv_rutpt_genELtask_35_03_01,0.621532802457298,0.776883640795058,0.690579217337543,13.0,8.0,6.0,,,,,,,,,,,,
-2_enseef_rutpt_genELtask_30_02_07,0.581125408551174,0.9817974971558592,0.7301031542449821,33.0,19.0,14.0,,,,,,,,,,,,
-2_enve_rutpt_genELtask_98_08_09,0.706094341484646,0.653694398543149,0.678884744790711,60.0,38.0,32.0,0.946299547882118,0.5820056648349491,0.720735220838514,35.0,23.0,16.0,,,,,,
+2_rmcv_rvpnot_genELtask_35_03_01,0.621532802457298,0.776883640795058,0.690579217337543,13.0,8.0,6.0,,,,,,,,,,,,
+2_enseef_rvpnot_genELtask_30_02_07,0.581125408551174,0.9817974971558592,0.7301031542449821,33.0,19.0,14.0,,,,,,,,,,,,
+2_enve_rvpnot_genELtask_98_08_09,0.706094341484646,0.653694398543149,0.678884744790711,60.0,38.0,32.0,0.946299547882118,0.5820056648349491,0.720735220838514,35.0,23.0,16.0,,,,,,
 2_ense_enve_genELtask_27_02_04,0.719424014941704,0.864942528735632,0.785500589115627,13.0,7.0,5.0,0.9999190024906072,0.8978260869565211,0.946126400507407,21.0,14.0,11.0,0.999968338733338,0.429313929313929,0.600721559748524,21.0,7.0,6.0
-2_rmcv_rutpt_genELtask_40_03_06,0.5606421352399971,0.948186528497409,0.704643718294452,53.0,31.0,23.0,0.944173528628774,0.537360890302066,0.68491412883913,43.0,32.0,18.0,0.999975738293562,0.619124797406807,0.7647576697452171,72.0,34.0,24.0
+2_rmcv_rvpnot_genELtask_40_03_06,0.5606421352399971,0.948186528497409,0.704643718294452,53.0,31.0,23.0,0.944173528628774,0.537360890302066,0.68491412883913,43.0,32.0,18.0,0.999975738293562,0.619124797406807,0.7647576697452171,72.0,34.0,24.0
 2_enve_rmcv_genELtask_57_05_01,0.7924317665331421,0.691240242844752,0.738385200063108,36.0,21.0,21.0,0.8854314359131831,0.5931142410015651,0.7103764222778941,49.0,31.0,29.0,0.999977588346547,0.154388821055487,0.267480688435321,48.0,14.0,18.0
-2_ense_rutpt_genELtask_13_01_01,0.558377492316494,0.9734048560135512,0.709666569930252,10.0,6.0,4.0,0.9998904744560232,0.98306242807825,0.991405046861986,17.0,12.0,9.0,0.999958764759687,0.5019278789659221,0.6683689264379801,17.0,6.0,5.0
-2_rmcv_rutpt_genELtask_14_01_02,0.63059293251033,0.858703884020339,0.727178886534933,14.0,8.0,7.0,0.950354219836116,0.9539963167587472,0.952171785516888,13.0,8.0,4.0,,,,,,
-2_enself_rutpt_genELtask_6_00_05,0.304990362815595,0.965804942797547,0.463585596534443,7.0,5.0,2.0,,,,,,,,,,,,
+2_ense_rvpnot_genELtask_13_01_01,0.558377492316494,0.9734048560135512,0.709666569930252,10.0,6.0,4.0,0.9998904744560232,0.98306242807825,0.991405046861986,17.0,12.0,9.0,0.999958764759687,0.5019278789659221,0.6683689264379801,17.0,6.0,5.0
+2_rmcv_rvpnot_genELtask_14_01_02,0.63059293251033,0.858703884020339,0.727178886534933,14.0,8.0,7.0,0.950354219836116,0.9539963167587472,0.952171785516888,13.0,8.0,4.0,,,,,,
+2_enself_rvpnot_genELtask_6_00_05,0.304990362815595,0.965804942797547,0.463585596534443,7.0,5.0,2.0,,,,,,,,,,,,
 2_enself_enve_genELtask_1_00_00,0.99999235550635,1.0,0.9999961777385652,11.0,8.0,2.0,0.99999235550635,1.0,0.9999961777385652,11.0,8.0,2.0,0.99999235550635,1.0,0.9999961777385652,11.0,8.0,2.0
 2_ense_enve_genELtask_75_06_08,0.376214776532216,0.994733180959952,0.545948253307299,27.0,17.0,8.0,,,,,,,,,,,,
-2_ense_rutpt_genELtask_63_05_07,0.6573958369729971,0.8956219596942321,0.758237476824929,20.0,12.0,7.0,0.9999372360394012,0.675123929698062,0.806038095867535,34.0,24.0,15.0,0.9999945106782452,0.306359918200409,0.469027745793637,23.0,9.0,6.0
+2_ense_rvpnot_genELtask_63_05_07,0.6573958369729971,0.8956219596942321,0.758237476824929,20.0,12.0,7.0,0.9999372360394012,0.675123929698062,0.806038095867535,34.0,24.0,15.0,0.9999945106782452,0.306359918200409,0.469027745793637,23.0,9.0,6.0
 2_ense_enseef_genELtask_13_01_01,0.5117056856187291,1.0,0.676991150442477,5.0,4.0,0.0,0.999924672038159,1.0,0.999962334600451,11.0,8.0,4.0,0.9999494991947552,0.8935532233883051,0.9437621478696352,9.0,6.0,2.0
 2_enve_rt10v_genELtask_87_07_09,0.596839186930711,0.9805402930402932,0.7420216614732781,11.0,7.0,4.0,0.9998912061922872,0.985859162976717,0.992825606792686,20.0,14.0,10.0,0.999963701948584,0.458929917106254,0.629125047708193,20.0,7.0,5.0
 2_enseef_rmcv_genELtask_26_02_03,0.205214360644956,0.459392779284309,0.283698413158103,38.0,22.0,12.0,0.9998919442260292,0.545462580653307,0.7058621584772831,37.0,23.0,13.0,0.9999836414087492,0.226789023577154,0.36972671482018,52.0,16.0,17.0
 2_enseef_rmcv_genELtask_7_00_06,0.99999164182521,1.0,0.99999582089514,10.0,7.0,2.0,0.99999164182521,1.0,0.99999582089514,10.0,7.0,2.0,0.99999164182521,1.0,0.99999582089514,10.0,7.0,2.0
-2_enseef_rutpt_genELtask_66_05_10,0.333333333333333,0.9333084513284652,0.491224623846678,35.0,25.0,3.0,,,,,,,,,,,,
+2_enseef_rvpnot_genELtask_66_05_10,0.333333333333333,0.9333084513284652,0.491224623846678,35.0,25.0,3.0,,,,,,,,,,,,
 2_ense_enve_genELtask_38_03_04,0.628502768118806,0.90566037735849,0.742046314726516,12.0,7.0,5.0,0.999890172988081,0.932038834951456,0.964772999505981,20.0,14.0,11.0,0.999958783900242,0.418118466898954,0.589673423575309,24.0,8.0,7.0
 2_ense_enself_genELtask_46_04_01,0.239994432129174,0.7058823529411761,0.358202753504566,60.0,37.0,26.0,0.9918439784394,0.277625570776255,0.433820962157561,42.0,27.0,18.0,0.9999768005382272,0.538726333907056,0.70021802605904,67.0,31.0,22.0
 2_ense_rmcv_genELtask_57_05_01,0.985651749748327,0.5835562302706091,0.7330872985183551,37.0,24.0,24.0,0.8686210699890091,0.5116754401187791,0.643995061971668,37.0,22.0,20.0,0.999964019789788,0.343095044153963,0.5108974113390451,35.0,13.0,13.0
@@ -31,90 +31,90 @@ log,fitness_heu,precision_heu,fscore_heu,size_heu,pnsize_heu,cfc_heu,fitness_imf
 2_enve_rt10v_genELtask_26_02_03,0.366366600647832,1.0,0.5362639872405071,9.0,6.0,4.0,0.99990642611941,1.0,0.9999532108705852,14.0,10.0,7.0,0.9999518739739792,0.7030075187969921,0.825590661554185,14.0,6.0,3.0
 2_ense_enve_genELtask_86_07_08,0.210526315789473,0.931621860478376,0.343442159381344,31.0,23.0,4.0,,,,,,,,,,,,
 2_ense_rt10v_genELtask_36_03_02,0.686074934451525,0.8489932885906041,0.758888766083978,12.0,7.0,5.0,0.999898794993806,0.8897058823529411,0.9415893708653852,20.0,14.0,11.0,0.999964849720434,0.389484978540772,0.560612236747381,24.0,8.0,7.0
-2_ense_rutpt_genELtask_74_06_07,0.376214776532216,0.994733180959952,0.545948253307299,27.0,17.0,8.0,0.945685191537984,0.507638900441974,0.6606462982975451,28.0,22.0,8.0,0.999991271385349,0.422792878091587,0.5943124793046201,34.0,19.0,8.0
+2_ense_rvpnot_genELtask_74_06_07,0.376214776532216,0.994733180959952,0.545948253307299,27.0,17.0,8.0,0.945685191537984,0.507638900441974,0.6606462982975451,28.0,22.0,8.0,0.999991271385349,0.422792878091587,0.5943124793046201,34.0,19.0,8.0
 2_ense_enve_genELtask_63_05_07,0.8345111061710161,0.750922833538407,0.790513472393637,19.0,12.0,10.0,0.951311110211227,0.806136044880785,0.872727437197594,17.0,11.0,6.0,0.999984077546128,0.6069269554658,0.7553838130811351,17.0,9.0,5.0
-2_enseef_rutpt_genELtask_36_03_02,0.411253907606807,1.0,0.5828205759291161,16.0,2.0,2.0,0.9999599158452612,0.967316199705076,0.9833672233470432,26.0,20.0,6.0,0.999970859820378,0.935089398444181,0.966442410025773,38.0,16.0,12.0
-2_enve_rutpt_genELtask_104_09_04,0.167809895660848,0.994679218254374,0.287171748678333,66.0,29.0,22.0,0.9999448679254772,0.8648118499688351,0.927482027868995,40.0,30.0,12.0,0.9999761978718432,0.7411172031692661,0.8513036262838821,72.0,32.0,22.0
+2_enseef_rvpnot_genELtask_36_03_02,0.411253907606807,1.0,0.5828205759291161,16.0,2.0,2.0,0.9999599158452612,0.967316199705076,0.9833672233470432,26.0,20.0,6.0,0.999970859820378,0.935089398444181,0.966442410025773,38.0,16.0,12.0
+2_enve_rvpnot_genELtask_104_09_04,0.167809895660848,0.994679218254374,0.287171748678333,66.0,29.0,22.0,0.9999448679254772,0.8648118499688351,0.927482027868995,40.0,30.0,12.0,0.9999761978718432,0.7411172031692661,0.8513036262838821,72.0,32.0,22.0
 2_enseef_rmcv_genELtask_25_02_02,0.6972711171232691,0.6280667320902841,0.660862122322123,35.0,19.0,19.0,0.999851179841882,0.380713051672687,0.551450465261634,50.0,32.0,30.0,0.999994308371914,0.066650169100057,0.124970955477214,52.0,8.0,28.0
 2_enself_rt10v_genELtask_46_04_01,0.25000374998125,0.9560932750759872,0.396364304255718,47.0,26.0,14.0,0.99999166673611,0.523571420946443,0.6872929150364361,28.0,31.0,2.0,,,,,,
-2_enve_rutpt_genELtask_92_08_03,0.7918985400959571,0.8607513102906881,0.824890651634898,50.0,32.0,32.0,0.999889328379646,0.784711070534819,0.8793276363340541,41.0,29.0,15.0,0.999967139367258,0.5828774525979901,0.7364693941967031,56.0,20.0,22.0
-2_rt10v_rutpt_genELtask_39_03_05,0.7106696771109791,0.911764705882352,0.7987546811417171,11.0,7.0,4.0,0.999902527561172,0.935714285714285,0.966744112722958,18.0,13.0,9.0,0.9999663876844472,0.483394833948339,0.6517341546741701,18.0,7.0,5.0
-2_enve_rutpt_genELtask_90_08_01,0.965262320562224,0.7686440923023531,0.8558053361097541,19.0,12.0,12.0,0.947355914307445,0.9556540197126232,0.9514868749990352,15.0,10.0,6.0,0.999973368540446,0.7842481606003021,0.8790694004289971,14.0,7.0,4.0
+2_enve_rvpnot_genELtask_92_08_03,0.7918985400959571,0.8607513102906881,0.824890651634898,50.0,32.0,32.0,0.999889328379646,0.784711070534819,0.8793276363340541,41.0,29.0,15.0,0.999967139367258,0.5828774525979901,0.7364693941967031,56.0,20.0,22.0
+2_rt10v_rvpnot_genELtask_39_03_05,0.7106696771109791,0.911764705882352,0.7987546811417171,11.0,7.0,4.0,0.999902527561172,0.935714285714285,0.966744112722958,18.0,13.0,9.0,0.9999663876844472,0.483394833948339,0.6517341546741701,18.0,7.0,5.0
+2_enve_rvpnot_genELtask_90_08_01,0.965262320562224,0.7686440923023531,0.8558053361097541,19.0,12.0,12.0,0.947355914307445,0.9556540197126232,0.9514868749990352,15.0,10.0,6.0,0.999973368540446,0.7842481606003021,0.8790694004289971,14.0,7.0,4.0
 2_enseef_rt10v_genELtask_30_02_07,0.7274261453928691,0.7474647920773261,0.737309340920239,31.0,17.0,17.0,0.9827215183894872,0.6358220435530331,0.772096616684245,39.0,25.0,17.0,0.999969565638089,0.49631538701181,0.6633766931770031,40.0,9.0,13.0
 2_ense_rt10v_genELtask_6_00_05,0.9999899905001912,1.0,0.999994995225048,9.0,6.0,2.0,0.9999899905001912,1.0,0.999994995225048,9.0,6.0,2.0,0.9999899905001912,1.0,0.999994995225048,9.0,6.0,2.0
 2_enve_rt10v_genELtask_83_07_05,0.63766810311605,0.82383808095952,0.7188957146869801,20.0,12.0,10.0,0.9999267492461212,0.6979087706782,0.8220556586844351,21.0,14.0,10.0,0.9999849524483452,0.468847352024922,0.6383850567451991,18.0,7.0,7.0
-2_ense_rutpt_genELtask_60_05_04,0.970546528974293,0.849847166705854,0.90619542328928,36.0,19.0,14.0,0.999948305140494,0.6450958302641651,0.7842494535469401,41.0,24.0,17.0,,,,,,
-2_rmcv_rutpt_genELtask_27_02_04,0.989858681298604,0.706400742115027,0.8244457155629641,17.0,10.0,10.0,0.7179240845917051,0.451928783382789,0.554686092572465,19.0,12.0,10.0,0.999961563039018,0.451928783382789,0.6225145221572881,15.0,7.0,6.0
+2_ense_rvpnot_genELtask_60_05_04,0.970546528974293,0.849847166705854,0.90619542328928,36.0,19.0,14.0,0.999948305140494,0.6450958302641651,0.7842494535469401,41.0,24.0,17.0,,,,,,
+2_rmcv_rvpnot_genELtask_27_02_04,0.989858681298604,0.706400742115027,0.8244457155629641,17.0,10.0,10.0,0.7179240845917051,0.451928783382789,0.554686092572465,19.0,12.0,10.0,0.999961563039018,0.451928783382789,0.6225145221572881,15.0,7.0,6.0
 2_ense_enself_genELtask_68_06_01,0.375846683049094,0.9948169438816372,0.5455731679933841,27.0,17.0,8.0,0.945754097115007,0.509464155567057,0.6622069391622061,28.0,22.0,8.0,,,,,,
-2_ense_rutpt_genELtask_64_05_08,0.925579613623396,0.335261569416499,0.492228962786787,42.0,26.0,25.0,0.9583669308182,0.255417956656346,0.403340205867791,44.0,29.0,24.0,0.9999911214204932,0.186399963043377,0.31422742553855,40.0,12.0,13.0
+2_ense_rvpnot_genELtask_64_05_08,0.925579613623396,0.335261569416499,0.492228962786787,42.0,26.0,25.0,0.9583669308182,0.255417956656346,0.403340205867791,44.0,29.0,24.0,0.9999911214204932,0.186399963043377,0.31422742553855,40.0,12.0,13.0
 2_ense_rt10v_genELtask_52_04_07,0.96283828781031,0.757696061036195,0.848037446712772,19.0,12.0,12.0,0.9491195800138532,0.934533148056838,0.941769887596226,15.0,10.0,6.0,0.9999737508925832,0.767222344888496,0.868270598626077,14.0,7.0,4.0
 2_enself_rt10v_genELtask_4_00_03,0.475717886543682,0.873407706305878,0.6159480931011121,35.0,22.0,13.0,0.968001734688637,0.474673908836393,0.636990260738525,34.0,26.0,12.0,,,,,,
-2_enve_rutpt_genELtask_40_03_06,0.9181595948322372,0.311457174638487,0.465132552472846,39.0,20.0,20.0,0.8824390996804921,0.337837837837837,0.48861255718872,57.0,38.0,37.0,,,,,,
+2_enve_rvpnot_genELtask_40_03_06,0.9181595948322372,0.311457174638487,0.465132552472846,39.0,20.0,20.0,0.8824390996804921,0.337837837837837,0.48861255718872,57.0,38.0,37.0,,,,,,
 2_enve_rmcv_genELtask_6_00_05,0.999990024323254,1.0,0.999995012136748,9.0,6.0,2.0,0.999990024323254,1.0,0.999995012136748,9.0,6.0,2.0,0.999990024323254,1.0,0.999995012136748,9.0,6.0,2.0
 2_enself_rt10v_genELtask_5_00_04,0.545070482757374,0.7510891903000411,0.631706966449578,30.0,18.0,16.0,0.878720533973232,0.341596510002819,0.491950627346905,34.0,21.0,17.0,,,,,,
-2_rt10v_rutpt_genELtask_46_04_01,0.449428102739264,0.988165680473372,0.617851067740062,12.0,7.0,5.0,0.9998956642708092,0.99715909090909,0.998525502620386,16.0,11.0,8.0,0.99994203234595,0.711967545638945,0.8317335024068031,16.0,7.0,4.0
-2_enself_rutpt_genELtask_16_01_04,0.330481074348851,0.9262709608730372,0.487152618349915,43.0,27.0,15.0,0.9374595290874732,0.595695242754066,0.7284850714466461,35.0,26.0,12.0,0.9999796345611012,0.405511155511155,0.5770267581442461,43.0,20.0,11.0
-2_ense_rutpt_genELtask_51_04_06,0.68954353062947,0.866666666666666,0.7680252889365781,68.0,41.0,30.0,0.969111685495283,0.372699386503067,0.538357952433842,39.0,24.0,14.0,0.999979621268381,0.529411764705882,0.692302808582083,83.0,42.0,31.0
+2_rt10v_rvpnot_genELtask_46_04_01,0.449428102739264,0.988165680473372,0.617851067740062,12.0,7.0,5.0,0.9998956642708092,0.99715909090909,0.998525502620386,16.0,11.0,8.0,0.99994203234595,0.711967545638945,0.8317335024068031,16.0,7.0,4.0
+2_enself_rvpnot_genELtask_16_01_04,0.330481074348851,0.9262709608730372,0.487152618349915,43.0,27.0,15.0,0.9374595290874732,0.595695242754066,0.7284850714466461,35.0,26.0,12.0,0.9999796345611012,0.405511155511155,0.5770267581442461,43.0,20.0,11.0
+2_ense_rvpnot_genELtask_51_04_06,0.68954353062947,0.866666666666666,0.7680252889365781,68.0,41.0,30.0,0.969111685495283,0.372699386503067,0.538357952433842,39.0,24.0,14.0,0.999979621268381,0.529411764705882,0.692302808582083,83.0,42.0,31.0
 2_ense_rmcv_genELtask_15_01_03,0.5868420246057651,1.0,0.7396350934826801,12.0,5.0,3.0,0.9999033128659012,1.0,0.999951654095737,13.0,7.0,4.0,0.999928282347008,0.9939161784587652,0.996913166156935,26.0,10.0,7.0
 2_ense_enve_genELtask_50_04_05,0.182506660542554,0.6640419947506561,0.286320428634979,35.0,22.0,12.0,0.937168118712648,0.450389105058365,0.6083933736860121,48.0,34.0,23.0,0.999984410889412,0.428571428571428,0.599997193929473,42.0,17.0,8.0
-2_rt10v_rutpt_genELtask_36_03_02,0.9999684222991192,0.9746835443037972,0.9871641003301672,27.0,14.0,11.0,0.999933996438877,0.9746835443037972,0.987147325099058,25.0,14.0,9.0,0.9999605278738992,0.798962386511024,0.8882324455132301,20.0,9.0,4.0
+2_rt10v_rvpnot_genELtask_36_03_02,0.9999684222991192,0.9746835443037972,0.9871641003301672,27.0,14.0,11.0,0.999933996438877,0.9746835443037972,0.987147325099058,25.0,14.0,9.0,0.9999605278738992,0.798962386511024,0.8882324455132301,20.0,9.0,4.0
 2_enseef_enve_genELtask_43_03_09,0.214876579245927,0.995915307256792,0.353485808465591,57.0,27.0,16.0,0.9999585932664372,0.918682488970028,0.957599060954287,36.0,30.0,8.0,0.9999805165548252,0.918682488970028,0.957609113455355,54.0,29.0,15.0
 2_ense_rt10v_genELtask_74_06_07,0.7559297979920231,0.842836745090442,0.797021195137192,31.0,18.0,16.0,0.88018802018927,0.759696783247304,0.8155158291476461,37.0,23.0,19.0,,,,,,
 2_enseef_rt10v_genELtask_35_03_01,0.391156045341259,0.55909169103733,0.460284379487729,101.0,53.0,52.0,0.936932843595019,0.222303397703602,0.359345829833517,47.0,36.0,22.0,,,,,,
 2_rmcv_rt10v_genELtask_29_02_06,0.9660408814625492,0.411463412761186,0.577116862169062,55.0,35.0,35.0,0.7548793166177751,0.56297558205565,0.644955105577183,70.0,47.0,45.0,,,,,,
 2_enseef_enself_genELtask_59_05_03,0.272726033063486,0.9775360105270572,0.426469826376751,51.0,29.0,15.0,,,,,,,,,,,,
-2_rt10v_rutpt_genELtask_34_03_00,0.487808909827258,1.0,0.655741347702905,15.0,5.0,3.0,0.999924035423212,1.0,0.999962016268897,18.0,7.0,3.0,0.999924035423212,1.0,0.999962016268897,18.0,7.0,3.0
+2_rt10v_rvpnot_genELtask_34_03_00,0.487808909827258,1.0,0.655741347702905,15.0,5.0,3.0,0.999924035423212,1.0,0.999962016268897,18.0,7.0,3.0,0.999924035423212,1.0,0.999962016268897,18.0,7.0,3.0
 2_enve_rmcv_genELtask_70_06_03,0.347586684851292,0.938228122460038,0.507251278902368,46.0,29.0,16.0,0.943213662107766,0.580070754716981,0.718356539061433,38.0,29.0,12.0,0.9999801548798732,0.395704287667927,0.567028509892201,47.0,22.0,12.0
 2_enve_rmcv_genELtask_81_07_03,0.8052215201204641,0.933556672250139,0.8646530374561281,70.0,36.0,32.0,0.988543209867918,0.6580958999305071,0.79016249455882,46.0,24.0,20.0,0.9999666386676,0.533369803063457,0.6956751233721911,77.0,32.0,28.0
-2_enseef_rutpt_genELtask_33_02_10,0.095011168899106,0.911547911547911,0.172085740948745,24.0,13.0,12.0,0.989297973164528,0.204235093817614,0.338573551001341,57.0,47.0,23.0,,,,,,
-2_rt10v_rutpt_genELtask_82_07_04,0.7559297979920231,0.842836745090442,0.797021195137192,31.0,18.0,16.0,0.88018802018927,0.759696783247304,0.8155158291476461,37.0,23.0,19.0,,,,,,
-2_enseef_rutpt_genELtask_44_03_10,0.091146671989072,0.792651296829971,0.163493310242416,58.0,33.0,20.0,0.9999922963848312,0.359577374300167,0.528953553477383,29.0,34.0,2.0,,,,,,
+2_enseef_rvpnot_genELtask_33_02_10,0.095011168899106,0.911547911547911,0.172085740948745,24.0,13.0,12.0,0.989297973164528,0.204235093817614,0.338573551001341,57.0,47.0,23.0,,,,,,
+2_rt10v_rvpnot_genELtask_82_07_04,0.7559297979920231,0.842836745090442,0.797021195137192,31.0,18.0,16.0,0.88018802018927,0.759696783247304,0.8155158291476461,37.0,23.0,19.0,,,,,,
+2_enseef_rvpnot_genELtask_44_03_10,0.091146671989072,0.792651296829971,0.163493310242416,58.0,33.0,20.0,0.9999922963848312,0.359577374300167,0.528953553477383,29.0,34.0,2.0,,,,,,
 2_ense_enve_genELtask_61_05_05,0.713002366339327,0.613043478260869,0.659255413300855,51.0,28.0,26.0,0.9137228361650772,0.321773220747889,0.475940879323169,84.0,56.0,52.0,,,,,,
-2_rmcv_rutpt_genELtask_20_01_08,0.5782801378586421,0.8451025056947601,0.6866825244936531,21.0,10.0,8.0,0.9998891453152512,0.48886018727801,0.656666618260859,33.0,21.0,18.0,0.9999939483761052,0.15010906206623,0.261034276581731,35.0,7.0,14.0
-2_ense_rutpt_genELtask_73_06_06,0.343661633801327,0.93459966338062,0.5025358242100271,10.0,7.0,4.0,,,,,,,,,,,,
-2_ense_rutpt_genELtask_43_03_09,0.523799546675301,0.810185185185185,0.63625110930794,34.0,19.0,13.0,0.999961906213096,0.6947368421052631,0.8198629728586311,25.0,23.0,6.0,0.9999809527437572,0.673469387755102,0.8048718790308921,42.0,21.0,9.0
+2_rmcv_rvpnot_genELtask_20_01_08,0.5782801378586421,0.8451025056947601,0.6866825244936531,21.0,10.0,8.0,0.9998891453152512,0.48886018727801,0.656666618260859,33.0,21.0,18.0,0.9999939483761052,0.15010906206623,0.261034276581731,35.0,7.0,14.0
+2_ense_rvpnot_genELtask_73_06_06,0.343661633801327,0.93459966338062,0.5025358242100271,10.0,7.0,4.0,,,,,,,,,,,,
+2_ense_rvpnot_genELtask_43_03_09,0.523799546675301,0.810185185185185,0.63625110930794,34.0,19.0,13.0,0.999961906213096,0.6947368421052631,0.8198629728586311,25.0,23.0,6.0,0.9999809527437572,0.673469387755102,0.8048718790308921,42.0,21.0,9.0
 2_ense_rt10v_genELtask_60_05_04,0.594380762823812,0.9383697813121272,0.727775238522635,20.0,12.0,10.0,0.9998871822436772,0.597046083189812,0.7476564471568381,28.0,22.0,15.0,0.99999176735647,0.247860470154912,0.397256056698389,29.0,7.0,8.0
 2_ense_rt10v_genELtask_58_05_02,0.7518808197684701,0.7228979375991531,0.7371045876754511,36.0,20.0,19.0,0.8856271723435871,0.36583522297808,0.517784018570507,43.0,25.0,21.0,0.99999208807758,0.215909563193549,0.355140326864629,36.0,11.0,12.0
-2_ense_rutpt_genELtask_24_02_01,0.5573481288122081,0.9546835135463492,0.7038094375497791,10.0,6.0,4.0,0.9998916267014312,0.97099307877751,0.985230486954072,17.0,12.0,9.0,0.999960205665284,0.493147438682412,0.6605388647964611,17.0,6.0,5.0
+2_ense_rvpnot_genELtask_24_02_01,0.5573481288122081,0.9546835135463492,0.7038094375497791,10.0,6.0,4.0,0.9998916267014312,0.97099307877751,0.985230486954072,17.0,12.0,9.0,0.999960205665284,0.493147438682412,0.6605388647964611,17.0,6.0,5.0
 2_enself_enve_genELtask_9_00_08,0.9652359707947332,0.768362583466288,0.8556204691697781,19.0,12.0,12.0,0.9474799728159452,0.955149640211399,0.9512993479615832,15.0,10.0,6.0,0.999973396145827,0.7837486389796231,0.8787555155027321,14.0,7.0,4.0
 2_enve_rmcv_genELtask_58_05_02,0.415008541816268,0.490962099125364,0.449801473993236,47.0,26.0,19.0,0.8575302629017151,0.6815398075240591,0.7594729071203481,47.0,33.0,29.0,0.9999852099317732,0.212445123115098,0.350439897859302,40.0,14.0,12.0
 2_enseef_rmcv_genELtask_24_02_01,0.77567212523937,0.557521064367919,0.648748511821253,20.0,12.0,11.0,0.962423342740302,0.685453696831505,0.8006624552153411,21.0,14.0,10.0,0.999983982079033,0.461944639247555,0.6319559424668221,19.0,8.0,7.0
 2_ense_rmcv_genELtask_36_03_02,0.6965863019071621,0.8709677419354831,0.7740775519905101,13.0,7.0,5.0,0.99991994157317,0.902439024390243,0.9486819182778732,21.0,14.0,11.0,0.999969621176065,0.427355623100303,0.598802049151352,21.0,7.0,6.0
-2_rt10v_rutpt_genELtask_79_07_01,0.305577114979487,1.0,0.468110403397027,4.0,3.0,0.0,0.999969354526046,0.99458561940514,0.997270221033034,13.0,7.0,4.0,0.999969354526046,0.99458561940514,0.997270221033034,13.0,7.0,4.0
+2_rt10v_rvpnot_genELtask_79_07_01,0.305577114979487,1.0,0.468110403397027,4.0,3.0,0.0,0.999969354526046,0.99458561940514,0.997270221033034,13.0,7.0,4.0,0.999969354526046,0.99458561940514,0.997270221033034,13.0,7.0,4.0
 2_ense_enseef_genELtask_48_04_03,0.655072463768116,1.0,0.7915936952714531,21.0,2.0,2.0,0.994528764202692,0.968615904619812,0.981401313828198,39.0,26.0,12.0,0.9999344970445412,0.762408546724309,0.8651648263449251,59.0,21.0,19.0
-2_ense_rutpt_genELtask_52_04_07,0.257045973964673,0.268716310640846,0.262751619168329,57.0,31.0,28.0,0.982293938857566,0.300744073369095,0.460499342346232,61.0,40.0,37.0,0.9999904556225492,0.118044105523495,0.211161591901052,66.0,20.0,21.0
-2_enve_rutpt_genELtask_68_06_01,0.612758862758862,0.73631531718092,0.6688790625402841,11.0,2.0,4.0,0.99995724197451,0.996940869240813,0.998446777448246,13.0,6.0,4.0,0.999959583943106,0.734713515891344,0.8470573755230311,14.0,7.0,5.0
-2_enself_rutpt_genELtask_19_01_07,0.8802105407158861,0.477698355968548,0.6192979944867101,29.0,17.0,16.0,0.937223459978035,0.449635384720234,0.6077169751010041,36.0,24.0,21.0,0.999988489711354,0.299522207142724,0.460971595356243,34.0,11.0,9.0
-2_enself_rutpt_genELtask_15_01_03,0.553448813982521,0.482353832172069,0.515461429504232,27.0,2.0,12.0,0.954121079554186,0.683627547963718,0.796536712797817,34.0,21.0,13.0,0.9999728687179172,0.575028126757922,0.7301741740599811,39.0,11.0,12.0
+2_ense_rvpnot_genELtask_52_04_07,0.257045973964673,0.268716310640846,0.262751619168329,57.0,31.0,28.0,0.982293938857566,0.300744073369095,0.460499342346232,61.0,40.0,37.0,0.9999904556225492,0.118044105523495,0.211161591901052,66.0,20.0,21.0
+2_enve_rvpnot_genELtask_68_06_01,0.612758862758862,0.73631531718092,0.6688790625402841,11.0,2.0,4.0,0.99995724197451,0.996940869240813,0.998446777448246,13.0,6.0,4.0,0.999959583943106,0.734713515891344,0.8470573755230311,14.0,7.0,5.0
+2_enself_rvpnot_genELtask_19_01_07,0.8802105407158861,0.477698355968548,0.6192979944867101,29.0,17.0,16.0,0.937223459978035,0.449635384720234,0.6077169751010041,36.0,24.0,21.0,0.999988489711354,0.299522207142724,0.460971595356243,34.0,11.0,9.0
+2_enself_rvpnot_genELtask_15_01_03,0.553448813982521,0.482353832172069,0.515461429504232,27.0,2.0,12.0,0.954121079554186,0.683627547963718,0.796536712797817,34.0,21.0,13.0,0.9999728687179172,0.575028126757922,0.7301741740599811,39.0,11.0,12.0
 2_ense_rt10v_genELtask_49_04_04,0.32819794712184,0.900523560209424,0.481069114575047,68.0,40.0,28.0,0.996464599156002,0.30859375,0.471247507967231,54.0,33.0,23.0,0.999978120136858,0.5835156819839531,0.736981621221618,75.0,35.0,25.0
-2_ense_rutpt_genELtask_36_03_02,0.964589734525874,0.765441225888836,0.853553220455989,19.0,12.0,12.0,0.948454201632237,0.948423937871174,0.948439069510284,15.0,10.0,6.0,0.9999735343110272,0.7788563443159541,0.8756719692018531,14.0,7.0,4.0
+2_ense_rvpnot_genELtask_36_03_02,0.964589734525874,0.765441225888836,0.853553220455989,19.0,12.0,12.0,0.948454201632237,0.948423937871174,0.948439069510284,15.0,10.0,6.0,0.9999735343110272,0.7788563443159541,0.8756719692018531,14.0,7.0,4.0
 2_ense_enself_genELtask_2_00_01,0.9999916385568592,1.0,0.999995819260951,10.0,7.0,2.0,0.9999916385568592,1.0,0.999995819260951,10.0,7.0,2.0,0.9999916385568592,1.0,0.999995819260951,10.0,7.0,2.0
 2_ense_rt10v_genELtask_53_04_08,0.375438596491228,1.0,0.545918367346938,5.0,4.0,0.0,0.99995714450424,0.9649410439851612,0.982137086400542,16.0,9.0,5.0,0.9999716825400812,0.9649410439851612,0.9821440985953412,15.0,8.0,4.0
-2_enve_rutpt_genELtask_79_07_01,0.7245959005321511,1.0,0.84030804005572,6.0,5.0,0.0,0.999983389858438,0.9920143222202412,0.995982915750192,12.0,9.0,2.0,0.999983389858438,0.9920143222202412,0.995982915750192,12.0,9.0,2.0
+2_enve_rvpnot_genELtask_79_07_01,0.7245959005321511,1.0,0.84030804005572,6.0,5.0,0.0,0.999983389858438,0.9920143222202412,0.995982915750192,12.0,9.0,2.0,0.999983389858438,0.9920143222202412,0.995982915750192,12.0,9.0,2.0
 2_ense_enve_genELtask_51_04_06,0.448062957547123,0.9944289693593312,0.617773696712791,8.0,5.0,2.0,0.9998886471346212,0.8052783575901411,0.8920932915681831,39.0,23.0,18.0,0.99995956302415,0.285498504739216,0.444179343072323,24.0,5.0,5.0
 2_rmcv_rt10v_genELtask_37_03_03,0.9002713692269521,1.0,0.947518742644837,19.0,11.0,7.0,0.999929916709381,0.950678175092478,0.97468225671055,25.0,17.0,11.0,0.999966570032556,0.6698523023457861,0.8022785228808441,17.0,9.0,3.0
 2_ense_enve_genELtask_31_02_08,0.88321907198791,1.0,0.937988665392594,43.0,21.0,16.0,0.999962076127302,1.0,0.9999810377040892,33.0,20.0,7.0,0.999976675301335,1.0,0.9999883375146552,49.0,16.0,9.0
 2_enve_rt10v_genELtask_85_07_07,0.971842610813648,0.605565903363646,0.746179246853621,22.0,14.0,14.0,0.936995854755172,0.7352531013237861,0.8239551959436151,18.0,12.0,8.0,0.9999795552857752,0.5052420046913441,0.6713053926368471,17.0,7.0,6.0
-2_ense_rutpt_genELtask_48_04_03,0.9650516985908292,0.7514651617104401,0.844970122359544,19.0,12.0,12.0,0.9504255156455352,0.9188994286348252,0.934396629070668,15.0,10.0,6.0,,,,,,
+2_ense_rvpnot_genELtask_48_04_03,0.9650516985908292,0.7514651617104401,0.844970122359544,19.0,12.0,12.0,0.9504255156455352,0.9188994286348252,0.934396629070668,15.0,10.0,6.0,,,,,,
 2_enseef_rmcv_genELtask_37_03_03,0.232737151913063,0.8909987219280621,0.36906983166942,56.0,27.0,17.0,0.9999596189724392,0.7639950910978941,0.8661948471008121,35.0,27.0,9.0,0.999979809078554,0.7639950910978941,0.866202421878684,54.0,27.0,15.0
 2_enself_rt10v_genELtask_18_01_06,0.350082811041695,0.937825253355245,0.509844623287187,48.0,30.0,18.0,0.943703763058128,0.578884793722506,0.717588157063451,38.0,29.0,12.0,0.9999801320524332,0.394602479941648,0.565896400265661,47.0,22.0,12.0
 2_ense_enve_genELtask_13_01_01,0.262561550272674,1.0,0.415918812379434,5.0,4.0,0.0,0.9999143769922932,1.0,0.9999571866632432,12.0,8.0,4.0,,,,,,
 2_enseef_enve_genELtask_32_02_09,0.262397526131843,1.0,0.415712991668899,57.0,23.0,19.0,0.99995409326936,0.995614002034741,0.9977793280957572,32.0,23.0,9.0,0.9999770461078112,0.995614002034741,0.997790754518516,49.0,22.0,14.0
-2_enve_rutpt_genELtask_38_03_04,0.999946849528312,0.8897058823529411,0.9416106769839412,15.0,8.0,7.0,0.999845969258706,0.8897058823529411,0.9415659480713512,19.0,14.0,11.0,0.9999325346723592,0.314285714285714,0.478253153380238,19.0,6.0,7.0
-2_ense_rutpt_genELtask_55_04_10,0.922038199798948,0.372353673723536,0.5304797071605081,49.0,28.0,28.0,0.966297488204652,0.17986838898367,0.303283104028023,41.0,25.0,22.0,0.9999950000249992,0.122777591412277,0.218703196824962,63.0,23.0,19.0
+2_enve_rvpnot_genELtask_38_03_04,0.999946849528312,0.8897058823529411,0.9416106769839412,15.0,8.0,7.0,0.999845969258706,0.8897058823529411,0.9415659480713512,19.0,14.0,11.0,0.9999325346723592,0.314285714285714,0.478253153380238,19.0,6.0,7.0
+2_ense_rvpnot_genELtask_55_04_10,0.922038199798948,0.372353673723536,0.5304797071605081,49.0,28.0,28.0,0.966297488204652,0.17986838898367,0.303283104028023,41.0,25.0,22.0,0.9999950000249992,0.122777591412277,0.218703196824962,63.0,23.0,19.0
 2_enve_rmcv_genELtask_48_04_03,0.310591500770047,0.962264150943396,0.46960716460881,27.0,12.0,10.0,0.9543865417117092,0.771764705882352,0.8534151913847,32.0,21.0,18.0,0.99995442068222,0.144990176817288,0.253258661738882,45.0,11.0,19.0
 2_ense_enve_genELtask_85_07_07,0.355510034247671,0.9505055016444032,0.51747354323988,48.0,28.0,13.0,,,,,,,,,,,,
 2_rmcv_rt10v_genELtask_38_03_04,0.117096479831115,0.5127401019208151,0.190652822470773,46.0,29.0,16.0,0.948058531506299,0.584840055632823,0.723417203148606,53.0,36.0,22.0,0.99998321271705,0.485884101040118,0.6539964098646821,51.0,21.0,9.0
-2_rt10v_rutpt_genELtask_92_08_03,0.553448813982521,0.482353832172069,0.515461429504232,27.0,2.0,12.0,0.954121079554186,0.683627547963718,0.796536712797817,34.0,21.0,13.0,0.9999728687179172,0.575028126757922,0.7301741740599811,39.0,11.0,12.0
+2_rt10v_rvpnot_genELtask_92_08_03,0.553448813982521,0.482353832172069,0.515461429504232,27.0,2.0,12.0,0.954121079554186,0.683627547963718,0.796536712797817,34.0,21.0,13.0,0.9999728687179172,0.575028126757922,0.7301741740599811,39.0,11.0,12.0
 2_ense_enve_genELtask_14_01_02,0.513849454224241,1.0,0.678864668861751,9.0,5.0,2.0,0.9999321594502372,1.0,0.999966078574494,14.0,9.0,5.0,0.9999553538686292,0.9052540913006032,0.950251089194146,12.0,7.0,3.0
 2_ense_rt10v_genELtask_21_01_09,0.439765122158905,0.9838318512530312,0.607833456140563,14.0,9.0,6.0,0.9999010667301392,0.968225419664268,0.983808344280905,19.0,12.0,9.0,0.9999464729531912,0.6914185139377791,0.817542647158023,19.0,7.0,4.0
 2_enseef_rt10v_genELtask_20_01_08,0.403107621057632,0.995612347009468,0.5738660115837271,20.0,12.0,6.0,0.9999537228773212,0.995612347009468,0.9977783125879912,21.0,16.0,6.0,0.999973559838885,0.8953963309103491,0.9447999156862832,22.0,13.0,5.0
-2_enve_rutpt_genELtask_57_05_01,0.7272727272727271,1.0,0.842105263157894,6.0,5.0,0.0,0.999950003749718,1.0,0.9999750012499372,12.0,11.0,2.0,0.999962501406197,1.0,0.999981250351555,16.0,9.0,4.0
+2_enve_rvpnot_genELtask_57_05_01,0.7272727272727271,1.0,0.842105263157894,6.0,5.0,0.0,0.999950003749718,1.0,0.9999750012499372,12.0,11.0,2.0,0.999962501406197,1.0,0.999981250351555,16.0,9.0,4.0
 2_enve_rt10v_genELtask_90_08_01,0.222222222222222,0.949402480270574,0.360146604113297,59.0,28.0,21.0,,,,,,,,,,,,
 2_enve_rt10v_genELtask_88_07_10,0.528845874386026,1.0,0.6918236602475141,6.0,5.0,0.0,0.9999222564581572,0.997746185852981,0.998833035953066,14.0,10.0,5.0,,,,,,
-2_rt10v_rutpt_genELtask_91_08_02,0.984592744119528,0.6909480091640321,0.812039211882551,35.0,22.0,22.0,0.961535152634132,0.6281265964201991,0.7598670636967211,37.0,22.0,20.0,0.9999603921122112,0.396498619700312,0.567840390406966,33.0,11.0,15.0
+2_rt10v_rvpnot_genELtask_91_08_02,0.984592744119528,0.6909480091640321,0.812039211882551,35.0,22.0,22.0,0.961535152634132,0.6281265964201991,0.7598670636967211,37.0,22.0,20.0,0.9999603921122112,0.396498619700312,0.567840390406966,33.0,11.0,15.0
 2_ense_enself_genELtask_81_07_03,0.260869565217391,0.956599722803489,0.409945049505911,39.0,27.0,7.0,,,,,,,,,,,,
-2_enve_rutpt_genELtask_65_05_09,0.729043111348324,0.6015037593984961,0.659160803547576,34.0,20.0,18.0,0.9797901201625492,0.221002621231979,0.360655385977178,39.0,28.0,22.0,0.9999908509528732,0.206679944844492,0.342559138150624,43.0,14.0,11.0
+2_enve_rvpnot_genELtask_65_05_09,0.729043111348324,0.6015037593984961,0.659160803547576,34.0,20.0,18.0,0.9797901201625492,0.221002621231979,0.360655385977178,39.0,28.0,22.0,0.9999908509528732,0.206679944844492,0.342559138150624,43.0,14.0,11.0
 2_enve_rt10v_genELtask_56_05_00,0.564754460993953,1.0,0.721844193542306,6.0,5.0,0.0,0.999939568466039,1.0,0.9999697833199992,12.0,11.0,2.0,0.9999598925132572,1.0,0.999979945854468,16.0,9.0,4.0
 2_ense_rmcv_genELtask_47_04_02,0.704876993006993,0.9093149334647612,0.794149896905968,64.0,39.0,33.0,0.9999457747435052,0.835351882160392,0.910268241065006,40.0,28.0,13.0,0.9999752457108292,0.8827395364925631,0.937707286570242,58.0,23.0,16.0
 2_rmcv_rt10v_genELtask_12_01_00,0.246560987169968,0.644859813084112,0.356727758775521,110.0,64.0,47.0,0.999953538524817,0.096707208543458,0.176358487604131,51.0,47.0,25.0,0.9999972477139972,0.294223826714801,0.45467196098091,114.0,66.0,53.0
@@ -127,10 +127,10 @@ log,fitness_heu,precision_heu,fscore_heu,size_heu,pnsize_heu,cfc_heu,fitness_imf
 2_enseef_rt10v_genELtask_46_04_01,0.124712160577651,1.0,0.221767248455106,57.0,28.0,19.0,0.999984232214168,0.378395188579726,0.549034926693109,35.0,34.0,9.0,,,,,,
 2_ense_enve_genELtask_3_00_02,0.449740927490259,1.0,0.620443168792696,8.0,4.0,2.0,0.9999003956395072,1.0,0.999950195339372,17.0,12.0,7.0,0.99994602477269,0.6827635714562881,0.811461134669153,16.0,7.0,4.0
 2_enseef_enve_genELtask_64_05_08,0.31578947368421,0.961977921105394,0.475489518108612,44.0,25.0,13.0,0.999990625087889,0.5850692803303561,0.7382229445806281,25.0,28.0,2.0,,,,,,
-2_enve_rutpt_genELtask_74_06_07,0.6910161070216141,0.8949356463346391,0.779866026890566,20.0,12.0,7.0,0.999939195755478,0.6643152746425881,0.7982852301370761,33.0,21.0,16.0,0.999994206024542,0.374059951276347,0.544458574641551,21.0,9.0,4.0
-2_rt10v_rutpt_genELtask_52_04_07,0.775730598925458,0.760067607289829,0.7678192328141431,41.0,25.0,20.0,0.848949124771983,0.312437464517484,0.456770406157989,37.0,23.0,19.0,,,,,,
-2_enve_rutpt_genELtask_63_05_07,0.097582824637883,0.267243617481609,0.142963250738059,50.0,27.0,25.0,0.9208140141769192,0.344112328475694,0.5009990603041631,60.0,38.0,35.0,0.9999915818403032,0.124812603227035,0.221925832409909,61.0,16.0,19.0
-2_rt10v_rutpt_genELtask_71_06_04,0.316609088642139,0.978322875071306,0.478397201634953,41.0,26.0,13.0,0.9786643281014472,0.680606377417668,0.8028649946527351,33.0,25.0,10.0,,,,,,
+2_enve_rvpnot_genELtask_74_06_07,0.6910161070216141,0.8949356463346391,0.779866026890566,20.0,12.0,7.0,0.999939195755478,0.6643152746425881,0.7982852301370761,33.0,21.0,16.0,0.999994206024542,0.374059951276347,0.544458574641551,21.0,9.0,4.0
+2_rt10v_rvpnot_genELtask_52_04_07,0.775730598925458,0.760067607289829,0.7678192328141431,41.0,25.0,20.0,0.848949124771983,0.312437464517484,0.456770406157989,37.0,23.0,19.0,,,,,,
+2_enve_rvpnot_genELtask_63_05_07,0.097582824637883,0.267243617481609,0.142963250738059,50.0,27.0,25.0,0.9208140141769192,0.344112328475694,0.5009990603041631,60.0,38.0,35.0,0.9999915818403032,0.124812603227035,0.221925832409909,61.0,16.0,19.0
+2_rt10v_rvpnot_genELtask_71_06_04,0.316609088642139,0.978322875071306,0.478397201634953,41.0,26.0,13.0,0.9786643281014472,0.680606377417668,0.8028649946527351,33.0,25.0,10.0,,,,,,
 2_enve_rt10v_genELtask_74_06_07,0.8237121508566251,0.724998929290333,0.7712096014161961,36.0,22.0,22.0,0.961368055017889,0.483060135328314,0.6430206580860921,40.0,24.0,22.0,0.999965710364044,0.31517177055806,0.479282155675176,36.0,11.0,14.0
 2_enve_rt10v_genELtask_47_04_02,0.261192567952045,0.331768388106416,0.292280415327318,23.0,12.0,10.0,0.886452862532877,0.594684385382059,0.711830961604183,38.0,25.0,22.0,0.999987395116868,0.186889818688981,0.314922994208278,32.0,8.0,10.0
 2_ense_enseef_genELtask_59_05_03,0.101800052423948,1.0,0.184788614231756,10.0,4.0,4.0,0.999937954635036,0.8871043343442391,0.9401477633146952,36.0,26.0,12.0,0.9999664512059052,0.7971452228382171,0.887110679976254,47.0,20.0,16.0
@@ -142,42 +142,42 @@ log,fitness_heu,precision_heu,fscore_heu,size_heu,pnsize_heu,cfc_heu,fitness_imf
 2_enve_rt10v_genELtask_48_04_03,0.062498278077038,0.310975609756097,0.104079244985377,46.0,24.0,23.0,0.9335701975557812,0.283549783549783,0.434983619618205,59.0,39.0,36.0,0.9999903744242032,0.099413399197283,0.18084791897449,53.0,14.0,19.0
 2_ense_enve_genELtask_1_00_00,0.9999916385568592,1.0,0.999995819260951,10.0,7.0,2.0,0.9999916385568592,1.0,0.999995819260951,10.0,7.0,2.0,0.9999916385568592,1.0,0.999995819260951,10.0,7.0,2.0
 2_ense_enve_genELtask_74_06_07,0.376214776532216,0.994733180959952,0.545948253307299,27.0,17.0,8.0,0.945685191537984,0.507638900441974,0.6606462982975451,28.0,22.0,8.0,,,,,,
-2_ense_rutpt_genELtask_88_07_10,0.333333333333333,0.933384683612258,0.49123518211412,35.0,25.0,3.0,,,,,,,,,,,,
+2_ense_rvpnot_genELtask_88_07_10,0.333333333333333,0.933384683612258,0.49123518211412,35.0,25.0,3.0,,,,,,,,,,,,
 2_enself_rmcv_genELtask_15_01_03,0.303335929965216,0.790966386554621,0.438505010571132,78.0,46.0,32.0,0.9872264432186152,0.363284489908764,0.531123511926861,51.0,35.0,26.0,0.999976240472256,0.5228810187027451,0.6866941627607771,79.0,34.0,25.0
 2_rmcv_rt10v_genELtask_54_04_09,0.441785558219033,0.9966820542412,0.612206675889985,12.0,8.0,6.0,0.994610654336551,0.998473282442748,0.996538225472552,21.0,14.0,11.0,0.9999450953973992,0.6907921928817451,0.8171041947196591,16.0,7.0,4.0
 2_ense_enseef_genELtask_47_04_02,0.344364190296313,0.954362416107382,0.506108428135021,52.0,31.0,20.0,0.945322648096083,0.569013622189397,0.710412178174166,38.0,29.0,12.0,0.9999806295142952,0.440533672172808,0.611622027362969,54.0,24.0,13.0
-2_rt10v_rutpt_genELtask_62_05_06,0.888929761186602,0.58980441405614,0.7091128422018961,33.0,20.0,20.0,,,,,,,,,,,,
-2_rt10v_rutpt_genELtask_90_08_01,0.143881523107091,1.0,0.251567177545224,4.0,3.0,0.0,0.9999632697337992,0.9800944413233932,0.989929168953756,17.0,9.0,6.0,0.9999705221321772,0.8644240297827891,0.9272699788976012,15.0,7.0,4.0
+2_rt10v_rvpnot_genELtask_62_05_06,0.888929761186602,0.58980441405614,0.7091128422018961,33.0,20.0,20.0,,,,,,,,,,,,
+2_rt10v_rvpnot_genELtask_90_08_01,0.143881523107091,1.0,0.251567177545224,4.0,3.0,0.0,0.9999632697337992,0.9800944413233932,0.989929168953756,17.0,9.0,6.0,0.9999705221321772,0.8644240297827891,0.9272699788976012,15.0,7.0,4.0
 2_enve_rt10v_genELtask_60_05_04,0.901677784888781,0.654304635761589,0.758327275149257,40.0,22.0,19.0,0.950722616003376,0.6186107470511141,0.7495249149352561,45.0,30.0,23.0,0.9999848278164052,0.181861461556772,0.307753561451281,57.0,18.0,16.0
-2_rt10v_rutpt_genELtask_27_02_04,0.8999793932104021,0.903420523138833,0.901696675104934,31.0,17.0,12.0,0.9999342879672692,0.663736263736263,0.7978654759472441,37.0,23.0,16.0,0.9999828574367292,0.375855631611698,0.546356554892866,26.0,12.0,5.0
+2_rt10v_rvpnot_genELtask_27_02_04,0.8999793932104021,0.903420523138833,0.901696675104934,31.0,17.0,12.0,0.9999342879672692,0.663736263736263,0.7978654759472441,37.0,23.0,16.0,0.9999828574367292,0.375855631611698,0.546356554892866,26.0,12.0,5.0
 2_rmcv_rt10v_genELtask_27_02_04,0.516174675851637,0.794117647058823,0.6256671307486,27.0,19.0,11.0,0.999903722656389,0.846153846153846,0.916626214700918,29.0,21.0,14.0,0.999966555172648,0.6649874055415611,0.7987790423554341,35.0,14.0,9.0
 2_ense_rmcv_genELtask_37_03_03,0.347586684851292,0.938228122460038,0.507251278902368,46.0,29.0,16.0,0.943213662107766,0.580070754716981,0.718356539061433,38.0,29.0,12.0,0.9999801548798732,0.395704287667927,0.567028509892201,47.0,22.0,12.0
 2_enseef_enve_genELtask_31_02_08,0.355749896035362,0.7186262671646401,0.475906351218088,55.0,34.0,26.0,0.999918792644788,0.891761223482979,0.9427480317076132,34.0,25.0,15.0,0.9999729144583452,0.8223839854413101,0.902525417244995,44.0,19.0,11.0
-2_rt10v_rutpt_genELtask_61_05_05,0.605456915994056,0.8172844922132041,0.6956015271512921,17.0,9.0,7.0,0.9699429828767232,0.669481921785798,0.7921793677187431,22.0,14.0,9.0,0.999986784471079,0.425507916015492,0.5969889506542301,21.0,9.0,7.0
+2_rt10v_rvpnot_genELtask_61_05_05,0.605456915994056,0.8172844922132041,0.6956015271512921,17.0,9.0,7.0,0.9699429828767232,0.669481921785798,0.7921793677187431,22.0,14.0,9.0,0.999986784471079,0.425507916015492,0.5969889506542301,21.0,9.0,7.0
 2_ense_rt10v_genELtask_25_02_02,0.7156021491591,0.854368932038835,0.7788528734876411,13.0,7.0,5.0,0.999918849917938,0.8888888888888881,0.9411405271391812,21.0,14.0,11.0,0.999969605787058,0.422039859320046,0.593564307560928,21.0,7.0,6.0
-2_enve_rutpt_genELtask_61_05_05,0.9448534970195952,0.422191543326805,0.5836079197849261,55.0,35.0,35.0,0.836247168616255,0.528098574534911,0.647373937173884,73.0,46.0,40.0,,,,,,
+2_enve_rvpnot_genELtask_61_05_05,0.9448534970195952,0.422191543326805,0.5836079197849261,55.0,35.0,35.0,0.836247168616255,0.528098574534911,0.647373937173884,73.0,46.0,40.0,,,,,,
 2_enseef_enve_genELtask_30_02_07,0.6670729945878161,0.594415171862814,0.6286516501199011,50.0,30.0,21.0,0.99991352642056,0.8417043253712071,0.914013229581276,38.0,24.0,15.0,0.9999735642989972,0.412198391420911,0.583763886540791,51.0,15.0,12.0
 2_enself_rt10v_genELtask_10_00_09,0.498406375838381,1.0,0.665248605285085,9.0,6.0,4.0,0.9998882948342572,0.997266135742303,0.998575493909553,16.0,11.0,8.0,0.999950689843589,0.687113549813684,0.8145269776765001,16.0,6.0,3.0
-2_rmcv_rutpt_genELtask_38_03_04,0.349958743145046,0.972942135289323,0.5147620843999631,44.0,27.0,15.0,0.943053585938286,0.604190761276277,0.736514907988688,38.0,29.0,12.0,0.999979768010162,0.502176986146451,0.668594438918876,46.0,22.0,12.0
+2_rmcv_rvpnot_genELtask_38_03_04,0.349958743145046,0.972942135289323,0.5147620843999631,44.0,27.0,15.0,0.943053585938286,0.604190761276277,0.736514907988688,38.0,29.0,12.0,0.999979768010162,0.502176986146451,0.668594438918876,46.0,22.0,12.0
 2_enself_enve_genELtask_6_00_05,0.6462704836598631,0.6465378421900161,0.646404135279471,25.0,13.0,10.0,0.9494909669515752,0.8281710914454271,0.884691178172457,28.0,18.0,15.0,0.999975452553115,0.267064144736842,0.421545766312493,29.0,8.0,9.0
-2_enve_rutpt_genELtask_46_04_01,0.529747534342072,0.9424657534246572,0.678256219023699,15.0,9.0,5.0,0.9999024204979232,0.960600375234521,0.97985745536197,21.0,16.0,9.0,0.99995789650962,0.521384928716904,0.685398409808051,25.0,10.0,7.0
-2_ense_rutpt_genELtask_49_04_04,0.254065466328768,1.0,0.405186927078911,66.0,29.0,21.0,0.999950592791603,0.898376364553337,0.9464460005788572,43.0,30.0,14.0,0.999979744554846,0.7605815186700271,0.8640041430420191,74.0,32.0,23.0
-2_ense_rutpt_genELtask_53_04_08,0.587265446107901,0.8377253814147011,0.690484682891213,28.0,18.0,11.0,0.999933686890258,0.583968527169904,0.737330622076769,38.0,25.0,18.0,0.999996428584183,0.284328983598707,0.442766242020272,24.0,11.0,4.0
+2_enve_rvpnot_genELtask_46_04_01,0.529747534342072,0.9424657534246572,0.678256219023699,15.0,9.0,5.0,0.9999024204979232,0.960600375234521,0.97985745536197,21.0,16.0,9.0,0.99995789650962,0.521384928716904,0.685398409808051,25.0,10.0,7.0
+2_ense_rvpnot_genELtask_49_04_04,0.254065466328768,1.0,0.405186927078911,66.0,29.0,21.0,0.999950592791603,0.898376364553337,0.9464460005788572,43.0,30.0,14.0,0.999979744554846,0.7605815186700271,0.8640041430420191,74.0,32.0,23.0
+2_ense_rvpnot_genELtask_53_04_08,0.587265446107901,0.8377253814147011,0.690484682891213,28.0,18.0,11.0,0.999933686890258,0.583968527169904,0.737330622076769,38.0,25.0,18.0,0.999996428584183,0.284328983598707,0.442766242020272,24.0,11.0,4.0
 2_enseef_enself_genELtask_36_03_02,0.194818945436577,0.939702372595816,0.322729634681293,76.0,37.0,34.0,0.999921883586294,0.94680353187108,0.972638014028554,33.0,23.0,10.0,0.999955638018094,0.94680353187108,0.9726539825096372,53.0,23.0,16.0
 2_ense_rt10v_genELtask_51_04_06,0.32823707113298,0.949177877428998,0.487790387645361,48.0,28.0,22.0,0.945547714508714,0.484056987788331,0.6403154351126741,38.0,29.0,16.0,0.9999780539167552,0.464820846905537,0.634640899372066,46.0,19.0,12.0
 2_ense_enve_genELtask_64_05_08,0.844557955914588,0.8677694702730181,0.85600639084615,38.0,24.0,24.0,0.9998886779644752,0.8772566482167381,0.934566949065618,31.0,21.0,11.0,0.999964401670992,0.702658017720118,0.8253538732564061,39.0,12.0,14.0
 2_ense_enseef_genELtask_35_03_01,0.613226590030449,0.7371737900945301,0.6695119110047241,11.0,2.0,4.0,0.99995715816078,0.998445625962404,0.99920082042268,13.0,6.0,4.0,0.999959603397321,0.736666666666666,0.848353984428537,14.0,7.0,5.0
-2_enve_rutpt_genELtask_77_06_10,0.430260001972527,0.6215005599104141,0.508493742443146,86.0,54.0,43.0,,,,,,,,,,,,
+2_enve_rvpnot_genELtask_77_06_10,0.430260001972527,0.6215005599104141,0.508493742443146,86.0,54.0,43.0,,,,,,,,,,,,
 2_enseef_rmcv_genELtask_13_01_01,0.724798331778521,1.0,0.840444147497693,6.0,5.0,0.0,0.999983385603606,0.9896890987800072,0.99480961159254,12.0,9.0,2.0,0.999983385603606,0.9896890987800072,0.99480961159254,12.0,9.0,2.0
 2_ense_enseef_genELtask_58_05_02,0.225420538815396,0.637019197304859,0.333002306545369,55.0,36.0,23.0,,,,,,,0.999982296525308,0.484721663109443,0.652942397986183,44.0,20.0,10.0
 2_enve_rt10v_genELtask_96_08_07,0.9652300174717752,0.768572469045884,0.855748245251958,19.0,12.0,12.0,0.947444388709922,0.955454056155249,0.9514323653131852,15.0,10.0,6.0,0.999973384276718,0.7840037785383921,0.8789158606421261,14.0,7.0,4.0
-2_enseef_rutpt_genELtask_27_02_04,0.220718291751281,0.6349925628696941,0.327574373971091,56.0,37.0,24.0,0.999917632094613,0.7217976132506221,0.8383943422053901,35.0,27.0,11.0,,,,,,
+2_enseef_rvpnot_genELtask_27_02_04,0.220718291751281,0.6349925628696941,0.327574373971091,56.0,37.0,24.0,0.999917632094613,0.7217976132506221,0.8383943422053901,35.0,27.0,11.0,,,,,,
 2_ense_rt10v_genELtask_43_03_09,0.316894690458113,0.9996728242814712,0.481237774232528,23.0,14.0,6.0,0.9896059143986292,0.6623314789755921,0.7935496242358571,37.0,28.0,18.0,0.999975420250205,0.740033123550844,0.8505877012595171,30.0,13.0,5.0
 2_enve_rt10v_genELtask_75_06_08,0.7740693215001581,0.8127877119526241,0.792956163600802,28.0,16.0,16.0,0.98165723341719,0.656575006806425,0.7868623127872161,36.0,23.0,17.0,0.999969046687424,0.231356277731142,0.375772530459738,36.0,8.0,17.0
-2_rt10v_rutpt_genELtask_51_04_06,0.545070482757374,0.7510891903000411,0.631706966449578,30.0,18.0,16.0,0.878720533973232,0.341596510002819,0.491950627346905,34.0,21.0,17.0,,,,,,
+2_rt10v_rvpnot_genELtask_51_04_06,0.545070482757374,0.7510891903000411,0.631706966449578,30.0,18.0,16.0,0.878720533973232,0.341596510002819,0.491950627346905,34.0,21.0,17.0,,,,,,
 2_enve_rt10v_genELtask_80_07_02,0.508643798301461,0.656677181913775,0.5732579808265861,32.0,16.0,15.0,0.8530258259357411,0.56366703850231,0.678795740616524,42.0,26.0,22.0,,,,,,
 2_enve_rmcv_genELtask_47_04_02,0.7125106697886,0.866071428571428,0.7818220342132111,13.0,7.0,5.0,0.99991789143025,0.899328859060402,0.9469596472619852,21.0,14.0,11.0,0.999968022702797,0.429028815368196,0.600442330950693,21.0,7.0,6.0
-2_enve_rutpt_genELtask_70_06_03,0.97493545754995,0.6375132876360831,0.7709197711012511,38.0,23.0,23.0,0.8459696881426381,0.6019518123972261,0.7033986121771201,53.0,35.0,33.0,0.9999658433765,0.28928206054041,0.448745627218993,32.0,10.0,16.0
+2_enve_rvpnot_genELtask_70_06_03,0.97493545754995,0.6375132876360831,0.7709197711012511,38.0,23.0,23.0,0.8459696881426381,0.6019518123972261,0.7033986121771201,53.0,35.0,33.0,0.9999658433765,0.28928206054041,0.448745627218993,32.0,10.0,16.0
 2_enseef_enself_genELtask_13_01_01,0.8295699853207841,1.0,0.906846955270019,15.0,9.0,3.0,0.9999575617277512,1.0,0.999978780413614,19.0,17.0,3.0,0.99997573655231,1.0,0.999987868128974,27.0,13.0,7.0
 2_enself_rt10v_genELtask_6_00_05,0.605456915994056,0.8172844922132041,0.6956015271512921,17.0,9.0,7.0,0.9699429828767232,0.669481921785798,0.7921793677187431,22.0,14.0,9.0,0.999986784471079,0.425507916015492,0.5969889506542301,21.0,9.0,7.0
 2_enve_rmcv_genELtask_89_08_00,0.3,0.9640707026922892,0.457602901786564,45.0,26.0,13.0,,,,,,,,,,,,
@@ -186,50 +186,50 @@ log,fitness_heu,precision_heu,fscore_heu,size_heu,pnsize_heu,cfc_heu,fitness_imf
 2_ense_rt10v_genELtask_33_02_10,0.9999706267886912,0.979527881450685,0.98964369560696,30.0,15.0,13.0,0.9999227112316212,0.979527881450685,0.989620229540463,29.0,16.0,12.0,0.9999652718620172,0.36135220235398,0.5308675751377351,41.0,12.0,12.0
 2_enself_rt10v_genELtask_14_01_02,0.842762737269624,0.472304190203186,0.6053537867072251,47.0,26.0,25.0,0.8213126128427111,0.497695852534562,0.6198047878778761,43.0,28.0,23.0,0.9999944207118052,0.185612901584717,0.313108501451081,35.0,12.0,12.0
 2_enself_rt10v_genELtask_21_01_09,0.9999667850151852,0.982152588555858,0.990979634695894,17.0,10.0,7.0,0.999908998246086,0.982152588555858,0.990951257505825,19.0,14.0,9.0,,,,,,
-2_rt10v_rutpt_genELtask_102_09_02,0.206376405307617,0.9973077373288072,0.341984709318483,29.0,16.0,14.0,0.889192139603533,0.8494048565203971,0.868843237901426,37.0,25.0,22.0,,,,,,
-2_enseef_rutpt_genELtask_53_04_08,0.175596023577021,0.9991326973113612,0.298696585099205,14.0,9.0,2.0,0.999989192262517,0.932491356226082,0.9650614893211692,18.0,18.0,2.0,0.999989192262517,0.932491356226082,0.9650614893211692,18.0,18.0,2.0
-2_rt10v_rutpt_genELtask_80_07_02,0.985072402672326,0.712919969188461,0.8271860328700411,29.0,18.0,18.0,0.9686666193751672,0.691338675620024,0.8068368212742281,31.0,18.0,16.0,0.999958215484349,0.408035700462898,0.5795744516613001,25.0,8.0,12.0
+2_rt10v_rvpnot_genELtask_102_09_02,0.206376405307617,0.9973077373288072,0.341984709318483,29.0,16.0,14.0,0.889192139603533,0.8494048565203971,0.868843237901426,37.0,25.0,22.0,,,,,,
+2_enseef_rvpnot_genELtask_53_04_08,0.175596023577021,0.9991326973113612,0.298696585099205,14.0,9.0,2.0,0.999989192262517,0.932491356226082,0.9650614893211692,18.0,18.0,2.0,0.999989192262517,0.932491356226082,0.9650614893211692,18.0,18.0,2.0
+2_rt10v_rvpnot_genELtask_80_07_02,0.985072402672326,0.712919969188461,0.8271860328700411,29.0,18.0,18.0,0.9686666193751672,0.691338675620024,0.8068368212742281,31.0,18.0,16.0,0.999958215484349,0.408035700462898,0.5795744516613001,25.0,8.0,12.0
 2_enself_rmcv_genELtask_3_00_02,0.706185805810103,0.78169014084507,0.7420221870613031,12.0,7.0,5.0,0.9998970863015292,0.829670329670329,0.906864582289819,20.0,14.0,11.0,0.999970803772152,0.364734299516908,0.534509103501085,24.0,8.0,7.0
 2_enve_rmcv_genELtask_91_08_02,0.252913239017876,1.0,0.403720275501483,51.0,23.0,14.0,,,,,,,,,,,,
 2_enseef_rt10v_genELtask_21_01_09,0.323370650458697,0.9683410492990572,0.484834309446013,22.0,13.0,8.0,0.9999436784512072,0.9683410492990572,0.983888659175998,23.0,16.0,8.0,0.999974873337461,0.808335595981536,0.894000558997158,21.0,11.0,4.0
 2_ense_rt10v_genELtask_31_02_08,0.560804262046058,0.9522776572668112,0.7058988175458281,10.0,6.0,4.0,0.999891247371402,0.968996617812852,0.984201542420387,17.0,12.0,9.0,0.9999613614145412,0.48218793828892,0.650635273147622,17.0,6.0,5.0
-2_rmcv_rutpt_genELtask_22_01_10,0.5810393600057651,0.27069351230425,0.369326088637156,42.0,24.0,18.0,0.882285684874598,0.210106382978723,0.339390699471461,41.0,32.0,22.0,0.9999920635550512,0.195216049382716,0.326661939369636,51.0,21.0,16.0
+2_rmcv_rvpnot_genELtask_22_01_10,0.5810393600057651,0.27069351230425,0.369326088637156,42.0,24.0,18.0,0.882285684874598,0.210106382978723,0.339390699471461,41.0,32.0,22.0,0.9999920635550512,0.195216049382716,0.326661939369636,51.0,21.0,16.0
 2_ense_enself_genELtask_36_03_02,0.252685684913903,1.0,0.403430306511837,24.0,16.0,4.0,0.999955441832052,1.0,0.999977720419657,21.0,23.0,2.0,0.999977720419657,1.0,0.999988860085732,33.0,22.0,7.0
 2_ense_enseef_genELtask_70_06_03,0.255365119632201,0.953464788732394,0.40283856009013,43.0,25.0,12.0,0.960891204240725,0.35300484772231,0.5163258580827941,36.0,28.0,10.0,0.99999349651232,0.354734346674104,0.5236949125210181,44.0,26.0,11.0
-2_ense_rutpt_genELtask_59_05_03,0.456102571226449,0.8951559983296541,0.60430026004728,31.0,20.0,20.0,0.932650769964648,0.8215002721440491,0.8735540360541411,36.0,21.0,15.0,0.999971082245738,0.7017752471549551,0.8247468394868801,41.0,13.0,11.0
-2_enve_rutpt_genELtask_83_07_05,0.63766810311605,0.82383808095952,0.7188957146869801,20.0,12.0,10.0,0.9999267492461212,0.6979087706782,0.8220556586844351,21.0,14.0,10.0,0.9999849524483452,0.468847352024922,0.6383850567451991,18.0,7.0,7.0
-2_enve_rutpt_genELtask_93_08_04,0.292253437667682,0.943058402242796,0.446222647711078,49.0,30.0,19.0,0.979833439440754,0.780331036259419,0.8687761328171191,37.0,27.0,13.0,0.999976518607339,0.661563242239755,0.796306803366801,58.0,24.0,15.0
+2_ense_rvpnot_genELtask_59_05_03,0.456102571226449,0.8951559983296541,0.60430026004728,31.0,20.0,20.0,0.932650769964648,0.8215002721440491,0.8735540360541411,36.0,21.0,15.0,0.999971082245738,0.7017752471549551,0.8247468394868801,41.0,13.0,11.0
+2_enve_rvpnot_genELtask_83_07_05,0.63766810311605,0.82383808095952,0.7188957146869801,20.0,12.0,10.0,0.9999267492461212,0.6979087706782,0.8220556586844351,21.0,14.0,10.0,0.9999849524483452,0.468847352024922,0.6383850567451991,18.0,7.0,7.0
+2_enve_rvpnot_genELtask_93_08_04,0.292253437667682,0.943058402242796,0.446222647711078,49.0,30.0,19.0,0.979833439440754,0.780331036259419,0.8687761328171191,37.0,27.0,13.0,0.999976518607339,0.661563242239755,0.796306803366801,58.0,24.0,15.0
 2_ense_enseef_genELtask_36_03_02,0.335428382401587,1.0,0.5023532325984641,16.0,11.0,2.0,0.999964939702187,1.0,0.999982469543782,20.0,22.0,2.0,0.999979979833592,1.0,0.999989989816593,28.0,19.0,8.0
 2_ense_enself_genELtask_67_06_00,0.473157737466167,0.8411438206983771,0.605635296729162,13.0,6.0,4.0,,,,,,,,,,,,
-2_enve_rutpt_genELtask_81_07_03,0.553448813982521,0.482353832172069,0.515461429504232,27.0,2.0,12.0,0.954121079554186,0.683627547963718,0.796536712797817,34.0,21.0,13.0,0.9999728687179172,0.575028126757922,0.7301741740599811,39.0,11.0,12.0
+2_enve_rvpnot_genELtask_81_07_03,0.553448813982521,0.482353832172069,0.515461429504232,27.0,2.0,12.0,0.954121079554186,0.683627547963718,0.796536712797817,34.0,21.0,13.0,0.9999728687179172,0.575028126757922,0.7301741740599811,39.0,11.0,12.0
 2_enve_rt10v_genELtask_71_06_04,0.802075659979345,0.69080553295362,0.742293903070206,34.0,15.0,15.0,,,,,,,,,,,,
-2_ense_rutpt_genELtask_46_04_01,0.5990682072307111,0.658003751758636,0.627154436304579,15.0,2.0,6.0,0.99995000165365,0.975996442136641,0.9878280324808932,16.0,7.0,5.0,0.999966926994438,0.975996442136641,0.9878362911454752,15.0,6.0,4.0
+2_ense_rvpnot_genELtask_46_04_01,0.5990682072307111,0.658003751758636,0.627154436304579,15.0,2.0,6.0,0.99995000165365,0.975996442136641,0.9878280324808932,16.0,7.0,5.0,0.999966926994438,0.975996442136641,0.9878362911454752,15.0,6.0,4.0
 2_enself_enve_genELtask_43_03_09,0.120050399686034,0.992383916141184,0.214189879042252,50.0,23.0,18.0,0.999951701256724,0.9940091657004032,0.996971578307576,31.0,25.0,8.0,0.9999758500451552,0.9940091657004032,0.9969835806938412,47.0,25.0,12.0
-2_enve_rutpt_genELtask_91_08_02,0.965147839769944,0.7672088109111861,0.8548700710240701,19.0,12.0,12.0,0.9477285006183,0.9531862121075232,0.950449521551991,15.0,10.0,6.0,0.999973446093262,0.782110895466062,0.877725154895818,14.0,7.0,4.0
+2_enve_rvpnot_genELtask_91_08_02,0.965147839769944,0.7672088109111861,0.8548700710240701,19.0,12.0,12.0,0.9477285006183,0.9531862121075232,0.950449521551991,15.0,10.0,6.0,0.999973446093262,0.782110895466062,0.877725154895818,14.0,7.0,4.0
 2_ense_enve_genELtask_30_02_07,0.55183789844381,0.913858580987401,0.688139469370402,15.0,9.0,6.0,0.9520685225805032,0.789164614510323,0.8629961980449771,26.0,19.0,13.0,0.999960365770856,0.364346621966328,0.5340912047560511,23.0,8.0,8.0
 2_enself_enve_genELtask_31_02_08,0.063115705612325,1.0,0.118737227338716,10.0,5.0,5.0,,,,,,,,,,,,
-2_rt10v_rutpt_genELtask_72_06_05,0.831109727888882,0.55039826565659,0.6622348259113491,61.0,39.0,39.0,,,,,,,,,,,,
+2_rt10v_rvpnot_genELtask_72_06_05,0.831109727888882,0.55039826565659,0.6622348259113491,61.0,39.0,39.0,,,,,,,,,,,,
 2_ense_enseef_genELtask_82_07_04,0.260867296806114,0.851689337428696,0.399400602786254,48.0,31.0,14.0,0.9999925000562492,0.248509942507516,0.39808985585422,28.0,38.0,0.0,0.9999950000249992,0.351919629518972,0.5206214390154671,39.0,42.0,1.0
 2_ense_enseef_genELtask_71_06_04,0.157899168951742,0.736758893280632,0.260062747719589,64.0,32.0,24.0,0.99999166673611,0.302881570614312,0.464940161390075,31.0,35.0,4.0,0.999994444475308,0.317141909814323,0.481560085851028,41.0,39.0,5.0
 2_rmcv_rt10v_genELtask_17_01_05,0.6526084080791,0.8016398024783371,0.719487734693787,25.0,15.0,13.0,0.971953434341488,0.593521223577934,0.736996908500358,25.0,16.0,11.0,0.99998845351907,0.318739329057265,0.483398701310413,24.0,9.0,9.0
 2_ense_rt10v_genELtask_63_05_07,0.985631416486038,0.58348814205409,0.7330279465646871,37.0,24.0,24.0,0.8685878833091141,0.511472625870819,0.643825285225615,37.0,22.0,20.0,0.999964058530892,0.343120501268706,0.5109256398048461,35.0,13.0,13.0
-2_enve_rutpt_genELtask_48_04_03,0.451396747515872,0.941935483870967,0.610316196254125,10.0,6.0,4.0,0.9999000081502792,0.969072164948453,0.984244753550997,17.0,12.0,9.0,0.999959246943898,0.462295081967213,0.632278849020803,17.0,6.0,5.0
-2_rmcv_rutpt_genELtask_39_03_05,0.347586684851292,0.938228122460038,0.507251278902368,46.0,29.0,16.0,0.943213662107766,0.580070754716981,0.718356539061433,38.0,29.0,12.0,,,,,,
+2_enve_rvpnot_genELtask_48_04_03,0.451396747515872,0.941935483870967,0.610316196254125,10.0,6.0,4.0,0.9999000081502792,0.969072164948453,0.984244753550997,17.0,12.0,9.0,0.999959246943898,0.462295081967213,0.632278849020803,17.0,6.0,5.0
+2_rmcv_rvpnot_genELtask_39_03_05,0.347586684851292,0.938228122460038,0.507251278902368,46.0,29.0,16.0,0.943213662107766,0.580070754716981,0.718356539061433,38.0,29.0,12.0,,,,,,
 2_ense_rmcv_genELtask_6_00_05,0.9999900179907292,1.0,0.999995008970454,9.0,6.0,2.0,0.9999900179907292,1.0,0.999995008970454,9.0,6.0,2.0,0.9999900179907292,1.0,0.999995008970454,9.0,6.0,2.0
 2_enself_enve_genELtask_42_03_08,0.089373633824702,0.9147802929427432,0.162838060484831,28.0,16.0,6.0,0.999980885310729,0.491463274894007,0.6590308827368441,22.0,24.0,4.0,0.9999904708792332,0.579359719032824,0.7336614791889241,38.0,25.0,5.0
-2_enve_rutpt_genELtask_55_04_10,0.797791071166094,0.324022346368715,0.460864758346906,68.0,38.0,36.0,0.901900248183476,0.6339468302658481,0.7445491306714911,43.0,31.0,18.0,0.9999945173125392,0.229194363201276,0.372917637368163,90.0,36.0,29.0
+2_enve_rvpnot_genELtask_55_04_10,0.797791071166094,0.324022346368715,0.460864758346906,68.0,38.0,36.0,0.901900248183476,0.6339468302658481,0.7445491306714911,43.0,31.0,18.0,0.9999945173125392,0.229194363201276,0.372917637368163,90.0,36.0,29.0
 2_enself_enve_genELtask_7_00_06,0.9646628283681772,0.412360933264596,0.5777522149943111,56.0,36.0,36.0,0.7549804058706631,0.5623003194888171,0.6445485996358441,70.0,47.0,45.0,,,,,,
 2_enself_enve_genELtask_54_04_09,0.418045730633802,0.997344815213491,0.589145862522586,21.0,11.0,7.0,0.999990000099999,0.944525893346365,0.971466935667847,18.0,18.0,2.0,0.999990000099999,0.944525893346365,0.971466935667847,18.0,18.0,2.0
 2_enseef_enve_genELtask_53_04_08,0.461538461538461,1.0,0.631578947368421,14.0,9.0,2.0,0.999990000099999,0.9793776846794292,0.98957651833106,15.0,17.0,0.0,0.999990000099999,0.9793776846794292,0.98957651833106,15.0,17.0,0.0
-2_ense_rutpt_genELtask_58_05_02,0.6514437730290441,0.888699895806431,0.751797413357422,26.0,16.0,16.0,0.972305922828994,0.5471019685953921,0.700207610423846,37.0,21.0,19.0,,,,,,
+2_ense_rvpnot_genELtask_58_05_02,0.6514437730290441,0.888699895806431,0.751797413357422,26.0,16.0,16.0,0.972305922828994,0.5471019685953921,0.700207610423846,37.0,21.0,19.0,,,,,,
 2_enseef_rmcv_genELtask_6_00_05,0.9999916831367532,1.0,0.999995841551084,10.0,7.0,2.0,0.9999916831367532,1.0,0.999995841551084,10.0,7.0,2.0,0.9999916831367532,1.0,0.999995841551084,10.0,7.0,2.0
-2_enve_rutpt_genELtask_71_06_04,0.889707929503831,0.5797482984461281,0.7020374590721581,33.0,18.0,18.0,,,,,,,,,,,,
+2_enve_rvpnot_genELtask_71_06_04,0.889707929503831,0.5797482984461281,0.7020374590721581,33.0,18.0,18.0,,,,,,,,,,,,
 2_enve_rmcv_genELtask_69_06_02,0.875055836127258,0.420022338486861,0.5675997106603381,39.0,21.0,20.0,,,,,,,,,,,,
 2_enseef_enve_genELtask_19_01_07,0.38658878798223,0.9996457459998812,0.557556211282027,19.0,11.0,6.0,0.999907353633494,0.6974928854863921,0.82176053617915,29.0,22.0,14.0,0.999975326605625,0.806953477318308,0.8931547997736051,21.0,11.0,4.0
 2_enve_rt10v_genELtask_73_06_06,0.978545949737159,0.320364312600824,0.482698781625753,60.0,38.0,38.0,0.880498531207595,0.52843439534087,0.660479573115015,60.0,36.0,35.0,0.999983511484769,0.158346416242972,0.273400179957871,69.0,25.0,27.0
-2_rt10v_rutpt_genELtask_38_03_04,0.8432749322387051,0.8923327895595431,0.8671105379394811,31.0,17.0,12.0,,,,,,,,,,,,
-2_enseef_rutpt_genELtask_15_01_03,0.999964245972414,0.8764880952380951,0.934163621435919,18.0,9.0,8.0,0.99989083345123,0.8764880952380951,0.9341315857451792,23.0,16.0,13.0,0.99995843128923,0.2780925401322,0.43516414666084,23.0,7.0,8.0
-2_enve_rutpt_genELtask_53_04_08,0.7113967187922421,0.46788990825688,0.5645028746282611,39.0,22.0,21.0,0.8762484855272401,0.360163710777626,0.510497885824719,51.0,32.0,31.0,0.999982608998104,0.133519748339741,0.235583852884303,60.0,23.0,21.0
+2_rt10v_rvpnot_genELtask_38_03_04,0.8432749322387051,0.8923327895595431,0.8671105379394811,31.0,17.0,12.0,,,,,,,,,,,,
+2_enseef_rvpnot_genELtask_15_01_03,0.999964245972414,0.8764880952380951,0.934163621435919,18.0,9.0,8.0,0.99989083345123,0.8764880952380951,0.9341315857451792,23.0,16.0,13.0,0.99995843128923,0.2780925401322,0.43516414666084,23.0,7.0,8.0
+2_enve_rvpnot_genELtask_53_04_08,0.7113967187922421,0.46788990825688,0.5645028746282611,39.0,22.0,21.0,0.8762484855272401,0.360163710777626,0.510497885824719,51.0,32.0,31.0,0.999982608998104,0.133519748339741,0.235583852884303,60.0,23.0,21.0
 2_enseef_rt10v_genELtask_43_03_09,0.063850164946259,1.0,0.12003601080324,7.0,3.0,2.0,0.9998918187171,0.9841275677795752,0.9919470649269332,36.0,23.0,11.0,,,,,,
 2_enve_rt10v_genELtask_102_09_02,0.7977784698432471,1.0,0.8875158794318051,13.0,2.0,2.0,0.9998797874372832,1.0,0.9999398901056592,17.0,8.0,4.0,0.999920228517012,1.0,0.99996011266757,29.0,6.0,4.0
 2_ense_rt10v_genELtask_45_04_00,0.999900003124902,0.494117647058823,0.6613954484104371,19.0,12.0,12.0,0.999923813151754,0.56,0.7179290817204901,20.0,14.0,10.0,0.9999729737034132,0.56,0.71794175247087,18.0,9.0,4.0
@@ -237,49 +237,49 @@ log,fitness_heu,precision_heu,fscore_heu,size_heu,pnsize_heu,cfc_heu,fitness_imf
 2_enself_rt10v_genELtask_35_03_01,0.105262049873159,0.8208092485549131,0.186594842544833,87.0,52.0,36.0,0.99999166673611,0.278365384615384,0.43550127818967,31.0,35.0,4.0,0.999994444475308,0.268837888033961,0.423754010061801,45.0,38.0,7.0
 2_ense_enseef_genELtask_60_05_04,0.383868741957741,0.9647696476964772,0.54921306375568,16.0,9.0,4.0,0.999988889012344,0.7269758327890261,0.8419022649301521,14.0,16.0,0.0,0.999988889012344,0.7269758327890261,0.8419022649301521,14.0,16.0,0.0
 2_rmcv_rt10v_genELtask_40_03_06,0.358748578333807,0.822327923894795,0.499559466410159,45.0,28.0,11.0,0.99995853559571,0.685503291057954,0.8133970834964581,31.0,29.0,6.0,0.999979267368022,0.6670832682393371,0.8002932386749431,42.0,28.0,9.0
-2_enseef_rutpt_genELtask_26_02_03,0.553448813982521,0.482353832172069,0.515461429504232,27.0,2.0,12.0,0.954121079554186,0.683627547963718,0.796536712797817,34.0,21.0,13.0,0.9999728687179172,0.575028126757922,0.7301741740599811,39.0,11.0,12.0
+2_enseef_rvpnot_genELtask_26_02_03,0.553448813982521,0.482353832172069,0.515461429504232,27.0,2.0,12.0,0.954121079554186,0.683627547963718,0.796536712797817,34.0,21.0,13.0,0.9999728687179172,0.575028126757922,0.7301741740599811,39.0,11.0,12.0
 2_enseef_enself_genELtask_37_03_03,0.4,1.0,0.5714285714285711,12.0,7.0,2.0,0.999987500156248,1.0,0.999993750039062,13.0,14.0,0.0,0.999987500156248,1.0,0.999993750039062,13.0,14.0,0.0
 2_enseef_enve_genELtask_63_05_07,0.151200174453552,0.727581810454738,0.250370395750587,42.0,38.0,8.0,0.9999925000562492,0.366987099666284,0.5369273211658311,27.0,37.0,0.0,0.9999950000249992,0.366987099666284,0.536927681529758,37.0,50.0,0.0
 2_enve_rt10v_genELtask_82_07_04,0.545070482757374,0.7510891903000411,0.631706966449578,30.0,18.0,16.0,0.878720533973232,0.341596510002819,0.491950627346905,34.0,21.0,17.0,,,,,,
-2_enself_rutpt_genELtask_5_00_04,0.8138750291831941,0.756789445981207,0.784294853767567,30.0,16.0,16.0,0.718310654671738,0.348327223488226,0.469151079418713,55.0,37.0,35.0,,,,,,
+2_enself_rvpnot_genELtask_5_00_04,0.8138750291831941,0.756789445981207,0.784294853767567,30.0,16.0,16.0,0.718310654671738,0.348327223488226,0.469151079418713,55.0,37.0,35.0,,,,,,
 2_enve_rt10v_genELtask_79_07_01,0.090250911670127,0.8982569922983381,0.164021981298347,78.0,32.0,27.0,,,,,,,,,,,,
 2_enseef_rt10v_genELtask_29_02_06,0.291365046596918,0.890133621271426,0.439025165398042,52.0,32.0,19.0,0.961353763333082,0.5638844811171051,0.710829891631078,47.0,33.0,18.0,0.999981083268171,0.5921654653713571,0.743843918189615,49.0,22.0,11.0
-2_ense_rutpt_genELtask_61_05_05,0.466447024531595,0.764805246422893,0.579476911362056,19.0,10.0,8.0,0.969837410379496,0.783336459114778,0.8666670387722221,19.0,13.0,6.0,0.999986945740452,0.6078514601726981,0.75610026839327,20.0,10.0,5.0
+2_ense_rvpnot_genELtask_61_05_05,0.466447024531595,0.764805246422893,0.579476911362056,19.0,10.0,8.0,0.969837410379496,0.783336459114778,0.8666670387722221,19.0,13.0,6.0,0.999986945740452,0.6078514601726981,0.75610026839327,20.0,10.0,5.0
 2_ense_rt10v_genELtask_71_06_04,0.376214776532216,0.994733180959952,0.545948253307299,27.0,17.0,8.0,0.945685191537984,0.507638900441974,0.6606462982975451,28.0,22.0,8.0,,,,,,
 2_enseef_enve_genELtask_17_01_05,0.6816700018978381,0.6185983827493261,0.6486044969203161,41.0,22.0,20.0,0.999897904275048,0.540229885057471,0.701467415414695,48.0,31.0,27.0,0.999968769744128,0.383048084759576,0.553913888789635,53.0,18.0,14.0
 2_enve_rmcv_genELtask_80_07_02,0.991200260161704,0.7447296837810261,0.850467795534783,17.0,10.0,10.0,0.742612455647855,0.474979669287069,0.579382555732164,21.0,14.0,12.0,0.9999616558116092,0.474979669287069,0.6440411540375981,15.0,7.0,6.0
-2_enve_rutpt_genELtask_3_00_02,0.999991758309684,1.0,0.99999587913786,10.0,7.0,2.0,0.999991758309684,1.0,0.99999587913786,10.0,7.0,2.0,0.999991758309684,1.0,0.99999587913786,10.0,7.0,2.0
+2_enve_rvpnot_genELtask_3_00_02,0.999991758309684,1.0,0.99999587913786,10.0,7.0,2.0,0.999991758309684,1.0,0.99999587913786,10.0,7.0,2.0,0.999991758309684,1.0,0.99999587913786,10.0,7.0,2.0
 2_enve_rmcv_genELtask_68_06_01,0.605456915994056,0.8172844922132041,0.6956015271512921,17.0,9.0,7.0,0.9699429828767232,0.669481921785798,0.7921793677187431,22.0,14.0,9.0,0.999986784471079,0.425507916015492,0.5969889506542301,21.0,9.0,7.0
 2_enve_rmcv_genELtask_90_08_01,0.817210325966445,0.743124788382679,0.778408746894449,17.0,10.0,10.0,0.950939297008463,0.965601374570446,0.9582142512717552,13.0,8.0,4.0,0.999977876229932,0.965601374570446,0.9824890158302972,13.0,8.0,3.0
 2_ense_rmcv_genELtask_26_02_03,0.554721448479335,0.9571791320406272,0.702384536947744,10.0,6.0,4.0,0.999891428197286,0.972704532077692,0.9861106312491452,17.0,12.0,9.0,0.99995986748636,0.493966000373622,0.661272673434515,17.0,6.0,5.0
-2_enself_rutpt_genELtask_1_00_00,0.498174992178537,1.0,0.6650424613671161,5.0,4.0,0.0,0.999924815830862,1.0,0.9999624065022132,11.0,8.0,4.0,0.999949947735128,0.888780713954566,0.94109366305493,9.0,6.0,2.0
+2_enself_rvpnot_genELtask_1_00_00,0.498174992178537,1.0,0.6650424613671161,5.0,4.0,0.0,0.999924815830862,1.0,0.9999624065022132,11.0,8.0,4.0,0.999949947735128,0.888780713954566,0.94109366305493,9.0,6.0,2.0
 2_enself_enve_genELtask_19_01_07,0.217351319395282,0.739100735323662,0.335917559476261,43.0,29.0,14.0,0.999960487944019,0.569159121533769,0.7254216051459701,31.0,27.0,8.0,0.999982409513755,0.6134637924884061,0.760425727986164,44.0,27.0,11.0
-2_rt10v_rutpt_genELtask_41_03_07,0.423756674493268,0.9405437824870052,0.5842726262280741,38.0,25.0,17.0,0.967766231720394,0.481057453301852,0.642660889155459,34.0,26.0,12.0,,,,,,
-2_enself_rutpt_genELtask_13_01_01,0.715442972868715,1.0,0.8341203807810501,44.0,23.0,11.0,0.9999416480765252,0.987667146129668,0.993766496436103,37.0,28.0,10.0,0.9999737932508492,0.841022500453638,0.913636233667436,68.0,30.0,24.0
+2_rt10v_rvpnot_genELtask_41_03_07,0.423756674493268,0.9405437824870052,0.5842726262280741,38.0,25.0,17.0,0.967766231720394,0.481057453301852,0.642660889155459,34.0,26.0,12.0,,,,,,
+2_enself_rvpnot_genELtask_13_01_01,0.715442972868715,1.0,0.8341203807810501,44.0,23.0,11.0,0.9999416480765252,0.987667146129668,0.993766496436103,37.0,28.0,10.0,0.9999737932508492,0.841022500453638,0.913636233667436,68.0,30.0,24.0
 2_enve_rt10v_genELtask_23_02_00,0.329340177147154,0.482625482625482,0.391514000698293,27.0,14.0,12.0,0.8083037134262001,0.632911392405063,0.709935146642371,32.0,21.0,18.0,0.999984516368778,0.19361483007209,0.324416786568765,29.0,9.0,10.0
 2_enseef_rmcv_genELtask_45_04_00,0.228611064603315,0.9529979879275652,0.368761366745274,45.0,25.0,13.0,0.9999885136502612,0.570720927818233,0.7266963032761521,28.0,29.0,4.0,,,,,,
-2_rmcv_rutpt_genELtask_13_01_01,0.725493153669532,1.0,0.840911077655286,6.0,5.0,0.0,0.9999833710008892,0.988304495416626,0.994109633312546,12.0,9.0,2.0,0.9999833710008892,0.988304495416626,0.994109633312546,12.0,9.0,2.0
+2_rmcv_rvpnot_genELtask_13_01_01,0.725493153669532,1.0,0.840911077655286,6.0,5.0,0.0,0.9999833710008892,0.988304495416626,0.994109633312546,12.0,9.0,2.0,0.9999833710008892,0.988304495416626,0.994109633312546,12.0,9.0,2.0
 2_ense_enself_genELtask_35_03_01,0.319382077891694,0.998076958981348,0.4839131754895,19.0,10.0,6.0,0.999890236663564,0.6920901566184691,0.817993155525193,30.0,22.0,15.0,0.99997259202083,0.7727182133828451,0.871778690770826,22.0,10.0,4.0
 2_ense_rmcv_genELtask_78_07_00,0.333333333333333,0.9333084513284652,0.491224623846678,35.0,25.0,3.0,,,,,,,,,,,,
 2_ense_rt10v_genELtask_64_05_08,0.704085195400562,0.7850950570342201,0.742386699979642,25.0,15.0,15.0,0.9861083936441232,0.804590394582385,0.8861494146973941,34.0,24.0,14.0,,,,,,
 2_ense_enve_genELtask_43_03_09,0.233072159304752,1.0,0.378034906628929,36.0,20.0,10.0,0.999958000294181,1.0,0.9999789997060872,25.0,24.0,4.0,0.9999789997060872,1.0,0.9999894997427892,38.0,23.0,9.0
-2_enself_rutpt_genELtask_4_00_03,0.553448813982521,0.482353832172069,0.515461429504232,27.0,2.0,12.0,0.954121079554186,0.683627547963718,0.796536712797817,34.0,21.0,13.0,,,,,,
+2_enself_rvpnot_genELtask_4_00_03,0.553448813982521,0.482353832172069,0.515461429504232,27.0,2.0,12.0,0.954121079554186,0.683627547963718,0.796536712797817,34.0,21.0,13.0,,,,,,
 2_enseef_enself_genELtask_24_02_01,0.225420538815396,0.637019197304859,0.333002306545369,55.0,36.0,23.0,,,,,,,,,,,,
 2_enve_rmcv_genELtask_78_07_00,0.210526315789473,0.9750725137129892,0.346286482124348,46.0,26.0,14.0,0.999991176548442,0.5544359622067461,0.713357424538907,27.0,30.0,2.0,,,,,,
-2_ense_rutpt_genELtask_35_03_01,0.384651380439869,1.0,0.555593105778979,5.0,4.0,0.0,0.999971297912874,0.992541653836942,0.9962426241177472,14.0,8.0,4.0,0.999971297912874,0.992541653836942,0.9962426241177472,14.0,8.0,4.0
+2_ense_rvpnot_genELtask_35_03_01,0.384651380439869,1.0,0.555593105778979,5.0,4.0,0.0,0.999971297912874,0.992541653836942,0.9962426241177472,14.0,8.0,4.0,0.999971297912874,0.992541653836942,0.9962426241177472,14.0,8.0,4.0
 2_enself_rmcv_genELtask_24_02_01,0.75,1.0,0.8571428571428571,5.0,4.0,0.0,0.999980000399992,1.0,0.999990000099999,9.0,10.0,0.0,0.999980000399992,1.0,0.999990000099999,9.0,10.0,0.0
 2_ense_rmcv_genELtask_35_03_01,0.7162336281842161,0.8419048586079091,0.774001254160805,13.0,7.0,6.0,0.999980046548507,0.9964113729035592,0.9981925201131492,11.0,8.0,2.0,0.999980046548507,0.9964113729035592,0.9981925201131492,11.0,8.0,2.0
-2_ense_rutpt_genELtask_25_02_02,0.677252863620671,0.8863879957127541,0.7678346402784071,13.0,7.0,5.0,0.99991699189129,0.9138911454102352,0.9549706339144012,21.0,14.0,11.0,0.9999696672497292,0.431365030674846,0.602726876966725,21.0,7.0,6.0
+2_ense_rvpnot_genELtask_25_02_02,0.677252863620671,0.8863879957127541,0.7678346402784071,13.0,7.0,5.0,0.99991699189129,0.9138911454102352,0.9549706339144012,21.0,14.0,11.0,0.9999696672497292,0.431365030674846,0.602726876966725,21.0,7.0,6.0
 2_enseef_rt10v_genELtask_23_02_00,0.981745852817756,0.6190476190476191,0.759307735042809,21.0,11.0,11.0,0.9816852347431492,0.361581920903954,0.528501916255787,31.0,18.0,17.0,0.999956365540412,0.346341463414634,0.51448697835434,37.0,14.0,12.0
-2_ense_rutpt_genELtask_54_04_09,0.7937617996740931,0.539473684210526,0.642367545166265,30.0,17.0,17.0,0.985515704370604,0.344456404736275,0.5104876922067181,34.0,29.0,17.0,0.999989080579005,0.283570300157977,0.441845087948595,41.0,15.0,12.0
+2_ense_rvpnot_genELtask_54_04_09,0.7937617996740931,0.539473684210526,0.642367545166265,30.0,17.0,17.0,0.985515704370604,0.344456404736275,0.5104876922067181,34.0,29.0,17.0,0.999989080579005,0.283570300157977,0.441845087948595,41.0,15.0,12.0
 2_enseef_enve_genELtask_29_02_06,0.977429483535976,0.628571428571428,0.765110707176178,34.0,21.0,21.0,0.9137622990635772,0.467550694478052,0.618585649324324,41.0,24.0,22.0,0.9999637895289932,0.291315842993695,0.451188552770254,29.0,10.0,12.0
 2_enseef_enself_genELtask_1_00_00,0.999991559211032,1.0,0.999995779587704,10.0,7.0,2.0,0.999991559211032,1.0,0.999995779587704,10.0,7.0,2.0,0.999991559211032,1.0,0.999995779587704,10.0,7.0,2.0
 2_enseef_rt10v_genELtask_22_01_10,0.495013064265309,0.953563807084933,0.6517107258210231,10.0,6.0,4.0,0.999897528420254,0.974917004795278,0.9872492703630352,17.0,12.0,9.0,,,,,,
 2_enself_rmcv_genELtask_36_03_02,0.282546201232032,0.967626047817648,0.437378232050609,58.0,28.0,16.0,0.9999637396582072,0.8463354997805681,0.9167580133146,37.0,31.0,8.0,0.999982914906712,0.8463354997805681,0.916766071688612,55.0,30.0,15.0
-2_enseef_rutpt_genELtask_25_02_02,0.985072402672326,0.712919969188461,0.8271860328700411,29.0,18.0,18.0,0.9686666193751672,0.691338675620024,0.8068368212742281,31.0,18.0,16.0,0.999958215484349,0.408035700462898,0.5795744516613001,25.0,8.0,12.0
-2_enve_rutpt_genELtask_82_07_04,0.18695248553249,0.9680734812014532,0.313384718095071,58.0,32.0,21.0,0.9822566657555212,0.5774805197071631,0.727345728643454,42.0,29.0,15.0,,,,,,
-2_ense_rutpt_genELtask_12_01_00,0.496860019213674,1.0,0.663869717723749,8.0,4.0,2.0,0.9999144304534152,1.0,0.999957213396092,13.0,10.0,3.0,0.999950574537359,0.8898461254952541,0.9416908648675992,19.0,9.0,6.0
-2_enseef_rutpt_genELtask_14_01_02,0.985072402672326,0.712919969188461,0.8271860328700411,29.0,18.0,18.0,0.9686666193751672,0.691338675620024,0.8068368212742281,31.0,18.0,16.0,0.999958215484349,0.408035700462898,0.5795744516613001,25.0,8.0,12.0
+2_enseef_rvpnot_genELtask_25_02_02,0.985072402672326,0.712919969188461,0.8271860328700411,29.0,18.0,18.0,0.9686666193751672,0.691338675620024,0.8068368212742281,31.0,18.0,16.0,0.999958215484349,0.408035700462898,0.5795744516613001,25.0,8.0,12.0
+2_enve_rvpnot_genELtask_82_07_04,0.18695248553249,0.9680734812014532,0.313384718095071,58.0,32.0,21.0,0.9822566657555212,0.5774805197071631,0.727345728643454,42.0,29.0,15.0,,,,,,
+2_ense_rvpnot_genELtask_12_01_00,0.496860019213674,1.0,0.663869717723749,8.0,4.0,2.0,0.9999144304534152,1.0,0.999957213396092,13.0,10.0,3.0,0.999950574537359,0.8898461254952541,0.9416908648675992,19.0,9.0,6.0
+2_enseef_rvpnot_genELtask_14_01_02,0.985072402672326,0.712919969188461,0.8271860328700411,29.0,18.0,18.0,0.9686666193751672,0.691338675620024,0.8068368212742281,31.0,18.0,16.0,0.999958215484349,0.408035700462898,0.5795744516613001,25.0,8.0,12.0
 2_enseef_rt10v_genELtask_6_00_05,0.999991665235985,1.0,0.9999958326006252,10.0,7.0,2.0,0.999991665235985,1.0,0.9999958326006252,10.0,7.0,2.0,,,,,,
 2_ense_enve_genELtask_48_04_03,0.681790569572092,0.921951219512195,0.78388884214062,23.0,11.0,10.0,0.999895724880524,0.602370689655172,0.751819887024314,41.0,24.0,19.0,0.99997165855727,0.226499189627228,0.369340650287263,25.0,6.0,5.0
 2_ense_rmcv_genELtask_48_04_03,0.347586684851292,0.938228122460038,0.507251278902368,46.0,29.0,16.0,0.943213662107766,0.580070754716981,0.718356539061433,38.0,29.0,12.0,,,,,,
@@ -287,18 +287,18 @@ log,fitness_heu,precision_heu,fscore_heu,size_heu,pnsize_heu,cfc_heu,fitness_imf
 2_rmcv_rt10v_genELtask_15_01_03,0.8116729817460331,0.329336789803413,0.468556502921796,41.0,24.0,22.0,0.953887742276409,0.385348497363237,0.5489385625322261,37.0,22.0,19.0,0.9999917017156172,0.218030743664312,0.35800478917314,39.0,12.0,12.0
 2_enve_rt10v_genELtask_45_04_00,0.9269938176363672,0.230492196878751,0.369187772190117,35.0,18.0,15.0,0.947061948824926,0.457100591715976,0.616598954466908,32.0,25.0,12.0,0.999993951649486,0.147818838566268,0.257564553673138,49.0,17.0,15.0
 2_enself_rt10v_genELtask_20_01_08,0.8183944724113471,0.743941877464227,0.779394168689132,17.0,10.0,10.0,0.951078179224932,0.9711095320272052,0.960989481039565,13.0,8.0,4.0,0.9999778345858292,0.9711095320272052,0.985332282506448,13.0,8.0,3.0
-2_enve_rutpt_genELtask_44_03_10,0.085652738103905,0.266423357664233,0.129630442697284,65.0,37.0,32.0,0.977281799402882,0.145812310797174,0.25376273666963,42.0,26.0,22.0,0.999997903568336,0.091674462114125,0.167951984142366,81.0,30.0,27.0
+2_enve_rvpnot_genELtask_44_03_10,0.085652738103905,0.266423357664233,0.129630442697284,65.0,37.0,32.0,0.977281799402882,0.145812310797174,0.25376273666963,42.0,26.0,22.0,0.999997903568336,0.091674462114125,0.167951984142366,81.0,30.0,27.0
 2_ense_rt10v_genELtask_72_06_05,0.616629767807372,0.6651737451737451,0.6399825369242671,43.0,24.0,22.0,,,,,,,,,,,,
-2_enseef_rutpt_genELtask_13_01_01,0.566584581206791,0.9524940617577192,0.7105207509596471,10.0,6.0,4.0,0.9998906967454492,0.969074442235476,0.9842414170385412,17.0,12.0,9.0,0.999960707606884,0.484216335540838,0.6524791792969891,17.0,6.0,5.0
+2_enseef_rvpnot_genELtask_13_01_01,0.566584581206791,0.9524940617577192,0.7105207509596471,10.0,6.0,4.0,0.9998906967454492,0.969074442235476,0.9842414170385412,17.0,12.0,9.0,0.999960707606884,0.484216335540838,0.6524791792969891,17.0,6.0,5.0
 2_enseef_rt10v_genELtask_12_01_00,0.894659431067044,1.0,0.944401317088618,12.0,5.0,3.0,0.9999314514527552,1.0,0.999965724551611,15.0,11.0,3.0,0.999957862965406,0.8825383965055651,0.9375861487264,22.0,11.0,7.0
 2_ense_enself_genELtask_1_00_00,0.9999916312830692,1.0,0.9999958156240252,10.0,7.0,2.0,0.9999916312830692,1.0,0.9999958156240252,10.0,7.0,2.0,0.9999916312830692,1.0,0.9999958156240252,10.0,7.0,2.0
 2_enseef_enve_genELtask_18_01_06,0.613345297332571,0.737967742969827,0.669909978271778,11.0,2.0,4.0,0.999957103508849,0.998139759202163,0.999047604883979,13.0,6.0,4.0,0.999959541566798,0.740732692504346,0.8510438653337641,14.0,7.0,5.0
-2_rmcv_rutpt_genELtask_25_02_02,0.519346973106247,0.878953591486846,0.6529095371886591,15.0,7.0,6.0,0.9810339324635832,0.8962595444440861,0.936732627273968,21.0,12.0,9.0,0.999965527260202,0.5702309087741231,0.7262928870129151,21.0,8.0,7.0
+2_rmcv_rvpnot_genELtask_25_02_02,0.519346973106247,0.878953591486846,0.6529095371886591,15.0,7.0,6.0,0.9810339324635832,0.8962595444440861,0.936732627273968,21.0,12.0,9.0,0.999965527260202,0.5702309087741231,0.7262928870129151,21.0,8.0,7.0
 2_ense_enseef_genELtask_24_02_01,0.7685778204708981,0.842657342657342,0.803914609914495,13.0,7.0,5.0,0.999917798391837,0.873239436619718,0.9322950990052152,21.0,14.0,11.0,0.999970819356872,0.421768707482993,0.5932962994118981,21.0,7.0,6.0
 2_enseef_enve_genELtask_54_04_09,0.363636363636363,1.0,0.533333333333333,13.0,8.0,2.0,0.999988889012344,0.9996982683958492,0.9998435575857072,14.0,16.0,0.0,0.999988889012344,0.9996982683958492,0.9998435575857072,14.0,16.0,0.0
 2_ense_rt10v_genELtask_55_04_10,0.206715944988224,0.7554549197200491,0.324608826465103,10.0,6.0,4.0,0.963867633092104,0.9097625408575992,0.936033886648766,25.0,16.0,14.0,0.999919222178351,0.275416932120604,0.431877797257563,27.0,7.0,14.0
 2_ense_rmcv_genELtask_59_05_03,0.388467421274604,0.929033804842963,0.54785431586699,57.0,33.0,30.0,0.8962758559930231,0.600252206809583,0.718986264063024,46.0,31.0,24.0,0.999952876760494,0.268774703557312,0.423671782961099,68.0,26.0,21.0
-2_rmcv_rutpt_genELtask_57_05_01,0.99999168371026,1.0,0.99999584183784,10.0,7.0,2.0,0.99999168371026,1.0,0.99999584183784,10.0,7.0,2.0,0.99999168371026,1.0,0.99999584183784,10.0,7.0,2.0
+2_rmcv_rvpnot_genELtask_57_05_01,0.99999168371026,1.0,0.99999584183784,10.0,7.0,2.0,0.99999168371026,1.0,0.99999584183784,10.0,7.0,2.0,0.99999168371026,1.0,0.99999584183784,10.0,7.0,2.0
 2_enseef_rt10v_genELtask_11_00_10,0.49643323179838,0.9955539258376492,0.662507113822913,15.0,10.0,6.0,0.999908339570356,0.986657237152286,0.9932385935667092,22.0,15.0,11.0,0.999954330413416,0.7044823349211421,0.8266076127456181,17.0,8.0,4.0
 2_enself_rt10v_genELtask_31_02_08,0.30492070336831,1.0,0.46733982008445,4.0,3.0,0.0,0.999963970722146,0.931281181884233,0.9644012591969212,28.0,23.0,6.0,0.99997377001967,0.903325988188254,0.949196037113847,41.0,19.0,14.0
 2_enve_rmcv_genELtask_35_03_01,0.273993280248767,0.522123893805309,0.359390408905457,29.0,16.0,14.0,0.969709600204596,0.37037037037037,0.536015329928981,50.0,33.0,26.0,0.999996610180982,0.073684210526315,0.137254870030439,55.0,11.0,15.0
@@ -307,32 +307,32 @@ log,fitness_heu,precision_heu,fscore_heu,size_heu,pnsize_heu,cfc_heu,fitness_imf
 2_enseef_enve_genELtask_2_00_01,0.531166036172118,1.0,0.6938059278013,6.0,5.0,0.0,0.999933202598368,1.0,0.9999666001836732,12.0,9.0,4.0,0.999955531782524,0.8995870419372941,0.94711963959146,10.0,7.0,2.0
 2_ense_rt10v_genELtask_12_01_00,0.534263807355936,1.0,0.696443212431187,5.0,4.0,0.0,0.9999326703470612,1.0,0.999966334040172,11.0,10.0,2.0,,,,,,
 2_enseef_rmcv_genELtask_36_03_02,0.213541666666666,0.822946175637393,0.339093794929465,44.0,29.0,17.0,0.999922471691091,0.8244648318042811,0.903756467566546,30.0,23.0,8.0,0.999956963877504,0.8154869933454321,0.8983498440730501,48.0,22.0,14.0
-2_ense_rutpt_genELtask_33_02_10,0.075527292567843,0.87037037037037,0.138993297438011,60.0,35.0,29.0,0.9816994585606452,0.257540603248259,0.408036309602756,56.0,38.0,34.0,0.9999931250472652,0.133753241941459,0.235947521048283,70.0,26.0,23.0
+2_ense_rvpnot_genELtask_33_02_10,0.075527292567843,0.87037037037037,0.138993297438011,60.0,35.0,29.0,0.9816994585606452,0.257540603248259,0.408036309602756,56.0,38.0,34.0,0.9999931250472652,0.133753241941459,0.235947521048283,70.0,26.0,23.0
 2_rmcv_rt10v_genELtask_48_04_03,0.8351073892541561,1.0,0.9101455251548732,28.0,11.0,9.0,0.999890821669628,0.8957597173144871,0.944965225757406,28.0,16.0,12.0,0.999950272743193,0.277504105090312,0.434442607716483,51.0,17.0,19.0
 2_enself_enve_genELtask_53_04_08,0.375,0.9835115334273912,0.5429719453390031,38.0,21.0,12.0,0.999988461671596,0.750554173080472,0.8575004093473441,22.0,24.0,2.0,,,,,,
 2_enseef_enve_genELtask_42_03_08,0.064328693894322,0.997271773347324,0.120861270537676,12.0,5.0,5.0,0.999880306681391,0.976017298997444,0.9878047055101612,36.0,25.0,11.0,0.9999331463421972,0.872621819939364,0.931949659893318,58.0,19.0,17.0
 2_ense_rt10v_genELtask_47_04_02,0.8322758386208061,0.6427145708582831,0.725314286814653,57.0,32.0,30.0,0.967255231154022,0.250217202432667,0.397584193778772,69.0,46.0,43.0,0.999989473795012,0.095714285714285,0.174706488638787,76.0,21.0,24.0
 2_ense_enve_genELtask_42_03_08,0.252975832631958,1.0,0.403800019192016,24.0,16.0,4.0,0.999955429337059,1.0,0.999977714171882,21.0,23.0,2.0,0.999977714171882,1.0,0.9999888569617752,33.0,22.0,7.0
-2_ense_rutpt_genELtask_77_06_10,0.320727565123668,0.723747180813026,0.444483070500873,69.0,38.0,29.0,0.980241524149148,0.336954450161997,0.501514960928072,37.0,29.0,10.0,,,,,,
-2_enve_rutpt_genELtask_99_08_10,0.31578947368421,0.9620313810037132,0.475496048386301,44.0,25.0,13.0,0.999990625087889,0.5856099393124711,0.7386531796451801,25.0,28.0,2.0,,,,,,
+2_ense_rvpnot_genELtask_77_06_10,0.320727565123668,0.723747180813026,0.444483070500873,69.0,38.0,29.0,0.980241524149148,0.336954450161997,0.501514960928072,37.0,29.0,10.0,,,,,,
+2_enve_rvpnot_genELtask_99_08_10,0.31578947368421,0.9620313810037132,0.475496048386301,44.0,25.0,13.0,0.999990625087889,0.5856099393124711,0.7386531796451801,25.0,28.0,2.0,,,,,,
 2_enve_rt10v_genELtask_76_06_09,0.306458800057352,0.9594654590658852,0.464540640820729,19.0,10.0,10.0,0.9998922335861672,0.67891749012196,0.808720983720944,40.0,22.0,20.0,,,,,,
 2_rmcv_rt10v_genELtask_67_06_00,1.0,1.0,1.0,9.0,5.0,2.0,1.0,1.0,1.0,9.0,5.0,2.0,0.999987500156248,1.0,0.999993750039062,9.0,6.0,2.0
-2_rt10v_rutpt_genELtask_81_07_03,0.553374425817596,0.482287340116094,0.5153911995104531,27.0,2.0,12.0,0.953933622948694,0.683126590888373,0.7961312824912301,34.0,21.0,13.0,0.9999728671381932,0.57455943758994,0.729796202240924,39.0,11.0,12.0
+2_rt10v_rvpnot_genELtask_81_07_03,0.553374425817596,0.482287340116094,0.5153911995104531,27.0,2.0,12.0,0.953933622948694,0.683126590888373,0.7961312824912301,34.0,21.0,13.0,0.9999728671381932,0.57455943758994,0.729796202240924,39.0,11.0,12.0
 2_enseef_enself_genELtask_60_05_04,0.4,0.994257151972774,0.570487101646046,21.0,13.0,5.0,0.99999166673611,0.800817712286903,0.889390124451751,20.0,21.0,2.0,,,,,,
 2_rmcv_rt10v_genELtask_20_01_08,0.818422505919399,0.743902167871039,0.7793850876217471,17.0,10.0,10.0,0.9510168163482252,0.971080948887258,0.9609441612498112,13.0,8.0,4.0,0.9999778326820632,0.971080948887258,0.985317568108224,13.0,8.0,3.0
 2_rmcv_rt10v_genELtask_34_03_00,0.8014184397163121,1.0,0.889763779527559,9.0,2.0,2.0,0.9998801514488552,1.0,0.9999400721332932,11.0,8.0,2.0,0.999919864578228,1.0,0.999959930683628,19.0,6.0,4.0
-2_rt10v_rutpt_genELtask_49_04_04,0.5467132972692871,0.6924004825090471,0.610992399569771,30.0,19.0,11.0,0.9427558787418212,0.734313725490196,0.82558121594924,33.0,22.0,15.0,,,,,,
-2_rt10v_rutpt_genELtask_22_01_10,0.210526315789473,0.9750725137129892,0.346286482124348,44.0,25.0,12.0,0.999991176548442,0.5544359622067461,0.713357424538907,27.0,30.0,2.0,,,,,,
-2_rt10v_rutpt_genELtask_101_09_01,0.999945038918558,0.9389292257582952,0.968477057329068,15.0,8.0,7.0,0.9998508659513352,0.9389292257582952,0.968432885664322,19.0,14.0,11.0,0.999944638404705,0.333237001180068,0.499884701010216,19.0,6.0,7.0
+2_rt10v_rvpnot_genELtask_49_04_04,0.5467132972692871,0.6924004825090471,0.610992399569771,30.0,19.0,11.0,0.9427558787418212,0.734313725490196,0.82558121594924,33.0,22.0,15.0,,,,,,
+2_rt10v_rvpnot_genELtask_22_01_10,0.210526315789473,0.9750725137129892,0.346286482124348,44.0,25.0,12.0,0.999991176548442,0.5544359622067461,0.713357424538907,27.0,30.0,2.0,,,,,,
+2_rt10v_rvpnot_genELtask_101_09_01,0.999945038918558,0.9389292257582952,0.968477057329068,15.0,8.0,7.0,0.9998508659513352,0.9389292257582952,0.968432885664322,19.0,14.0,11.0,0.999944638404705,0.333237001180068,0.499884701010216,19.0,6.0,7.0
 2_enve_rmcv_genELtask_49_04_04,0.5283344527254831,0.9698795180722892,0.6840421653757821,10.0,6.0,4.0,0.999894128995323,0.984375,0.9920738763175012,17.0,12.0,9.0,,,,,,
 2_ense_rt10v_genELtask_41_03_07,0.598290598290598,0.771708193938183,0.674023597211946,12.0,2.0,4.0,0.999967143936699,0.9963889605364972,0.998174845545062,14.0,6.0,4.0,0.999967143936699,0.9963889605364972,0.998174845545062,14.0,6.0,4.0
 2_ense_rt10v_genELtask_22_01_10,0.450284905607828,1.0,0.6209606179678321,20.0,13.0,8.0,0.999898588309388,0.990870853646878,0.995364251507531,23.0,15.0,12.0,0.999949492693876,0.6335977016763701,0.775693169498589,26.0,10.0,7.0
 2_enseef_rt10v_genELtask_27_02_04,0.190084640558289,0.498273878020713,0.275188607235291,41.0,21.0,17.0,0.947881408389104,0.6741130091984231,0.787893203137904,42.0,25.0,22.0,0.999985865350075,0.196746800621935,0.328801954302776,50.0,14.0,14.0
 2_enve_rt10v_genELtask_17_01_05,0.49908905797878,1.0,0.665856448384329,5.0,4.0,0.0,0.99992472598505,1.0,0.9999623615759272,11.0,8.0,4.0,0.999949873798828,0.8893022368483801,0.9413859108522232,9.0,6.0,2.0
-2_enself_rutpt_genELtask_2_00_01,0.6218359364189561,0.944055944055944,0.749793672633223,13.0,7.0,5.0,0.9999180100614332,0.959824231010671,0.9794609872325792,21.0,14.0,11.0,0.999966092672626,0.455941553600715,0.626311833732338,21.0,7.0,6.0
-2_enself_rutpt_genELtask_20_01_08,0.116399053910211,0.8955524757465031,0.206020659782395,28.0,18.0,7.0,0.9536391734296712,0.493613701343598,0.650514323167768,29.0,23.0,8.0,,,,,,
+2_enself_rvpnot_genELtask_2_00_01,0.6218359364189561,0.944055944055944,0.749793672633223,13.0,7.0,5.0,0.9999180100614332,0.959824231010671,0.9794609872325792,21.0,14.0,11.0,0.999966092672626,0.455941553600715,0.626311833732338,21.0,7.0,6.0
+2_enself_rvpnot_genELtask_20_01_08,0.116399053910211,0.8955524757465031,0.206020659782395,28.0,18.0,7.0,0.9536391734296712,0.493613701343598,0.650514323167768,29.0,23.0,8.0,,,,,,
 2_ense_enve_genELtask_37_03_03,0.446139041579552,0.5585585585585581,0.496059271858748,16.0,8.0,5.0,0.999913730226575,0.687898089171974,0.815065680254322,25.0,16.0,12.0,0.9999725497731432,0.305084745762711,0.467529467343372,26.0,8.0,5.0
-2_enself_rutpt_genELtask_44_03_10,0.209831952700541,0.6200640341515471,0.313555551892513,51.0,30.0,21.0,0.996138374333927,0.318769342799927,0.482981993012197,25.0,30.0,2.0,0.999993750039062,0.38136574074074,0.552156567157578,34.0,34.0,1.0
+2_enself_rvpnot_genELtask_44_03_10,0.209831952700541,0.6200640341515471,0.313555551892513,51.0,30.0,21.0,0.996138374333927,0.318769342799927,0.482981993012197,25.0,30.0,2.0,0.999993750039062,0.38136574074074,0.552156567157578,34.0,34.0,1.0
 2_ense_rt10v_genELtask_68_06_01,0.097321400940021,0.8689927583936801,0.175039539339713,58.0,30.0,22.0,0.9434763818048292,0.242954343005515,0.386405509717821,40.0,33.0,14.0,0.999993453454703,0.274537939305677,0.430803263991845,57.0,30.0,11.0
 2_enve_rt10v_genELtask_64_05_08,0.782173785297342,0.8153777380420201,0.7984307016006431,45.0,24.0,24.0,0.987673807344384,0.511484446777792,0.6739512517206041,58.0,35.0,33.0,0.999966773056056,0.152649238597396,0.264865601356225,67.0,20.0,26.0
 2_enself_enve_genELtask_29_02_06,0.242424242424242,0.6962365591397851,0.359628568949058,61.0,37.0,26.0,0.999937502083263,0.353936718175128,0.522817544749526,40.0,32.0,12.0,0.999983333611106,0.657103825136612,0.793069779244795,70.0,38.0,23.0
@@ -341,20 +341,20 @@ log,fitness_heu,precision_heu,fscore_heu,size_heu,pnsize_heu,cfc_heu,fitness_imf
 2_ense_rmcv_genELtask_46_04_01,0.7238315165098871,1.0,0.8397938076632631,6.0,5.0,0.0,0.9999834059265392,0.9910951152997052,0.995519421689286,12.0,9.0,2.0,0.9999834059265392,0.9910951152997052,0.995519421689286,12.0,9.0,2.0
 2_enseef_rt10v_genELtask_47_04_02,0.153844970423304,0.93290478103656,0.264132029022377,25.0,16.0,3.0,0.999987500156248,0.6843311372099681,0.8125809071943291,19.0,23.0,0.0,0.99999166673611,0.6843311372099681,0.812582282796097,24.0,25.0,0.0
 2_enseef_enve_genELtask_52_04_07,0.16037218428605,0.886588008668432,0.271613107105319,44.0,30.0,11.0,0.999991176548442,0.434048412813451,0.605345328299401,27.0,32.0,2.0,,,,,,
-2_enself_rutpt_genELtask_55_04_10,0.307692307692307,1.0,0.470588235294117,4.0,3.0,0.0,,,,,,,,,,,,
+2_enself_rvpnot_genELtask_55_04_10,0.307692307692307,1.0,0.470588235294117,4.0,3.0,0.0,,,,,,,,,,,,
 2_enseef_enve_genELtask_3_00_02,0.5318263962594301,1.0,0.6943690193067541,6.0,5.0,0.0,0.999933064904474,1.0,0.999966531332122,12.0,9.0,4.0,0.999955440019612,0.900236966824644,0.947479685784093,10.0,7.0,2.0
 2_ense_rt10v_genELtask_40_03_06,0.347586684851292,0.938228122460038,0.507251278902368,46.0,29.0,16.0,0.943213662107766,0.580070754716981,0.718356539061433,38.0,29.0,12.0,0.9999801548798732,0.395704287667927,0.567028509892201,47.0,22.0,12.0
 2_ense_rmcv_genELtask_17_01_05,0.5196599362380441,1.0,0.683916083916083,5.0,4.0,0.0,0.9999249675872772,1.0,0.99996248238612,11.0,8.0,4.0,0.999949442556184,0.8954154727793691,0.944799806746868,9.0,6.0,2.0
 2_enve_rmcv_genELtask_37_03_03,0.463533646971744,1.0,0.633444469050452,9.0,6.0,4.0,,,,,,,,,,,,
 2_enself_rmcv_genELtask_17_01_05,0.5617173524150261,1.0,0.719358533791523,5.0,4.0,0.0,0.999934310260649,1.0,0.999967154051504,11.0,10.0,2.0,0.999955719367998,1.0,0.9999778591937952,15.0,8.0,4.0
-2_rmcv_rutpt_genELtask_11_00_10,0.3,0.966661399905198,0.457894145970287,45.0,26.0,13.0,,,,,,,,,,,,
+2_rmcv_rvpnot_genELtask_11_00_10,0.3,0.966661399905198,0.457894145970287,45.0,26.0,13.0,,,,,,,,,,,,
 2_enseef_rt10v_genELtask_19_01_07,0.6133767643865361,0.7376842248908291,0.669811898343007,11.0,2.0,4.0,0.9999570970663432,0.998026784572378,0.998991008352798,13.0,6.0,4.0,0.9999595412678112,0.743520988729787,0.852881226972177,14.0,7.0,5.0
-2_rt10v_rutpt_genELtask_68_06_01,0.241823587710604,0.9997177373828612,0.389443871203645,23.0,16.0,2.0,0.999957591875312,0.9997574463956532,0.999857509119502,22.0,24.0,2.0,0.999978795488034,0.9997574463956532,0.9998681086913732,34.0,23.0,7.0
+2_rt10v_rvpnot_genELtask_68_06_01,0.241823587710604,0.9997177373828612,0.389443871203645,23.0,16.0,2.0,0.999957591875312,0.9997574463956532,0.999857509119502,22.0,24.0,2.0,0.999978795488034,0.9997574463956532,0.9998681086913732,34.0,23.0,7.0
 2_enve_rmcv_genELtask_104_09_04,0.9018191917789192,1.0,0.948375319459656,21.0,2.0,2.0,0.983316411802746,1.0,0.991588035021558,31.0,12.0,8.0,0.999824489740158,0.8648055580350501,0.9274266247275292,38.0,20.0,17.0
-2_enself_rutpt_genELtask_21_01_09,0.36672342480924,0.857434297371894,0.5137266814293701,32.0,17.0,15.0,,,,,,,,,,,,
-2_enseef_rutpt_genELtask_12_01_00,0.5233431314090761,1.0,0.687098160117069,7.0,4.0,2.0,0.999923095610512,1.0,0.999961546326627,12.0,10.0,3.0,0.999952861201817,0.96250029187195,0.980869193496373,17.0,8.0,5.0
+2_enself_rvpnot_genELtask_21_01_09,0.36672342480924,0.857434297371894,0.5137266814293701,32.0,17.0,15.0,,,,,,,,,,,,
+2_enseef_rvpnot_genELtask_12_01_00,0.5233431314090761,1.0,0.687098160117069,7.0,4.0,2.0,0.999923095610512,1.0,0.999961546326627,12.0,10.0,3.0,0.999952861201817,0.96250029187195,0.980869193496373,17.0,8.0,5.0
 2_enve_rmcv_genELtask_7_00_06,0.999991638174163,1.0,0.9999958190696012,10.0,7.0,2.0,0.999991638174163,1.0,0.9999958190696012,10.0,7.0,2.0,0.999991638174163,1.0,0.9999958190696012,10.0,7.0,2.0
-2_ense_rutpt_genELtask_62_05_06,0.7228476141025201,0.750348675034867,0.7363414549618901,17.0,9.0,7.0,0.964476420694238,0.745952342531332,0.841255082703752,19.0,12.0,7.0,0.999984835198752,0.5786772376382221,0.733112516059901,18.0,9.0,5.0
+2_ense_rvpnot_genELtask_62_05_06,0.7228476141025201,0.750348675034867,0.7363414549618901,17.0,9.0,7.0,0.964476420694238,0.745952342531332,0.841255082703752,19.0,12.0,7.0,0.999984835198752,0.5786772376382221,0.733112516059901,18.0,9.0,5.0
 2_rmcv_rt10v_genELtask_19_01_07,0.8175124813927731,0.742236384704519,0.778057957699381,17.0,10.0,10.0,0.9516723887888632,0.960465482820216,0.956048717988713,13.0,8.0,4.0,0.9999778593624592,0.960465482820216,0.979823488734719,13.0,8.0,3.0
 2_rmcv_rt10v_genELtask_43_03_09,0.6009155200856531,0.920740851829634,0.727217363989869,12.0,7.0,5.0,0.999896208742133,0.943272688241292,0.970759449934231,20.0,14.0,11.0,0.999965154770266,0.41289231347491,0.5844580014999,24.0,8.0,7.0
 2_ense_rt10v_genELtask_70_06_03,0.351534528523659,0.950198728139904,0.513204468258849,35.0,22.0,14.0,0.9678588273820752,0.482041039671682,0.64355847739294,34.0,26.0,12.0,,,,,,
@@ -362,9 +362,9 @@ log,fitness_heu,precision_heu,fscore_heu,size_heu,pnsize_heu,cfc_heu,fitness_imf
 2_ense_enve_genELtask_41_03_07,0.479342596883266,0.995290611028316,0.647056072770133,19.0,10.0,6.0,0.9999210644104072,0.9958948243608392,0.9979038832372412,22.0,15.0,7.0,0.99995815074711,0.8186147833329771,0.9002449223516721,24.0,11.0,5.0
 2_ense_rt10v_genELtask_32_02_09,0.5561384879116,0.957592339261286,0.703630983834987,10.0,6.0,4.0,0.999891460104102,0.9729513560677672,0.9862374681390872,17.0,12.0,9.0,0.999959875845226,0.494183255161207,0.6614673202599121,17.0,6.0,5.0
 2_rmcv_rt10v_genELtask_55_04_10,0.483852163566945,0.9533527696793,0.64191513622203,10.0,6.0,4.0,0.999897353686636,0.975166704989652,0.987377197655018,17.0,12.0,9.0,0.999951078031064,0.492438096896862,0.659900264794353,17.0,6.0,5.0
-2_enseef_rutpt_genELtask_37_03_03,0.097161517353983,0.9997462250983372,0.177110355666386,13.0,7.0,7.0,0.9999588466820092,0.8019635250998821,0.8900833179033131,41.0,28.0,12.0,0.99997290208023,0.712265027028612,0.831947142414667,52.0,22.0,18.0
+2_enseef_rvpnot_genELtask_37_03_03,0.097161517353983,0.9997462250983372,0.177110355666386,13.0,7.0,7.0,0.9999588466820092,0.8019635250998821,0.8900833179033131,41.0,28.0,12.0,0.99997290208023,0.712265027028612,0.831947142414667,52.0,22.0,18.0
 2_ense_rt10v_genELtask_62_05_06,0.516239010540614,0.7416062651003911,0.608733191466726,18.0,9.0,8.0,0.965461003804118,0.7939962950488381,0.8713737588679801,18.0,12.0,6.0,0.9999846366818572,0.624401280211897,0.7687725938062531,19.0,9.0,5.0
-2_enve_rutpt_genELtask_62_05_06,0.8527023528647291,0.5054777070063691,0.6347052836660061,73.0,43.0,40.0,,,,,,,,,,,,
+2_enve_rvpnot_genELtask_62_05_06,0.8527023528647291,0.5054777070063691,0.6347052836660061,73.0,43.0,40.0,,,,,,,,,,,,
 2_ense_enself_genELtask_47_04_02,0.229316087622474,0.995201652089407,0.372743884145383,25.0,18.0,2.0,0.99995964837339,0.99579714578318,0.9978740562618432,23.0,26.0,2.0,0.999979823779623,0.99579714578318,0.9978841018279232,37.0,26.0,8.0
 2_enself_rmcv_genELtask_7_00_06,0.5098712446351931,1.0,0.6753837407617961,5.0,4.0,0.0,0.999925088118163,1.0,0.9999625426560812,11.0,8.0,4.0,0.999949776861626,0.8918918918918911,0.942834818591985,9.0,6.0,2.0
 2_enve_rmcv_genELtask_79_07_01,0.5650340238099081,0.8096306161071001,0.6655715605608481,17.0,9.0,7.0,,,,,,,,,,,,
@@ -372,15 +372,15 @@ log,fitness_heu,precision_heu,fscore_heu,size_heu,pnsize_heu,cfc_heu,fitness_imf
 2_enself_rmcv_genELtask_45_04_00,0.3,0.9664890989439392,0.457874813016477,45.0,26.0,13.0,,,,,,,,,,,,
 2_enself_rt10v_genELtask_22_01_10,0.5206643144481651,1.0,0.684785339540382,6.0,5.0,0.0,0.999921708047404,0.997359978612484,0.9986392004826332,14.0,10.0,5.0,0.99995590516302,0.9025887618701992,0.94878083863681,11.0,7.0,2.0
 2_enve_rt10v_genELtask_6_00_05,0.9999916605312,1.0,0.999995830248213,10.0,7.0,2.0,0.9999916605312,1.0,0.999995830248213,10.0,7.0,2.0,0.9999916605312,1.0,0.999995830248213,10.0,7.0,2.0
-2_ense_rutpt_genELtask_26_02_03,0.6940525134467761,0.8632478632478631,0.7694589408453211,13.0,7.0,5.0,0.999920247650611,0.89587852494577,0.9450444735775312,21.0,14.0,11.0,0.9999703112728092,0.42380708055413,0.5953100543583381,21.0,7.0,6.0
+2_ense_rvpnot_genELtask_26_02_03,0.6940525134467761,0.8632478632478631,0.7694589408453211,13.0,7.0,5.0,0.999920247650611,0.89587852494577,0.9450444735775312,21.0,14.0,11.0,0.9999703112728092,0.42380708055413,0.5953100543583381,21.0,7.0,6.0
 2_enseef_enve_genELtask_1_00_00,0.9999916385568592,1.0,0.999995819260951,10.0,7.0,2.0,0.9999916385568592,1.0,0.999995819260951,10.0,7.0,2.0,0.9999916385568592,1.0,0.999995819260951,10.0,7.0,2.0
 2_enseef_enve_genELtask_15_01_03,0.490810510792208,0.8888888888888881,0.632421829992699,10.0,6.0,4.0,0.999895386607173,0.940860215053763,0.969479915626026,17.0,12.0,9.0,0.999947979584877,0.48342541436464,0.6517580379230601,17.0,6.0,5.0
 2_enself_enve_genELtask_2_00_01,0.497780299742982,1.0,0.664690675699768,5.0,4.0,0.0,0.9999249011158812,1.0,0.9999624491479272,11.0,8.0,4.0,0.999950005003004,0.8884391263733821,0.940902162696102,9.0,6.0,2.0
-2_enself_rutpt_genELtask_3_00_02,0.985072402672326,0.712919969188461,0.8271860328700411,29.0,18.0,18.0,0.9686666193751672,0.691338675620024,0.8068368212742281,31.0,18.0,16.0,0.999958215484349,0.408035700462898,0.5795744516613001,25.0,8.0,12.0
-2_rt10v_rutpt_genELtask_42_03_08,0.312287466491302,0.987588352666644,0.474524504641332,29.0,19.0,8.0,0.98237034212894,0.435388175739327,0.603364290560629,35.0,27.0,12.0,,,,,,
+2_enself_rvpnot_genELtask_3_00_02,0.985072402672326,0.712919969188461,0.8271860328700411,29.0,18.0,18.0,0.9686666193751672,0.691338675620024,0.8068368212742281,31.0,18.0,16.0,0.999958215484349,0.408035700462898,0.5795744516613001,25.0,8.0,12.0
+2_rt10v_rvpnot_genELtask_42_03_08,0.312287466491302,0.987588352666644,0.474524504641332,29.0,19.0,8.0,0.98237034212894,0.435388175739327,0.603364290560629,35.0,27.0,12.0,,,,,,
 2_enve_rmcv_genELtask_92_08_03,0.327855387566347,0.9415849673202612,0.486361888841962,41.0,22.0,14.0,0.9999371032636872,0.9435377730981832,0.9709190884091412,26.0,23.0,7.0,0.99997216272254,0.910811840935078,0.953311818258602,37.0,22.0,8.0
 2_ense_enve_genELtask_62_05_06,0.8651006702360451,0.7858985382631121,0.8235998523669721,31.0,18.0,18.0,0.936916705801989,0.64647137150466,0.7650554358293781,49.0,31.0,30.0,0.999960109408034,0.288055624534392,0.447268035994172,43.0,16.0,16.0
-2_ense_rutpt_genELtask_39_03_05,0.8018559694719041,0.9894514767932492,0.8858307096569921,21.0,12.0,8.0,0.9999398150640272,0.814419225634178,0.897694658662313,32.0,21.0,15.0,0.999983551672421,0.456245325355272,0.6266018042629821,21.0,10.0,4.0
+2_ense_rvpnot_genELtask_39_03_05,0.8018559694719041,0.9894514767932492,0.8858307096569921,21.0,12.0,8.0,0.9999398150640272,0.814419225634178,0.897694658662313,32.0,21.0,15.0,0.999983551672421,0.456245325355272,0.6266018042629821,21.0,10.0,4.0
 2_enseef_enve_genELtask_27_02_04,0.7692069032383161,0.336588011933821,0.468270958338513,64.0,36.0,35.0,0.9863828275494072,0.279194412863407,0.435204688583645,47.0,30.0,27.0,0.999986394742928,0.088195576251455,0.162094904489872,82.0,26.0,29.0
 2_ense_enseef_genELtask_83_07_05,0.333333333333333,0.933380796209194,0.491234643734643,35.0,25.0,3.0,,,,,,,,,,,,
 2_enself_rt10v_genELtask_8_00_07,0.9887392459504112,0.651352771210927,0.7853437991478861,30.0,19.0,19.0,0.971386638892584,0.6430891108596171,0.7738588455134421,31.0,18.0,16.0,,,,,,
@@ -388,11 +388,11 @@ log,fitness_heu,precision_heu,fscore_heu,size_heu,pnsize_heu,cfc_heu,fitness_imf
 2_ense_rt10v_genELtask_79_07_01,0.26677472387524,1.0,0.421187317440529,38.0,26.0,6.0,,,,,,,,,,,,
 2_enve_rt10v_genELtask_57_05_01,,,,,,,0.893276650984517,0.269808269808269,0.414438229493986,54.0,38.0,31.0,0.9999944648251032,0.143686844838983,0.251269384736854,57.0,22.0,17.0
 2_ense_enve_genELtask_53_04_08,,,,,,,0.9999857210610832,0.977862695146645,0.988800480655492,14.0,11.0,2.0,0.9999857210610832,0.977862695146645,0.988800480655492,14.0,11.0,2.0
-2_enseef_rutpt_genELtask_38_03_04,,,,,,,0.81599256308025,0.470059989424356,0.5965004382205941,71.0,43.0,42.0,0.999977160503212,0.285677177334201,0.444397291252874,81.0,26.0,26.0
+2_enseef_rvpnot_genELtask_38_03_04,,,,,,,0.81599256308025,0.470059989424356,0.5965004382205941,71.0,43.0,42.0,0.999977160503212,0.285677177334201,0.444397291252874,81.0,26.0,26.0
 2_ense_rt10v_genELtask_57_05_01,,,,,,,0.9266241005953252,0.309025836036237,0.463482057318599,39.0,22.0,19.0,,,,,,
-2_ense_rutpt_genELtask_1_00_00,,,,,,,0.99999166673611,1.0,0.999995833350694,10.0,9.0,0.0,0.99999166673611,1.0,0.999995833350694,10.0,9.0,0.0
+2_ense_rvpnot_genELtask_1_00_00,,,,,,,0.99999166673611,1.0,0.999995833350694,10.0,9.0,0.0,0.99999166673611,1.0,0.999995833350694,10.0,9.0,0.0
 2_enve_rt10v_genELtask_84_07_06,,,,,,,0.9817738713436852,0.6244255920820071,0.763348195369119,37.0,29.0,10.0,0.9999816474940112,0.499893880438627,0.6665682542633541,46.0,24.0,11.0
-2_enseef_rutpt_genELtask_1_00_00,,,,,,,0.99999166673611,1.0,0.999995833350694,10.0,9.0,0.0,,,,,,
+2_enseef_rvpnot_genELtask_1_00_00,,,,,,,0.99999166673611,1.0,0.999995833350694,10.0,9.0,0.0,,,,,,
 2_enself_rmcv_genELtask_25_02_02,,,,,,,0.9999146614001352,0.996760758907913,0.998335219238167,20.0,17.0,4.0,0.99994802764929,0.996760758907913,0.998351849421906,27.0,14.0,7.0
 2_enseef_enve_genELtask_21_01_09,,,,,,,0.999932225786072,0.987593908310983,0.9937247696708212,33.0,23.0,13.0,,,,,,
 2_enve_rt10v_genELtask_46_04_01,,,,,,,0.8424889688357761,0.250871080139372,0.386617597406179,58.0,38.0,35.0,0.999996451625494,0.081820367921125,0.151264199471603,93.0,47.0,43.0
@@ -400,43 +400,43 @@ log,fitness_heu,precision_heu,fscore_heu,size_heu,pnsize_heu,cfc_heu,fitness_imf
 2_enself_rt10v_genELtask_36_03_02,,,,,,,0.9999828462634032,0.8860958881632921,0.93960095316717,19.0,21.0,2.0,0.9999902372560172,0.8548047548949771,0.921715244296296,31.0,23.0,3.0
 2_enseef_enself_genELtask_48_04_03,,,,,,,0.9999886896958452,0.84494014466801,0.915949246822108,28.0,29.0,2.0,,,,,,
 2_ense_enseef_genELtask_25_02_02,,,,,,,0.999942724949656,1.0,0.9999713616546972,22.0,20.0,4.0,0.9999713616546972,1.0,0.999985680622306,33.0,18.0,8.0
-2_ense_rutpt_genELtask_71_06_04,,,,,,,0.6811060735783051,0.5406274612759251,0.602790439835454,83.0,52.0,50.0,,,,,,
+2_ense_rvpnot_genELtask_71_06_04,,,,,,,0.6811060735783051,0.5406274612759251,0.602790439835454,83.0,52.0,50.0,,,,,,
 2_enself_rt10v_genELtask_7_00_06,,,,,,,0.825098095707526,0.5669634528957851,0.6720973879199981,76.0,49.0,47.0,,,,,,
-2_enself_rutpt_genELtask_22_01_10,,,,,,,0.8673395372282791,0.178633975481611,0.296252835745452,61.0,41.0,37.0,0.999993855644512,0.169710250791331,0.290174595604925,67.0,28.0,23.0
-2_enself_rutpt_genELtask_42_03_08,,,,,,,0.9999860236933932,0.8974057419578001,0.945922940939938,25.0,25.0,2.0,0.999995997402068,0.8974057419578001,0.945927403129916,41.0,29.0,6.0
+2_enself_rvpnot_genELtask_22_01_10,,,,,,,0.8673395372282791,0.178633975481611,0.296252835745452,61.0,41.0,37.0,0.999993855644512,0.169710250791331,0.290174595604925,67.0,28.0,23.0
+2_enself_rvpnot_genELtask_42_03_08,,,,,,,0.9999860236933932,0.8974057419578001,0.945922940939938,25.0,25.0,2.0,0.999995997402068,0.8974057419578001,0.945927403129916,41.0,29.0,6.0
 2_ense_enself_genELtask_24_02_01,,,,,,,0.9999417629321212,1.0,0.999970880618146,22.0,16.0,6.0,0.9999667047301932,1.0,0.999983352087948,27.0,13.0,7.0
 2_ense_rmcv_genELtask_58_05_02,,,,,,,0.8758294326838441,0.432375189107413,0.5789414137124801,35.0,22.0,20.0,0.999986040803998,0.174160878828965,0.296655290357639,32.0,8.0,12.0
-2_ense_rutpt_genELtask_66_05_10,,,,,,,0.765903362315955,0.440708604483007,0.559484260496061,58.0,39.0,34.0,0.999994564095909,0.185060893098782,0.312322745744907,55.0,18.0,16.0
-2_enve_rutpt_genELtask_60_05_04,,,,,,,0.943003838393027,0.431834459564662,0.592391924962781,63.0,39.0,37.0,,,,,,
-2_rmcv_rutpt_genELtask_45_04_00,,,,,,,0.999939565469116,1.0,0.999969781821447,12.0,11.0,2.0,0.999959891764004,1.0,0.999979945479826,16.0,9.0,4.0
+2_ense_rvpnot_genELtask_66_05_10,,,,,,,0.765903362315955,0.440708604483007,0.559484260496061,58.0,39.0,34.0,0.999994564095909,0.185060893098782,0.312322745744907,55.0,18.0,16.0
+2_enve_rvpnot_genELtask_60_05_04,,,,,,,0.943003838393027,0.431834459564662,0.592391924962781,63.0,39.0,37.0,,,,,,
+2_rmcv_rvpnot_genELtask_45_04_00,,,,,,,0.999939565469116,1.0,0.999969781821447,12.0,11.0,2.0,0.999959891764004,1.0,0.999979945479826,16.0,9.0,4.0
 2_enseef_rt10v_genELtask_17_01_05,,,,,,,0.99993984006207,0.987400530503978,0.9936306263110132,29.0,20.0,8.0,0.999965543575618,0.56188679245283,0.7194885443162921,25.0,12.0,6.0
-2_enseef_rutpt_genELtask_54_04_09,,,,,,,0.999981237768662,0.8710217755443881,0.9310572212174212,18.0,20.0,2.0,0.999989203513249,0.841676158503686,0.914028236309364,27.0,22.0,3.0
+2_enseef_rvpnot_genELtask_54_04_09,,,,,,,0.999981237768662,0.8710217755443881,0.9310572212174212,18.0,20.0,2.0,0.999989203513249,0.841676158503686,0.914028236309364,27.0,22.0,3.0
 2_ense_rmcv_genELtask_18_01_06,,,,,,,0.999990000099999,1.0,0.9999950000249992,9.0,8.0,0.0,,,,,,
 2_ense_rt10v_genELtask_61_05_05,,,,,,,0.999981050008008,0.8231072364220771,0.9029641018189972,19.0,12.0,5.0,0.999987675050598,0.8231072364220771,0.90296680275647,18.0,11.0,4.0
-2_enve_rutpt_genELtask_102_09_02,,,,,,,0.9999170610110152,0.943943512563813,0.971124406425324,32.0,22.0,10.0,0.9999529422841532,0.943943512563813,0.9711413284222972,53.0,22.0,16.0
+2_enve_rvpnot_genELtask_102_09_02,,,,,,,0.9999170610110152,0.943943512563813,0.971124406425324,32.0,22.0,10.0,0.9999529422841532,0.943943512563813,0.9711413284222972,53.0,22.0,16.0
 2_enseef_rt10v_genELtask_41_03_07,,,,,,,0.999919586578569,0.8652321630804071,0.927712811528636,33.0,23.0,10.0,0.9999545801015092,0.8652321630804071,0.927727872274508,58.0,24.0,17.0
 2_ense_enseef_genELtask_69_06_02,,,,,,,0.945685191537984,0.507638900441974,0.6606462982975451,28.0,22.0,8.0,0.999991271385349,0.422792878091587,0.5943124793046201,34.0,19.0,8.0
 2_enve_rmcv_genELtask_40_03_06,,,,,,,0.999990000099999,1.0,0.9999950000249992,9.0,8.0,0.0,0.999990000099999,1.0,0.9999950000249992,9.0,8.0,0.0
-2_rmcv_rutpt_genELtask_26_02_03,,,,,,,0.954121079554186,0.683627547963718,0.796536712797817,34.0,21.0,13.0,0.9999728687179172,0.575028126757922,0.7301741740599811,39.0,11.0,12.0
-2_enseef_rutpt_genELtask_40_03_06,,,,,,,0.8545808254116001,0.288300684045754,0.431149221506838,76.0,50.0,48.0,,,,,,
+2_rmcv_rvpnot_genELtask_26_02_03,,,,,,,0.954121079554186,0.683627547963718,0.796536712797817,34.0,21.0,13.0,0.9999728687179172,0.575028126757922,0.7301741740599811,39.0,11.0,12.0
+2_enseef_rvpnot_genELtask_40_03_06,,,,,,,0.8545808254116001,0.288300684045754,0.431149221506838,76.0,50.0,48.0,,,,,,
 2_ense_enself_genELtask_70_06_03,,,,,,,0.999980075778932,0.645676686685236,0.7846883223855141,29.0,28.0,7.0,,,,,,
-2_enve_rutpt_genELtask_64_05_08,,,,,,,0.8957168225337151,0.304556354916067,0.454556938592062,55.0,34.0,33.0,0.999994200277837,0.074003286653329,0.137808250680889,116.0,54.0,54.0
+2_enve_rvpnot_genELtask_64_05_08,,,,,,,0.8957168225337151,0.304556354916067,0.454556938592062,55.0,34.0,33.0,0.999994200277837,0.074003286653329,0.137808250680889,116.0,54.0,54.0
 2_ense_rt10v_genELtask_42_03_08,,,,,,,0.99998337737943,0.98881232579746,0.994366477729066,12.0,9.0,2.0,0.99998337737943,0.98881232579746,0.994366477729066,12.0,9.0,2.0
-2_enseef_rutpt_genELtask_2_00_01,,,,,,,0.999991753994698,1.0,0.99999587698035,10.0,7.0,2.0,0.999991753994698,1.0,0.99999587698035,10.0,7.0,2.0
+2_enseef_rvpnot_genELtask_2_00_01,,,,,,,0.999991753994698,1.0,0.99999587698035,10.0,7.0,2.0,0.999991753994698,1.0,0.99999587698035,10.0,7.0,2.0
 2_enve_rt10v_genELtask_97_08_08,,,,,,,0.9999669367480972,0.984313573349886,0.992078512824434,17.0,11.0,4.0,0.9999736050040532,0.984313573349886,0.992081794545102,18.0,10.0,5.0
 2_enself_rmcv_genELtask_14_01_02,,,,,,,0.920959964990204,0.5279854973940631,0.671182619110083,39.0,25.0,23.0,0.999989296647716,0.11335847606236,0.203633160321012,50.0,17.0,20.0
 2_enve_rt10v_genELtask_91_08_02,,,,,,,0.99998928582908,0.689261434502304,0.816047069084302,21.0,27.0,0.0,0.9999928571938772,0.689261434502304,0.8160482582517831,28.0,31.0,0.0
 2_ense_enve_genELtask_29_02_06,,,,,,,0.9999000076467032,0.9722697706264972,0.98589133835659,18.0,13.0,9.0,0.9999617650202992,0.492115751169641,0.6596130962290311,18.0,7.0,5.0
 2_ense_enself_genELtask_58_05_02,,,,,,,0.9999188138470272,0.8618945563651951,0.925790518266241,39.0,26.0,12.0,0.999955121528973,0.8618945563651951,0.9258060799302712,64.0,26.0,19.0
-2_enve_rutpt_genELtask_66_05_10,,,,,,,0.913914537819396,0.461866212185769,0.613624294194503,47.0,30.0,27.0,0.999996246137806,0.094009350197513,0.171862004388785,93.0,47.0,43.0
+2_enve_rvpnot_genELtask_66_05_10,,,,,,,0.913914537819396,0.461866212185769,0.613624294194503,47.0,30.0,27.0,0.999996246137806,0.094009350197513,0.171862004388785,93.0,47.0,43.0
 2_enself_rmcv_genELtask_2_00_01,,,,,,,0.962323500957618,0.6353733362700981,0.765395135206638,23.0,16.0,12.0,0.999982328259032,0.410306010484821,0.581865067435416,20.0,7.0,8.0
-2_rmcv_rutpt_genELtask_56_05_00,,,,,,,0.999991683073928,1.0,0.999995841519671,10.0,7.0,2.0,0.999991683073928,1.0,0.999995841519671,10.0,7.0,2.0
-2_rt10v_rutpt_genELtask_30_02_07,,,,,,,0.99998928582908,0.967019908515244,0.9832282944879692,20.0,24.0,0.0,0.999996428584183,0.967019908515244,0.9832317471353752,32.0,30.0,0.0
+2_rmcv_rvpnot_genELtask_56_05_00,,,,,,,0.999991683073928,1.0,0.999995841519671,10.0,7.0,2.0,0.999991683073928,1.0,0.999995841519671,10.0,7.0,2.0
+2_rt10v_rvpnot_genELtask_30_02_07,,,,,,,0.99998928582908,0.967019908515244,0.9832282944879692,20.0,24.0,0.0,0.999996428584183,0.967019908515244,0.9832317471353752,32.0,30.0,0.0
 2_ense_rmcv_genELtask_7_00_06,,,,,,,0.99999166673611,1.0,0.999995833350694,10.0,9.0,0.0,0.99999166673611,1.0,0.999995833350694,10.0,9.0,0.0
 2_enseef_enself_genELtask_49_04_04,,,,,,,0.99999166673611,0.5359673318592171,0.6978869435865881,25.0,31.0,0.0,,,,,,
 2_enself_enve_genELtask_17_01_05,,,,,,,0.9443552209521392,0.494665625813166,0.649247114678399,40.0,29.0,19.0,0.9999961553853712,0.106507081445542,0.192510366751208,60.0,20.0,16.0
-2_enve_rutpt_genELtask_1_00_00,,,,,,,0.9999900254933972,1.0,0.9999950127218252,9.0,6.0,2.0,0.9999900254933972,1.0,0.9999950127218252,9.0,6.0,2.0
-2_ense_rutpt_genELtask_75_06_08,,,,,,,0.9507021408374272,0.506187112645571,0.6606310953368241,29.0,23.0,8.0,,,,,,
+2_enve_rvpnot_genELtask_1_00_00,,,,,,,0.9999900254933972,1.0,0.9999950127218252,9.0,6.0,2.0,0.9999900254933972,1.0,0.9999950127218252,9.0,6.0,2.0
+2_ense_rvpnot_genELtask_75_06_08,,,,,,,0.9507021408374272,0.506187112645571,0.6606310953368241,29.0,23.0,8.0,,,,,,
 2_rmcv_rt10v_genELtask_2_00_01,,,,,,,0.999992351788694,0.442763965150917,0.6137704248458831,26.0,32.0,2.0,,,,,,
 2_enself_rt10v_genELtask_40_03_06,,,,,,,0.999957686283198,0.858354793344656,0.923761189329043,25.0,28.0,2.0,0.9999788426939772,0.858354793344656,0.92377021674032,40.0,28.0,9.0
 2_ense_enself_genELtask_25_02_02,,,,,,,0.999988889012344,1.0,0.999994444475308,14.0,15.0,0.0,0.999994444475308,1.0,0.999997222229938,18.0,16.0,0.0
diff --git a/data/GenED_feat.csv b/data/GenED_feat.csv
index b0ee1be42235cbc93e0ea3118fa8829007c1bed4..4006801c6fb1d3e29b2c777952a571e18784cc7b 100644
--- a/data/GenED_feat.csv
+++ b/data/GenED_feat.csv
@@ -1,7 +1,7 @@
 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
 2_rmcv_rt10v_genELtask_40_03_06,0.475,0.3107142857142857,0.5714285714285714,0.711996755762796,0.40848153131541576,0.10988860893433217,0.1999007815532011
-2_enself_rutpt_genELtask_25_02_02,0.19246861924686193,0.25784518828451886,0.7975941422594143,0.8336522045635787,0.45176947602735823,0.2018481552079625,0.2842730838492838
-2_rt10v_rutpt_genELtask_39_03_05,0.5,0.3,0.3,0.3935954518140152,0.25153078703466797,0.06196334316806251,0.1255248346244991
+2_enself_rvpnot_genELtask_25_02_02,0.19246861924686193,0.25784518828451886,0.7975941422594143,0.8336522045635787,0.45176947602735823,0.2018481552079625,0.2842730838492838
+2_rt10v_rvpnot_genELtask_39_03_05,0.5,0.3,0.3,0.3935954518140152,0.25153078703466797,0.06196334316806251,0.1255248346244991
 2_ense_rt10v_genELtask_32_02_09,0.0698856416772554,0.25497670478610757,0.8881829733163914,0.6299754360953089,0.19861483706118124,0.024012358558338984,0.08429853275845982
 2_enseef_rt10v_genELtask_41_03_07,0.2568149210903874,0.22955523672883787,0.7675753228120517,0.7805342585026933,0.4975405783736804,0.2001282318680447,0.29658217151597943
 2_enseef_rt10v_genELtask_18_01_06,0.41916167664670656,0.27844311377245506,0.6017964071856288,0.6751278958355251,0.39431303770630194,0.09769467089474564,0.19006045768775687
@@ -14,39 +14,39 @@ log,ratio_variants_per_number_of_traces,ratio_most_common_variant,ratio_top_10_v
 2_enself_rmcv_genELtask_36_03_02,0.4684904738641915,0.19149975574010747,0.5779189057156815,0.8195089118758452,0.44834084201292557,0.22065338938139506,0.28436940807757993
 2_ense_enseef_genELtask_60_05_04,0.9354838709677419,0.03225806451612903,0.14516129032258066,0.6982889326094908,0.5701685883981173,0.3145318746248109,0.3851735560295954
 2_enseef_rmcv_genELtask_6_00_05,0.0013035195026571743,0.502155820715933,0.502155820715933,0.0,0.0,1.4216584524393057e-14,0.0
-2_enself_rutpt_genELtask_5_00_04,0.4001380579843534,0.252416014726185,0.6396686608375518,0.6300151559480306,0.5856320187437172,0.05130745557946631,0.23819537119548825
+2_enself_rvpnot_genELtask_5_00_04,0.4001380579843534,0.252416014726185,0.6396686608375518,0.6300151559480306,0.5856320187437172,0.05130745557946631,0.23819537119548825
 2_enve_rt10v_genELtask_69_06_02,0.8911736792154381,0.023726668775703893,0.19772223979753242,0.600886975095726,0.5714031821905712,0.0771509993358544,0.24401227040205467
-2_enve_rutpt_genELtask_53_04_08,0.7727272727272727,0.22727272727272727,0.22727272727272727,0.38313314696144884,0.399177345275478,0.03309833960457497,0.16984481053441977
+2_enve_rvpnot_genELtask_53_04_08,0.7727272727272727,0.22727272727272727,0.22727272727272727,0.38313314696144884,0.399177345275478,0.03309833960457497,0.16984481053441977
 2_enself_rt10v_genELtask_22_01_10,0.018628281117696866,0.49043183742591023,0.9463166807790009,0.6358930597723753,0.13958458687314385,0.019675948344646973,0.060719108487034525
 2_enseef_enve_genELtask_63_05_07,1.0,0.0017889087656529517,0.09838998211091235,0.7004427016846122,0.6374757842317572,0.33825287987768765,0.41777327329593317
-2_enseef_rutpt_genELtask_26_02_03,0.29977628635346754,0.1309967685806612,0.7017151379567487,0.7651685138918896,0.5126735885740216,0.067947545802514,0.21874266825922745
-2_enself_rutpt_genELtask_20_01_08,0.8582871226124461,0.0018484288354898336,0.22735674676524953,0.7605567533804329,0.5998465511102566,0.09994583757366739,0.26342315309294484
+2_enseef_rvpnot_genELtask_26_02_03,0.29977628635346754,0.1309967685806612,0.7017151379567487,0.7651685138918896,0.5126735885740216,0.067947545802514,0.21874266825922745
+2_enself_rvpnot_genELtask_20_01_08,0.8582871226124461,0.0018484288354898336,0.22735674676524953,0.7605567533804329,0.5998465511102566,0.09994583757366739,0.26342315309294484
 2_enseef_rt10v_genELtask_30_02_07,0.2954661232806928,0.12353540499235864,0.7004584819154356,0.7247059167044853,0.5232074242321306,0.03505620467719201,0.20813882288394578
 2_enseef_enself_genELtask_60_05_04,0.9898073590867393,0.0002038528182652125,0.1091631841810213,0.8468803977116387,0.6211058437925346,0.37126718728679997,0.4349760128354891
 2_enve_rmcv_genELtask_48_04_03,0.3673469387755102,0.2857142857142857,0.2857142857142857,0.43867736895861886,0.42418836216219147,0.055786198025420126,0.18923112889237953
-2_enve_rutpt_genELtask_74_06_07,0.6997167138810199,0.11614730878186968,0.36827195467422097,0.5607960859608858,0.49166579537334987,0.0859779052659694,0.22026729064130462
+2_enve_rvpnot_genELtask_74_06_07,0.6997167138810199,0.11614730878186968,0.36827195467422097,0.5607960859608858,0.49166579537334987,0.0859779052659694,0.22026729064130462
 2_ense_rt10v_genELtask_45_04_00,0.9,0.2,0.0,0.5263299977307545,0.4431451750857598,0.13067961924455518,0.22971667122961203
-2_rmcv_rutpt_genELtask_56_05_00,0.0014187493179089819,0.5025646622285278,0.5025646622285278,0.0,0.0,1.4193032875648049e-14,0.0
+2_rmcv_rvpnot_genELtask_56_05_00,0.0014187493179089819,0.5025646622285278,0.5025646622285278,0.0,0.0,1.4193032875648049e-14,0.0
 2_enself_rt10v_genELtask_20_01_08,0.13438493519197847,0.10185864514551235,0.7998288089997554,0.7713587127087669,0.336225957294235,0.03783377185995952,0.13994836862171564
 2_ense_enself_genELtask_82_07_04,1.0,0.00010039152695512498,0.0999899608473045,0.7917266925266735,0.6492619817119419,0.37017385562318916,0.4410076123968031
 2_ense_enself_genELtask_70_06_03,0.9908226810881678,0.0006555227794165847,0.10816125860373647,0.8129369266683333,0.5918451306601266,0.3034439123784541,0.37661273659460537
 2_ense_enseef_genELtask_24_02_01,0.36585365853658536,0.1951219512195122,0.1951219512195122,0.376878125753153,0.24682117080495672,0.06443090251117767,0.12514804585839348
 2_enself_enve_genELtask_17_01_05,1.0,0.010309278350515464,0.09278350515463918,0.5033964980237928,0.4957612608790988,0.08252950868893243,0.22031339421894855
 2_enve_rmcv_genELtask_69_06_02,0.6320474777448071,0.21364985163204747,0.43026706231454004,0.5081424306564961,0.5059589286339654,0.07774795809955806,0.22222164971219155
-2_ense_rutpt_genELtask_72_06_05,0.5092186128182616,0.2517998244073749,0.5417032484635645,0.6135799384062383,0.6011834418147624,0.06928679277709242,0.2513141985126647
+2_ense_rvpnot_genELtask_72_06_05,0.5092186128182616,0.2517998244073749,0.5417032484635645,0.6135799384062383,0.6011834418147624,0.06928679277709242,0.2513141985126647
 2_ense_enve_genELtask_65_05_09,0.5332896747435057,0.0017463435931019428,0.21392709015498798,0.87556088130452,0.5334128948702155,0.32992625975985646,0.3828485709416318
 2_ense_enseef_genELtask_82_07_04,1.0,0.0037593984962406013,0.09774436090225563,0.6733280614279173,0.612850889895113,0.3243584061659897,0.40137877505390723
 2_rmcv_rt10v_genELtask_14_01_02,0.8890680425669139,0.028700419219606577,0.19982801246909598,0.6294417690195417,0.6013121836245198,0.06607548015704681,0.24928773228968032
 2_ense_rt10v_genELtask_53_04_08,0.15173596228032576,0.12366052293184741,0.7996142306043721,0.7636003447347126,0.4012151597937047,0.06404217618963838,0.1767276816418785
-2_enseef_rutpt_genELtask_25_02_02,0.1993997999333111,0.12870956985661888,0.7917639213071024,0.6831475616036886,0.4750582811403656,0.03672681784281664,0.19206206477852675
+2_enseef_rvpnot_genELtask_25_02_02,0.1993997999333111,0.12870956985661888,0.7917639213071024,0.6831475616036886,0.4750582811403656,0.03672681784281664,0.19206206477852675
 2_enseef_rt10v_genELtask_57_05_01,1.0,0.0001968503937007874,0.1,0.7831962227552328,0.6420396755456009,0.3651610017066387,0.43591664699098415
-2_rt10v_rutpt_genELtask_82_07_04,0.31950343025155176,0.07296090602199717,0.698573450941958,0.6786457966658624,0.5693710172340232,0.0570563755696918,0.2353767585504781
-2_rt10v_rutpt_genELtask_27_02_04,0.3541666666666667,0.22916666666666666,0.22916666666666666,0.2860625654508283,0.2666395034515648,0.028371359936932416,0.11749817137355634
+2_rt10v_rvpnot_genELtask_82_07_04,0.31950343025155176,0.07296090602199717,0.698573450941958,0.6786457966658624,0.5693710172340232,0.0570563755696918,0.2353767585504781
+2_rt10v_rvpnot_genELtask_27_02_04,0.3541666666666667,0.22916666666666666,0.22916666666666666,0.2860625654508283,0.2666395034515648,0.028371359936932416,0.11749817137355634
 2_enve_rt10v_genELtask_47_04_02,0.8,0.26666666666666666,0.26666666666666666,0.39570496026792323,0.37436755055805526,0.07967828552715818,0.17882289515676592
-2_enself_rutpt_genELtask_22_01_10,1.0,0.05,0.1,0.4297029597859261,0.4229766956299156,0.09451693007176624,0.20128570432796253
+2_enself_rvpnot_genELtask_22_01_10,1.0,0.05,0.1,0.4297029597859261,0.4229766956299156,0.09451693007176624,0.20128570432796253
 2_ense_enve_genELtask_2_00_01,0.0012998700129987,0.4978502149785021,0.4978502149785021,0.10218453802326659,0.06723120807207657,0.004115030241651627,0.026616483436976368
-2_enseef_rutpt_genELtask_14_01_02,0.1993997999333111,0.12870956985661888,0.7917639213071024,0.6831475616036886,0.4750582811403656,0.03672681784281664,0.19206206477852675
-2_enve_rutpt_genELtask_64_05_08,0.7894736842105263,0.1368421052631579,0.28421052631578947,0.46820410841020726,0.4678611739230912,0.08543888914611492,0.21324544589412509
+2_enseef_rvpnot_genELtask_14_01_02,0.1993997999333111,0.12870956985661888,0.7917639213071024,0.6831475616036886,0.4750582811403656,0.03672681784281664,0.19206206477852675
+2_enve_rvpnot_genELtask_64_05_08,0.7894736842105263,0.1368421052631579,0.28421052631578947,0.46820410841020726,0.4678611739230912,0.08543888914611492,0.21324544589412509
 2_enve_rt10v_genELtask_77_06_10,0.03557013118062563,0.25264883955600403,0.9490413723511605,0.6435188887425013,0.2582766324911369,0.020655917837850462,0.10533663929442574
 2_enve_rmcv_genELtask_49_04_04,0.15853658536585366,0.45121951219512196,0.45121951219512196,0.3958978820404354,0.16108509142129726,0.030748491415396692,0.07599910255471375
 2_enseef_enve_genELtask_3_00_02,0.0013961980453227366,0.49779830308237566,0.49779830308237566,0.11914304012564805,0.06575409360796385,0.0051097663052729085,0.026327280319246155
@@ -54,55 +54,55 @@ log,ratio_variants_per_number_of_traces,ratio_most_common_variant,ratio_top_10_v
 2_enve_rmcv_genELtask_6_00_05,0.0013044350792695164,0.5020068231988761,0.5020068231988761,0.0,0.0,4.086366171900412e-15,-8.018529846747978e-14
 2_enve_rt10v_genELtask_96_08_07,0.17118214104515475,0.09710806697108067,0.7836631151699645,0.8019514242735976,0.3427279684516806,0.06687058824232488,0.15640242664509152
 2_ense_enseef_genELtask_58_05_02,0.40627943485086343,0.2552590266875981,0.6342229199372057,0.7237630019929844,0.4694623510927801,0.06066621034601321,0.19888589789103306
-2_enself_rutpt_genELtask_19_01_07,0.7004470938897168,0.041728763040238454,0.353204172876304,0.6601805056902312,0.532560876807581,0.07368790008652162,0.22931544625011355
+2_enself_rvpnot_genELtask_19_01_07,0.7004470938897168,0.041728763040238454,0.353204172876304,0.6601805056902312,0.532560876807581,0.07368790008652162,0.22931544625011355
 2_enseef_enself_genELtask_2_00_01,0.0025864095932283094,0.5043498706795203,0.5043498706795203,0.0,0.0,7.473261707331885e-15,0.0
 2_enself_rt10v_genELtask_31_02_08,0.2618631963180448,0.044119980955403905,0.7643231233137597,0.8310469813378213,0.5355536031457515,0.20001080462410423,0.3015689300726041
 2_ense_enve_genELtask_75_06_08,0.764126149802891,0.005256241787122208,0.3120893561103811,0.7518665967768557,0.5972165683871318,0.07214013670664875,0.24994366904884743
 2_enve_rmcv_genELtask_47_04_02,0.2711864406779661,0.2033898305084746,0.2033898305084746,0.37499469519394973,0.23102755588157572,0.0566838513279209,0.11488704876988175
 2_enseef_rt10v_genELtask_42_03_08,0.2149191444966093,0.23526343244653103,0.7991653625456442,0.8337887512442002,0.4584345334231188,0.20558932027436128,0.2887032423616693
 2_enseef_rmcv_genELtask_56_05_00,1.0,0.00011618450098756825,0.09991867084930871,0.8011594979231932,0.6458961115179734,0.3704346521071957,0.4406806227735229
-2_enve_rutpt_genELtask_79_07_01,0.10139125212691422,0.10229206285657091,0.8227404664197778,0.7760273348455629,0.33771324075899195,0.058728255324134636,0.15033469561007223
+2_enve_rvpnot_genELtask_79_07_01,0.10139125212691422,0.10229206285657091,0.8227404664197778,0.7760273348455629,0.33771324075899195,0.058728255324134636,0.15033469561007223
 2_enseef_enve_genELtask_28_02_05,0.5930232558139535,0.23920265780730898,0.46511627906976744,0.49967732360423056,0.49979502661211317,0.06866910956634038,0.2147224463416379
 2_ense_enself_genELtask_2_00_01,0.0025864095932283094,0.5043498706795203,0.5043498706795203,0.0,0.0,7.473261707331885e-15,0.0
 2_ense_enself_genELtask_46_04_01,0.5862068965517241,0.27586206896551724,0.27586206896551724,0.5401297372660087,0.42032949062118424,0.11755401325199104,0.21303304868396442
 2_ense_rmcv_genELtask_59_05_03,0.3004807692307692,0.26802884615384615,0.71875,0.7035947903268801,0.5036052257262708,0.0944496693775114,0.23010773615681843
-2_enseef_rutpt_genELtask_38_03_04,0.4012918853451756,0.1303996770286637,0.6346386758175212,0.6809806764011145,0.5914727173523133,0.052999798179001384,0.24128273867443722
+2_enseef_rvpnot_genELtask_38_03_04,0.4012918853451756,0.1303996770286637,0.6346386758175212,0.6809806764011145,0.5914727173523133,0.052999798179001384,0.24128273867443722
 2_ense_enseef_genELtask_71_06_04,1.0,0.0038461538461538464,0.1,0.684890435710049,0.6018036519858658,0.32695459435000407,0.399647960064609
-2_enseef_rutpt_genELtask_16_01_04,0.43333333333333335,0.23333333333333334,0.23333333333333334,0.3248661175407305,0.2456784735152868,0.050844590722813096,0.11695904680005453
+2_enseef_rvpnot_genELtask_16_01_04,0.43333333333333335,0.23333333333333334,0.23333333333333334,0.3248661175407305,0.2456784735152868,0.050844590722813096,0.11695904680005453
 2_enve_rt10v_genELtask_17_01_05,0.0013908205841446453,0.4982347277201241,0.4982347277201241,0.10218453802326659,0.06770341961468727,0.004120226890669181,0.026782031043360924
 2_enve_rmcv_genELtask_68_06_01,0.5433911882510013,0.1068090787716956,0.5013351134846462,0.6936744365145042,0.5258616232094979,0.07097841656529008,0.22531839241753018
 2_enseef_rmcv_genELtask_15_01_03,0.41916167664670656,0.27844311377245506,0.6017964071856288,0.6751278958355251,0.39431303770630194,0.09769467089474564,0.19006045768775687
-2_enseef_rutpt_genELtask_40_03_06,0.5818632855567806,0.12624035281146637,0.4762954796030871,0.6374668756680482,0.6141990539886371,0.07978058218249626,0.2612481745426661
+2_enseef_rvpnot_genELtask_40_03_06,0.5818632855567806,0.12624035281146637,0.4762954796030871,0.6374668756680482,0.6141990539886371,0.07978058218249626,0.2612481745426661
 2_ense_rt10v_genELtask_71_06_04,0.764126149802891,0.005256241787122208,0.3120893561103811,0.7518665967768557,0.5972165683871318,0.07214013670664875,0.24994366904884743
-2_rt10v_rutpt_genELtask_51_04_06,0.6661613098847786,0.0661006670709521,0.40024257125530627,0.672249151728684,0.5796518064398385,0.06815187140942756,0.24348854205954132
+2_rt10v_rvpnot_genELtask_51_04_06,0.6661613098847786,0.0661006670709521,0.40024257125530627,0.672249151728684,0.5796518064398385,0.06815187140942756,0.24348854205954132
 2_enseef_rmcv_genELtask_7_00_06,0.0026227944682880307,0.5042918454935622,0.5042918454935622,0.0,0.0,1.0514584380307877e-14,0.0
 2_ense_enve_genELtask_27_02_04,0.26666666666666666,0.2,0.2,0.37499469519394973,0.2293601512318155,0.05603945365716461,0.11389717148419096
 2_enseef_rmcv_genELtask_26_02_03,0.3758604864616797,0.26571821936668194,0.661312528682882,0.6595192207269038,0.4691827633849348,0.06247106310143114,0.2005498383105583
 2_enseef_enve_genELtask_42_03_08,0.1056701030927835,0.24398625429553264,0.8900343642611683,0.800227299779838,0.4065432083141597,0.09264169499933393,0.20051473551728352
 2_enseef_rt10v_genELtask_22_01_10,0.03390531321066777,0.4180277031217697,0.9572048790572669,0.6138596409609469,0.1505233822845398,0.01807700732953072,0.06439276845416624
-2_enve_rutpt_genELtask_3_00_02,0.26666666666666666,0.4,0.0,0.0,0.0,5.684425045951545e-17,0.0
-2_ense_rutpt_genELtask_48_04_03,0.3076923076923077,0.09954751131221719,0.6432880844645551,0.7646293297633086,0.3861211932986765,0.09228802028769713,0.18540004058211074
+2_enve_rvpnot_genELtask_3_00_02,0.26666666666666666,0.4,0.0,0.0,0.0,5.684425045951545e-17,0.0
+2_ense_rvpnot_genELtask_48_04_03,0.3076923076923077,0.09954751131221719,0.6432880844645551,0.7646293297633086,0.3861211932986765,0.09228802028769713,0.18540004058211074
 2_enseef_rmcv_genELtask_24_02_01,0.35120328702797887,0.10154568577577773,0.6734494228135395,0.707484501419513,0.5019864539665425,0.06427676475770731,0.21339729709234978
 2_rmcv_rt10v_genELtask_43_03_09,0.09083880943177426,0.2531890220332431,0.9002705836876691,0.6253244549188018,0.24849191434312637,0.03528286726200614,0.10844659629540727
 2_ense_enve_genELtask_17_01_05,0.001366742596810934,0.4929384965831435,0.0,0.4999999999999999,0.09966076422987864,0.022781206035983215,0.04941995479496276
 2_enve_rmcv_genELtask_57_05_01,0.6016949152542372,0.1016949152542373,0.3559322033898305,0.4773732172087881,0.4755714450649136,0.044488435178440894,0.19953079295431608
 2_ense_enve_genELtask_41_03_07,0.05812069663831511,0.2559740785743216,0.8594572701498583,0.6992457754260718,0.29948375086451196,0.04831149694096134,0.13282399329286793
 2_ense_enve_genELtask_31_02_08,0.006763787721123829,0.1984911550468262,0.3064516129032258,0.7896095906624292,0.23163903091122362,0.1021862095415895,0.14068831931429496
-2_enve_rutpt_genELtask_88_07_10,0.9993016759776536,0.0013966480446927375,0.1005586592178771,0.6998483496820879,0.626682112040831,0.13285374601427621,0.2900732460556941
+2_enve_rvpnot_genELtask_88_07_10,0.9993016759776536,0.0013966480446927375,0.1005586592178771,0.6998483496820879,0.626682112040831,0.13285374601427621,0.2900732460556941
 2_enseef_rt10v_genELtask_19_01_07,0.03671103477887505,0.12773722627737227,0.7219836839845427,0.6013118785150664,0.2572525524334572,0.03432038332822809,0.11050812542172642
-2_rmcv_rutpt_genELtask_27_02_04,0.4010416666666667,0.20833333333333334,0.5963541666666666,0.6240183294948839,0.4917894997933917,0.09333615396880411,0.22633652278360303
-2_rmcv_rutpt_genELtask_38_03_04,0.3918918918918919,0.2702702702702703,0.6351351351351351,0.7036609540346844,0.3886100811104147,0.08605863905608521,0.18169519174394302
+2_rmcv_rvpnot_genELtask_27_02_04,0.4010416666666667,0.20833333333333334,0.5963541666666666,0.6240183294948839,0.4917894997933917,0.09333615396880411,0.22633652278360303
+2_rmcv_rvpnot_genELtask_38_03_04,0.3918918918918919,0.2702702702702703,0.6351351351351351,0.7036609540346844,0.3886100811104147,0.08605863905608521,0.18169519174394302
 2_ense_enself_genELtask_58_05_02,0.24125596184419715,0.2503974562798092,0.7825914149443561,0.8140568816041208,0.48402758993478934,0.20004033795585513,0.28969306486731833
 2_ense_enve_genELtask_74_06_07,0.764126149802891,0.005256241787122208,0.3120893561103811,0.7518665967768557,0.5972165683871318,0.07214013670664875,0.24994366904884743
 2_enself_enve_genELtask_19_01_07,0.5003940110323088,0.25847123719464143,0.5492513790386131,0.7240379302712872,0.5020529841261238,0.10054213453877364,0.22911978940908356
-2_ense_rutpt_genELtask_49_04_04,0.39861319340329837,0.1176911544227886,0.613568215892054,0.8636846626659379,0.3984131902789566,0.1995647018973926,0.2550563902315614
+2_ense_rvpnot_genELtask_49_04_04,0.39861319340329837,0.1176911544227886,0.613568215892054,0.8636846626659379,0.3984131902789566,0.1995647018973926,0.2550563902315614
 2_ense_rt10v_genELtask_55_04_10,0.04516129032258064,0.2506610259122158,0.9510312004230566,0.6398117865762518,0.31454087496661237,0.021158312910597415,0.12590558836330898
 2_enve_rmcv_genELtask_35_03_01,1.0,0.08333333333333333,0.08333333333333333,0.27035550155334326,0.27393512459289676,0.047651388767280284,0.12602619954419542
-2_enve_rutpt_genELtask_77_06_10,1.0,0.012195121951219513,0.0975609756097561,0.581945915609206,0.5444523764734037,0.10839193988503372,0.24967967646048206
+2_enve_rvpnot_genELtask_77_06_10,1.0,0.012195121951219513,0.0975609756097561,0.581945915609206,0.5444523764734037,0.10839193988503372,0.24967967646048206
 2_ense_rt10v_genELtask_49_04_04,0.53125,0.28125,0.46875,0.5967379634682549,0.3945610173557275,0.11969230594263638,0.2030863409303437
 2_ense_enve_genELtask_37_03_03,0.6428571428571429,0.21428571428571427,0.0,0.3164579484595264,0.2475825943071805,0.05307444232569762,0.11832022244192991
-2_enve_rutpt_genELtask_92_08_03,0.37270685136652937,0.1224260576563085,0.6632347435417447,0.7997976319077871,0.5536318510176064,0.09152245928259789,0.24446211340262772
-2_rt10v_rutpt_genELtask_72_06_05,0.4438559322033898,0.13029661016949154,0.6003707627118644,0.5938720427807744,0.5744784937521648,0.04902243185074568,0.23289649918236238
+2_enve_rvpnot_genELtask_92_08_03,0.37270685136652937,0.1224260576563085,0.6632347435417447,0.7997976319077871,0.5536318510176064,0.09152245928259789,0.24446211340262772
+2_rt10v_rvpnot_genELtask_72_06_05,0.4438559322033898,0.13029661016949154,0.6003707627118644,0.5938720427807744,0.5744784937521648,0.04902243185074568,0.23289649918236238
 2_ense_rt10v_genELtask_22_01_10,0.014917003140421714,0.2559443696724989,0.9554733064154329,0.6536758930238873,0.11661612663170118,0.010563441398145895,0.04779892416297424
 2_enseef_rt10v_genELtask_12_01_00,0.000784313725490196,0.253781512605042,0.0,0.6618975093740622,0.14936448421068016,0.07430966210920538,0.09978328777078581
 2_ense_enve_genELtask_14_01_02,0.004719207173194903,0.2529495044832468,0.2529495044832468,0.19382635134715698,0.1011114478587675,0.010665613901824978,0.0417362696777674
@@ -110,128 +110,128 @@ log,ratio_variants_per_number_of_traces,ratio_most_common_variant,ratio_top_10_v
 2_enself_rt10v_genELtask_4_00_03,0.7884539343614076,0.006326611308817715,0.2902332937920127,0.7341066607379166,0.6017750001320097,0.08446319079738644,0.25754661997038764
 2_ense_rt10v_genELtask_60_05_04,0.664,0.12266666666666666,0.4,0.5698206283003576,0.5021685225706433,0.08367260772037152,0.22321342009484033
 2_enve_rt10v_genELtask_6_00_05,0.0031887755102040817,0.5,0.5,0.0,0.0,-2.5665658462840415e-14,1.4609171699417573e-14
-2_rt10v_rutpt_genELtask_1_00_00,0.00029997000299970003,0.4931506849315068,0.0,0.5544923070581674,0.09456185168571228,0.029304614551683532,0.05069504678945394
+2_rt10v_rvpnot_genELtask_1_00_00,0.00029997000299970003,0.4931506849315068,0.0,0.5544923070581674,0.09456185168571228,0.029304614551683532,0.05069504678945394
 2_ense_rmcv_genELtask_67_06_00,0.764126149802891,0.005256241787122208,0.3120893561103811,0.7518665967768557,0.5972165683871318,0.07214013670664875,0.24994366904884743
 2_enve_rmcv_genELtask_80_07_02,0.3557142857142857,0.22714285714285715,0.6442857142857142,0.6517416801285024,0.4796199079069343,0.06233112245126289,0.20501008678504531
 2_enself_rmcv_genELtask_2_00_01,0.39909125331313894,0.09977281332828473,0.6310109806891329,0.7120187353302314,0.5226998876355093,0.06170678741918126,0.21976565915814453
 2_ense_rt10v_genELtask_72_06_05,0.5480417018878557,0.1301775147928994,0.5066215835446605,0.6452335687481102,0.598400815245819,0.07552037007446673,0.2538299951908796
 2_enve_rt10v_genELtask_73_06_06,0.4114255765199161,0.1331236897274633,0.6247379454926625,0.6001116168111318,0.5704156104208186,0.052163009043055916,0.233555266902745
-2_rt10v_rutpt_genELtask_32_02_09,0.8999808024572855,0.03014014206181609,0.18986369744672682,0.6269959605711612,0.6022506562371214,0.060311163757714184,0.24701173052131387
+2_rt10v_rvpnot_genELtask_32_02_09,0.8999808024572855,0.03014014206181609,0.18986369744672682,0.6269959605711612,0.6022506562371214,0.060311163757714184,0.24701173052131387
 2_ense_rt10v_genELtask_62_05_06,0.4425016812373907,0.07330195023537324,0.574310692669805,0.7462555444978821,0.5023514335003845,0.07666726463628988,0.21878168183405255
 2_enseef_rt10v_genELtask_17_01_05,0.12169312169312169,0.2962962962962963,0.43915343915343913,0.4445041290314368,0.2339676378620105,0.0398952531246502,0.10572131862070662
 2_ense_rt10v_genELtask_70_06_03,0.776595744680851,0.005319148936170213,0.30086436170212766,0.7386782362176705,0.6009353373215089,0.08448309412754813,0.25721823974699504
 2_enself_rt10v_genELtask_18_01_06,0.41566265060240964,0.28012048192771083,0.5993975903614458,0.6741020926914424,0.3935727659732712,0.09765230959491027,0.1897902218100729
-2_ense_rutpt_genELtask_1_00_00,0.0005581914596706671,0.5115824727881664,0.0,0.2704260414863775,0.045783347189129193,0.030906961757072707,0.035593816715716466
-2_rt10v_rutpt_genELtask_71_06_04,0.40285714285714286,0.26285714285714284,0.5942857142857143,0.6649333349223701,0.4032203160568205,0.07883697195523483,0.18405113903503278
-2_rmcv_rutpt_genELtask_13_01_01,0.1301053409720939,0.10072075401958973,0.7980040657919054,0.7677135027092734,0.3531155032845471,0.0607443292462888,0.1568964178715283
-2_enve_rutpt_genELtask_61_05_05,0.5066225165562914,0.12748344370860928,0.5347682119205298,0.5115553681333121,0.509213285521824,0.057932013520071234,0.21488747834900815
-2_rt10v_rutpt_genELtask_22_01_10,1.0,0.00020920502092050208,0.1,0.7886926739533026,0.6385811873842905,0.36528205705057953,0.43512772171918007
+2_ense_rvpnot_genELtask_1_00_00,0.0005581914596706671,0.5115824727881664,0.0,0.2704260414863775,0.045783347189129193,0.030906961757072707,0.035593816715716466
+2_rt10v_rvpnot_genELtask_71_06_04,0.40285714285714286,0.26285714285714284,0.5942857142857143,0.6649333349223701,0.4032203160568205,0.07883697195523483,0.18405113903503278
+2_rmcv_rvpnot_genELtask_13_01_01,0.1301053409720939,0.10072075401958973,0.7980040657919054,0.7677135027092734,0.3531155032845471,0.0607443292462888,0.1568964178715283
+2_enve_rvpnot_genELtask_61_05_05,0.5066225165562914,0.12748344370860928,0.5347682119205298,0.5115553681333121,0.509213285521824,0.057932013520071234,0.21488747834900815
+2_rt10v_rvpnot_genELtask_22_01_10,1.0,0.00020920502092050208,0.1,0.7886926739533026,0.6385811873842905,0.36528205705057953,0.43512772171918007
 2_enseef_enself_genELtask_1_00_00,0.03184713375796178,0.4713375796178344,0.0,0.0,0.0,-4.848323085588059e-16,8.484565399779102e-16
 2_ense_enve_genELtask_76_06_09,0.9073179150845703,0.0006903693476009665,0.1832930617880566,0.8552538997544381,0.5967992092551478,0.36015116862467417,0.4209255155064313
 2_enseef_enve_genELtask_52_04_07,1.0,0.003236245954692557,0.0970873786407767,0.6999996275803991,0.6063169003597013,0.33119709777210654,0.40411185598402416
 2_rmcv_rt10v_genELtask_19_01_07,0.18763866877971475,0.10047543581616482,0.7328050713153724,0.7542151037297532,0.35817523399194906,0.06683690101218798,0.16236781360250563
 2_enve_rt10v_genELtask_108_09_08,0.05234681455430813,0.24699039632084405,0.7921006357365075,0.8760651951121073,0.37273195251887453,0.16625906482174058,0.23537973063703235
-2_enself_rutpt_genELtask_44_03_10,1.0,0.014925373134328358,0.08955223880597014,0.6285077825310188,0.5591085247847546,0.2999556841383065,0.36959470012904033
+2_enself_rvpnot_genELtask_44_03_10,1.0,0.014925373134328358,0.08955223880597014,0.6285077825310188,0.5591085247847546,0.2999556841383065,0.36959470012904033
 2_enself_rt10v_genELtask_40_03_06,0.48242746219861055,0.25623212096444625,0.5657948508377605,0.8267614316035612,0.5062199953216602,0.2945399239488939,0.3530271594826512
 2_ense_rt10v_genELtask_31_02_08,0.1218274111675127,0.272419627749577,0.8020304568527918,0.5914616275520985,0.2374323859961468,0.039146103847632084,0.1065343586366209
-2_rt10v_rutpt_genELtask_30_02_07,0.7210321324245375,0.0019474196689386564,0.19961051606621227,0.8118191444671337,0.5236727697789572,0.3243200576635398,0.37399934603882795
+2_rt10v_rvpnot_genELtask_30_02_07,0.7210321324245375,0.0019474196689386564,0.19961051606621227,0.8118191444671337,0.5236727697789572,0.3243200576635398,0.37399934603882795
 2_enself_enve_genELtask_7_00_06,0.4507829977628635,0.12416107382550336,0.5939597315436241,0.5999899347586614,0.573075255614529,0.05347239544305581,0.23567001603302215
-2_rmcv_rutpt_genELtask_20_01_08,0.8048780487804879,0.17073170731707318,0.2682926829268293,0.43906933056673675,0.41953841763429844,0.07486005422897682,0.19028264894692767
+2_rmcv_rvpnot_genELtask_20_01_08,0.8048780487804879,0.17073170731707318,0.2682926829268293,0.43906933056673675,0.41953841763429844,0.07486005422897682,0.19028264894692767
 2_enve_rt10v_genELtask_90_08_01,1.0,0.0001779042874933286,0.09998220957125066,0.8000055995932255,0.6337187256640586,0.3666673685013622,0.4346645195998424
 2_enself_enve_genELtask_2_00_01,0.0013018225515722011,0.4977969156819547,0.4977969156819547,0.10218453802326659,0.06724513353984364,0.004115451974413046,0.026621664098740113
 2_enve_rt10v_genELtask_57_05_01,0.9852941176470589,0.029411764705882353,0.10294117647058823,0.4973459910235571,0.4778307741441975,0.08754585270088139,0.2158501817665866
 2_ense_rmcv_genELtask_48_04_03,0.41916167664670656,0.27844311377245506,0.6017964071856288,0.6751278958355251,0.39431303770630194,0.09769467089474564,0.19006045768775687
-2_enve_rutpt_genELtask_62_05_06,0.6115702479338843,0.12396694214876033,0.4132231404958678,0.5091125314236503,0.5080478312633268,0.06059730087911666,0.21751686410890697
+2_enve_rvpnot_genELtask_62_05_06,0.6115702479338843,0.12396694214876033,0.4132231404958678,0.5091125314236503,0.5080478312633268,0.06059730087911666,0.21751686410890697
 2_ense_rt10v_genELtask_52_04_07,0.24813895781637718,0.09088089330024814,0.6997518610421837,0.7829467853326988,0.37478769547050117,0.0797522974301869,0.17460809113961842
 2_ense_rt10v_genELtask_69_06_02,0.8793076067968874,0.029855486739717326,0.20851198983643005,0.6299666358917276,0.599643694181909,0.06034570682177423,0.24611464641134817
 2_enseef_enve_genELtask_43_03_09,0.3925910807225227,0.2504337177262986,0.6112868660067354,0.8592911573380767,0.4322475929103519,0.22841787565097968,0.2839181901131401
 2_enself_rt10v_genELtask_36_03_02,0.8493771234428086,0.0028312570781426952,0.20271800679501698,0.8371070917763516,0.537612850564994,0.3189793383301473,0.3740956891263012
-2_ense_rutpt_genELtask_13_01_01,0.0427497969130788,0.2550771730300569,0.9414094232331438,0.6827250036843905,0.17560870313616564,0.021437348376568417,0.07444504485425857
+2_ense_rvpnot_genELtask_13_01_01,0.0427497969130788,0.2550771730300569,0.9414094232331438,0.6827250036843905,0.17560870313616564,0.021437348376568417,0.07444504485425857
 2_rmcv_rt10v_genELtask_17_01_05,0.5507343124165555,0.10013351134846461,0.5026702269692924,0.6573094011272658,0.5384051812012618,0.07716780821313449,0.2335418738029831
 2_ense_rmcv_genELtask_25_02_02,0.2702702702702703,0.20270270270270271,0.3783783783783784,0.37623732333465304,0.2532195004505502,0.03311370214169924,0.11255619534042918
 2_enseef_rmcv_genELtask_13_01_01,0.11992731677771049,0.10175651120533011,0.8049666868564507,0.7706359247408532,0.34838980765400906,0.06018921240764036,0.1549073444916672
-2_rt10v_rutpt_genELtask_21_01_09,0.9033412887828163,0.011137629276054098,0.1869530628480509,0.6829961418530797,0.6176705675950748,0.0826622115745773,0.26318314897521317
-2_ense_rutpt_genELtask_46_04_01,0.11380737396538751,0.12547027840481564,0.8053047404063205,0.750945686855021,0.37445532889651906,0.05687223056238955,0.16430332399453865
+2_rt10v_rvpnot_genELtask_21_01_09,0.9033412887828163,0.011137629276054098,0.1869530628480509,0.6829961418530797,0.6176705675950748,0.0826622115745773,0.26318314897521317
+2_ense_rvpnot_genELtask_46_04_01,0.11380737396538751,0.12547027840481564,0.8053047404063205,0.750945686855021,0.37445532889651906,0.05687223056238955,0.16430332399453865
 2_ense_enseef_genELtask_59_05_03,0.259336387616271,0.12342079689018465,0.7664861863112592,0.8179223939823769,0.5377571171509679,0.19308851786689255,0.3000249372064951
 2_enve_rt10v_genELtask_74_06_07,0.3142559833506764,0.14151925078043703,0.6701352757544224,0.5969546313636848,0.49574182836884817,0.062486657480195856,0.21455335300554804
-2_enseef_rutpt_genELtask_54_04_09,0.900749063670412,0.0056179775280898875,0.1891385767790262,0.8122742339443141,0.5369497542384427,0.3147180250167532,0.37191915698047734
+2_enseef_rvpnot_genELtask_54_04_09,0.900749063670412,0.0056179775280898875,0.1891385767790262,0.8122742339443141,0.5369497542384427,0.3147180250167532,0.37191915698047734
 2_enve_rt10v_genELtask_76_06_09,0.11621251710837377,0.2573099415204678,0.8934925967400772,0.6002807675406352,0.49496464554780745,0.03613520487501194,0.19867649367463394
-2_enve_rutpt_genELtask_68_06_01,0.061658706035605734,0.14025184541901867,0.6851932262266609,0.6023923865883765,0.2770496979604465,0.037213677672216686,0.11920557566272547
-2_ense_rutpt_genELtask_73_06_06,0.5217555807794173,0.024214907302307985,0.5119182746878547,0.6737578531109052,0.5992329328317865,0.047910466405467915,0.2420428230959399
+2_enve_rvpnot_genELtask_68_06_01,0.061658706035605734,0.14025184541901867,0.6851932262266609,0.6023923865883765,0.2770496979604465,0.037213677672216686,0.11920557566272547
+2_ense_rvpnot_genELtask_73_06_06,0.5217555807794173,0.024214907302307985,0.5119182746878547,0.6737578531109052,0.5992329328317865,0.047910466405467915,0.2420428230959399
 2_rmcv_rt10v_genELtask_48_04_03,0.1956521739130435,0.32608695652173914,0.32608695652173914,0.4873370075035338,0.23320848584355752,0.04123271992420233,0.10645193913384911
 2_enseef_enself_genELtask_37_03_03,0.06011808910359635,0.006083378064054392,0.1587045983181249,0.8308234851968698,0.4339136417321697,0.2524422532212511,0.30279032438555026
-2_ense_rutpt_genELtask_75_06_08,0.7943495400788436,0.003942181340341655,0.2848226018396846,0.7493463029164047,0.5977215760776127,0.07278635139741209,0.25023933659329134
-2_enve_rutpt_genELtask_83_07_05,0.48951911220715166,0.07336621454993834,0.5413070283600493,0.7024083466841994,0.5087944795328581,0.06494299743605994,0.21598512046753815
+2_ense_rvpnot_genELtask_75_06_08,0.7943495400788436,0.003942181340341655,0.2848226018396846,0.7493463029164047,0.5977215760776127,0.07278635139741209,0.25023933659329134
+2_enve_rvpnot_genELtask_83_07_05,0.48951911220715166,0.07336621454993834,0.5413070283600493,0.7024083466841994,0.5087944795328581,0.06494299743605994,0.21598512046753815
 2_ense_rt10v_genELtask_64_05_08,0.1907945205479452,0.13205479452054794,0.7995616438356165,0.672187346968151,0.4728127070008119,0.02818922150124703,0.18660288503064887
 2_ense_rmcv_genELtask_16_01_04,0.022182254196642687,0.4088729016786571,0.7967625899280576,0.5661326285700348,0.10199820857680876,0.008419270609050752,0.041600094114697034
-2_enself_rutpt_genELtask_13_01_01,0.07066723513110211,0.24355148156043488,0.8579194201662759,0.8630532265787424,0.24846743956375728,0.09819484063759695,0.14387780877034134
-2_enself_rutpt_genELtask_3_00_02,0.1993997999333111,0.12870956985661888,0.7917639213071024,0.6831475616036886,0.4750582811403656,0.03672681784281664,0.19206206477852675
+2_enself_rvpnot_genELtask_13_01_01,0.07066723513110211,0.24355148156043488,0.8579194201662759,0.8630532265787424,0.24846743956375728,0.09819484063759695,0.14387780877034134
+2_enself_rvpnot_genELtask_3_00_02,0.1993997999333111,0.12870956985661888,0.7917639213071024,0.6831475616036886,0.4750582811403656,0.03672681784281664,0.19206206477852675
 2_enself_rmcv_genELtask_15_01_03,0.5339805825242718,0.3106796116504854,0.5048543689320388,0.6286153980442563,0.4946179192858565,0.0959950733781131,0.22680459843234826
-2_ense_rutpt_genELtask_74_06_07,0.764126149802891,0.005256241787122208,0.3120893561103811,0.7518665967768557,0.5972165683871318,0.07214013670664875,0.24994366904884743
-2_enve_rutpt_genELtask_93_08_04,0.3184086696135748,0.24982175958933409,0.6961357478967631,0.7965496745306102,0.38054868290554267,0.08325703121243298,0.1770244233388885
+2_ense_rvpnot_genELtask_74_06_07,0.764126149802891,0.005256241787122208,0.3120893561103811,0.7518665967768557,0.5972165683871318,0.07214013670664875,0.24994366904884743
+2_enve_rvpnot_genELtask_93_08_04,0.3184086696135748,0.24982175958933409,0.6961357478967631,0.7965496745306102,0.38054868290554267,0.08325703121243298,0.1770244233388885
 2_ense_rt10v_genELtask_42_03_08,0.1267361111111111,0.10173611111111111,0.8003472222222222,0.768477628302684,0.3516939337955358,0.06054923889328806,0.15627418897157339
 2_enseef_enve_genELtask_64_05_08,1.0,0.0001701258931609391,0.09986389928547125,0.799827095766494,0.6404147804497959,0.36850242876066913,0.43791006897138257
 2_ense_enve_genELtask_63_05_07,0.3769491525423729,0.1776271186440678,0.6311864406779661,0.6993892960804853,0.4563478890314499,0.06358426650389398,0.19639599353080342
 2_ense_rt10v_genELtask_47_04_02,0.7941176470588235,0.14705882352941177,0.23529411764705882,0.3452275114874784,0.3679381196745136,0.07006667559483372,0.17333067601872648
-2_enve_rutpt_genELtask_102_09_02,0.21031587365053978,0.2375049980007997,0.7944822071171531,0.8436095804469513,0.45431864527440585,0.207520432496227,0.28822392427664484
+2_enve_rvpnot_genELtask_102_09_02,0.21031587365053978,0.2375049980007997,0.7944822071171531,0.8436095804469513,0.45431864527440585,0.207520432496227,0.28822392427664484
 2_enself_rt10v_genELtask_46_04_01,1.0,0.00017667844522968197,0.1,0.7849872434442824,0.6451207913753952,0.36654951557798904,0.437739389732381
-2_enseef_rutpt_genELtask_55_04_10,1.0,0.00015264845061822624,0.09998473515493818,0.7996772405357235,0.6244970162558853,0.32009269501835763,0.39706145798613607
+2_enseef_rvpnot_genELtask_55_04_10,1.0,0.00015264845061822624,0.09998473515493818,0.7996772405357235,0.6244970162558853,0.32009269501835763,0.39706145798613607
 2_enve_rmcv_genELtask_70_06_03,0.41916167664670656,0.27844311377245506,0.6017964071856288,0.6751278958355251,0.39431303770630194,0.09769467089474564,0.19006045768775687
 2_enself_rmcv_genELtask_7_00_06,0.011553273427471117,0.5083440308087291,0.0,0.15588348026008178,0.08939766874463459,0.009126727232035881,0.0371539492547458
 2_enself_enve_genELtask_54_04_09,0.7662223340040242,0.0008802816901408451,0.19806338028169015,0.8789815716041716,0.5647735760888405,0.3463182970363317,0.40304616667774706
-2_enve_rutpt_genELtask_65_05_09,0.96,0.04,0.12,0.5147006595099423,0.4897124832059143,0.07557209277262326,0.21581058120648489
+2_enve_rvpnot_genELtask_65_05_09,0.96,0.04,0.12,0.5147006595099423,0.4897124832059143,0.07557209277262326,0.21581058120648489
 2_enseef_rt10v_genELtask_27_02_04,0.6551724137931034,0.19540229885057472,0.40229885057471265,0.5124041311444862,0.4450782150888588,0.0722889404498124,0.19836643170998772
 2_enself_rt10v_genELtask_21_01_09,0.06721915285451197,0.2605893186003683,0.8982504604051565,0.6106862743067145,0.18494001893952683,0.021402207275877886,0.07797520383296791
-2_ense_rutpt_genELtask_33_02_10,1.0,0.09090909090909091,0.09090909090909091,0.2596796388704891,0.26563917270831705,0.06138211074026601,0.1308565847381577
+2_ense_rvpnot_genELtask_33_02_10,1.0,0.09090909090909091,0.09090909090909091,0.2596796388704891,0.26563917270831705,0.06138211074026601,0.1308565847381577
 2_ense_rt10v_genELtask_68_06_01,1.0,0.0026455026455026454,0.09788359788359788,0.6763488461924798,0.6036041830290433,0.12942797270250642,0.2800378176902421
 2_enself_rt10v_genELtask_14_01_02,0.97,0.01,0.12666666666666668,0.5851365409913568,0.5520314172477577,0.1062956438151467,0.2519229712120042
-2_enseef_rutpt_genELtask_12_01_00,0.0004116920543433512,0.34026348291477976,0.0,0.5544923070581674,0.09743179221742981,0.028389972055774374,0.05056310594901508
+2_enseef_rvpnot_genELtask_12_01_00,0.0004116920543433512,0.34026348291477976,0.0,0.5544923070581674,0.09743179221742981,0.028389972055774374,0.05056310594901508
 2_ense_rmcv_genELtask_6_00_05,0.0014562563011089951,0.5033045816063627,0.5033045816063627,0.0,0.0,-1.3678544002165065e-14,-1.6165552002558713e-14
-2_ense_rutpt_genELtask_12_01_00,0.0005297171310520183,0.4447505032312745,0.0,0.6352130207431887,0.11201389909978393,0.03228567208363393,0.057830402547313954
+2_ense_rvpnot_genELtask_12_01_00,0.0005297171310520183,0.4447505032312745,0.0,0.6352130207431887,0.11201389909978393,0.03228567208363393,0.057830402547313954
 2_enve_rmcv_genELtask_7_00_06,0.002591283863368669,0.504829210836278,0.504829210836278,0.0,0.0,8.039697800600999e-15,0.0
 2_ense_rt10v_genELtask_63_05_07,0.29117876658860264,0.12412177985948478,0.6978922716627635,0.6185605423502091,0.5102401413861473,0.04941077155382689,0.21155986676551425
-2_enseef_rutpt_genELtask_53_04_08,0.8016194331983806,0.0016869095816464238,0.196693657219973,0.8539926880814102,0.5777145551275902,0.3328462089586026,0.39593339624659785
-2_ense_rutpt_genELtask_65_05_09,0.9010989010989011,0.03296703296703297,0.18681318681318682,0.5271839828952926,0.5045564181545842,0.0946501187734037,0.2321996779805319
+2_enseef_rvpnot_genELtask_53_04_08,0.8016194331983806,0.0016869095816464238,0.196693657219973,0.8539926880814102,0.5777145551275902,0.3328462089586026,0.39593339624659785
+2_ense_rvpnot_genELtask_65_05_09,0.9010989010989011,0.03296703296703297,0.18681318681318682,0.5271839828952926,0.5045564181545842,0.0946501187734037,0.2321996779805319
 2_enself_rmcv_genELtask_6_00_05,0.0013011710539485538,0.5020518466619958,0.5020518466619958,0.0,0.0,1.4212675557071876e-14,0.0
-2_ense_rutpt_genELtask_58_05_02,0.20221169036334913,0.12859399684044234,0.7965244865718799,0.6672249851308998,0.5007271458239038,0.03570578773101733,0.20055238352871674
+2_ense_rvpnot_genELtask_58_05_02,0.20221169036334913,0.12859399684044234,0.7965244865718799,0.6672249851308998,0.5007271458239038,0.03570578773101733,0.20055238352871674
 2_ense_enve_genELtask_50_04_05,0.6585365853658537,0.2926829268292683,0.34146341463414637,0.47183700866721895,0.4115371132889812,0.07004068926627709,0.18591470160708254
 2_enve_rmcv_genELtask_89_08_00,1.0,0.0001219958521410272,0.09991460290350128,0.8000847230210912,0.6459578072807687,0.3701337537518133,0.44052073894654764
 2_ense_enself_genELtask_67_06_00,0.46644962922770844,0.25375293904865254,0.5800325556158438,0.6407290162706628,0.6003719605317819,0.04137604010048015,0.23842018265513718
 2_ense_rt10v_genELtask_12_01_00,0.0004931777083675817,0.4887391089922736,0.0,0.5544923070581674,0.0999428375052426,0.030987398154804432,0.053590028683394644
 2_ense_rt10v_genELtask_74_06_07,0.31950343025155176,0.07296090602199717,0.698573450941958,0.6786457966658624,0.5693710172340232,0.0570563755696918,0.2353767585504781
 2_enself_enve_genELtask_43_03_09,0.21570304481696886,0.12452959288402327,0.5964762230585016,0.8856732668763607,0.396194624558549,0.22753261728760327,0.2775285507201372
-2_ense_rutpt_genELtask_62_05_06,0.5542483660130719,0.09934640522875816,0.4745098039215686,0.7111494306868057,0.5012466678531697,0.08641979271745638,0.22363433457780976
+2_ense_rvpnot_genELtask_62_05_06,0.5542483660130719,0.09934640522875816,0.4745098039215686,0.7111494306868057,0.5012466678531697,0.08641979271745638,0.22363433457780976
 2_ense_enseef_genELtask_47_04_02,0.44871794871794873,0.2777777777777778,0.5726495726495726,0.6563532462804809,0.39718748690875827,0.09842879303797471,0.19147819831256924
 2_enself_enve_genELtask_53_04_08,0.9963735267452403,0.00020147073637554144,0.103253752392465,0.8385280573395623,0.62729086250449,0.37179140112570663,0.4368162810549426
 2_enseef_rt10v_genELtask_35_03_01,1.0,0.0006666666666666666,0.1,0.7255219891970006,0.6258186547355591,0.13283853776854676,0.2887741789125533
-2_rt10v_rutpt_genELtask_41_03_07,0.7766323024054983,0.005498281786941581,0.30103092783505153,0.7380254228287201,0.6008918934229416,0.08423928259446348,0.257093256228804
+2_rt10v_rvpnot_genELtask_41_03_07,0.7766323024054983,0.005498281786941581,0.30103092783505153,0.7380254228287201,0.6008918934229416,0.08423928259446348,0.257093256228804
 2_ense_rt10v_genELtask_43_03_09,0.1407055910872705,0.24963895192902827,0.8609449143800288,0.7094773609632139,0.298258299252895,0.031614888031461785,0.12336048753268711
-2_enseef_rutpt_genELtask_1_00_00,0.0003420557550880794,0.5127415768770309,0.0,0.2704260414863775,0.04369927556248628,0.029487682965891097,0.033966611015182245
+2_enseef_rvpnot_genELtask_1_00_00,0.0003420557550880794,0.5127415768770309,0.0,0.2704260414863775,0.04369927556248628,0.029487682965891097,0.033966611015182245
 2_ense_rmcv_genELtask_7_00_06,0.0070921985815602835,0.5354609929078015,0.0,0.2704260414863775,0.0631036481450275,0.042228688492871126,0.04885113668899094
-2_rt10v_rutpt_genELtask_62_05_06,0.6022125272670614,0.061389841071985043,0.4579308195699595,0.7112269863164916,0.5592120075631053,0.052215107316819484,0.22814516460296091
+2_rt10v_rvpnot_genELtask_62_05_06,0.6022125272670614,0.061389841071985043,0.4579308195699595,0.7112269863164916,0.5592120075631053,0.052215107316819484,0.22814516460296091
 2_ense_enve_genELtask_61_05_05,0.7095238095238096,0.14761904761904762,0.35714285714285715,0.4996267160933724,0.5005307070225866,0.07770916810122416,0.22060325231418743
 2_enself_rt10v_genELtask_35_03_01,1.0,0.008264462809917356,0.09917355371900827,0.6486161031143698,0.5808313889126127,0.31074899266789247,0.3829008532866322
-2_rt10v_rutpt_genELtask_91_08_02,0.20016488046166528,0.12481450948062654,0.7930750206100577,0.6726820104618338,0.491675112509337,0.04113294731814114,0.20013536180109112
+2_rt10v_rvpnot_genELtask_91_08_02,0.20016488046166528,0.12481450948062654,0.7930750206100577,0.6726820104618338,0.491675112509337,0.04113294731814114,0.20013536180109112
 2_enself_rmcv_genELtask_17_01_05,0.009868421052631578,0.5164473684210527,0.0,0.5544923070581674,0.14040926575315152,0.043394553030050825,0.07519339068793919
-2_enve_rutpt_genELtask_46_04_01,0.14583333333333334,0.25,0.25,0.44624779143465043,0.21501055272268005,0.0480911538435993,0.10442423359025803
+2_enve_rvpnot_genELtask_46_04_01,0.14583333333333334,0.25,0.25,0.44624779143465043,0.21501055272268005,0.0480911538435993,0.10442423359025803
 2_enseef_enve_genELtask_54_04_09,0.20489673095335795,0.002188483107646013,0.18738886609218985,0.8687890313003677,0.4991733795223106,0.2986728105234114,0.35258669969850326
-2_ense_rutpt_genELtask_54_04_09,0.9473684210526315,0.10526315789473684,0.10526315789473684,0.42386075520025757,0.4051256559031175,0.07632945394280476,0.18861636503762272
+2_ense_rvpnot_genELtask_54_04_09,0.9473684210526315,0.10526315789473684,0.10526315789473684,0.42386075520025757,0.4051256559031175,0.07632945394280476,0.18861636503762272
 2_enseef_enve_genELtask_32_02_09,0.11739130434782609,0.24633540372670806,0.6737888198757764,0.8988520186266573,0.3478145999214712,0.18615508054299854,0.23356656847426072
-2_enve_rutpt_genELtask_44_03_10,1.0,0.1,0.1,0.32607923012979584,0.31359105712814755,0.06558274127883523,0.14676045208231644
+2_enve_rvpnot_genELtask_44_03_10,1.0,0.1,0.1,0.32607923012979584,0.31359105712814755,0.06558274127883523,0.14676045208231644
 2_enself_rmcv_genELtask_14_01_02,0.6289308176100629,0.2138364779874214,0.4339622641509434,0.5521532069667591,0.5321938660580855,0.10071977633322196,0.24398229113471603
 2_rmcv_rt10v_genELtask_27_02_04,0.30303030303030304,0.2222222222222222,0.43434343434343436,0.5896814290376616,0.2888035815703274,0.06990916283130365,0.14032796498107944
-2_rmcv_rutpt_genELtask_25_02_02,0.19672131147540983,0.12986680327868852,0.7617827868852459,0.7376797055128703,0.432732906258211,0.0396515613565788,0.1765753928400492
+2_rmcv_rvpnot_genELtask_25_02_02,0.19672131147540983,0.12986680327868852,0.7617827868852459,0.7376797055128703,0.432732906258211,0.0396515613565788,0.1765753928400492
 2_enself_enve_genELtask_9_00_08,0.17209062821833163,0.09670442842430484,0.7829042224510814,0.8016088301190095,0.3431871264383427,0.06692940816152883,0.15659230487129236
 2_rmcv_rt10v_genELtask_37_03_03,0.15789473684210525,0.3157894736842105,0.3157894736842105,0.4536006268361936,0.2371787996708232,0.054679948482344885,0.1161435520680737
 2_rmcv_rt10v_genELtask_34_03_00,0.04424778761061947,0.26548672566371684,0.0,0.796453035938273,0.28580976196834856,0.044608312003522284,0.1460687246082342
-2_rmcv_rutpt_genELtask_39_03_05,0.41916167664670656,0.27844311377245506,0.6017964071856288,0.6751278958355251,0.39431303770630194,0.09769467089474564,0.19006045768775687
+2_rmcv_rvpnot_genELtask_39_03_05,0.41916167664670656,0.27844311377245506,0.6017964071856288,0.6751278958355251,0.39431303770630194,0.09769467089474564,0.19006045768775687
 2_ense_enself_genELtask_24_02_01,0.001614367874079306,0.16345474725052972,0.16345474725052972,0.7433100657623489,0.1961919798423869,0.08850192256822507,0.12519684172076942
-2_enseef_rutpt_genELtask_66_05_10,1.0,0.00010003000900270081,0.09992997899369811,0.7833211622447188,0.6637142181686587,0.3713938786321146,0.44626927597771393
+2_enseef_rvpnot_genELtask_66_05_10,1.0,0.00010003000900270081,0.09992997899369811,0.7833211622447188,0.6637142181686587,0.3713938786321146,0.44626927597771393
 2_ense_rt10v_genELtask_33_02_10,0.03995189738107963,0.2568145376803848,0.943479422768573,0.6624261416130302,0.184536932438705,0.022989885591513062,0.07852394472577166
-2_enve_rutpt_genELtask_70_06_03,0.32346723044397463,0.12156448202959831,0.6839323467230444,0.6009319526851553,0.50503998091767,0.04450400513340562,0.20723108800360734
+2_enve_rvpnot_genELtask_70_06_03,0.32346723044397463,0.12156448202959831,0.6839323467230444,0.6009319526851553,0.50503998091767,0.04450400513340562,0.20723108800360734
 2_ense_rmcv_genELtask_37_03_03,0.41916167664670656,0.27844311377245506,0.6017964071856288,0.6751278958355251,0.39431303770630194,0.09769467089474564,0.19006045768775687
-2_ense_rutpt_genELtask_25_02_02,0.21333333333333335,0.22,0.5066666666666667,0.48153049985461915,0.25469180942444536,0.03217123052335337,0.11058330161553356
-2_ense_rutpt_genELtask_26_02_03,0.21818181818181817,0.19090909090909092,0.37272727272727274,0.4185882982245169,0.24680054455062841,0.03080151008311218,0.10784810680878289
+2_ense_rvpnot_genELtask_25_02_02,0.21333333333333335,0.22,0.5066666666666667,0.48153049985461915,0.25469180942444536,0.03217123052335337,0.11058330161553356
+2_ense_rvpnot_genELtask_26_02_03,0.21818181818181817,0.19090909090909092,0.37272727272727274,0.4185882982245169,0.24680054455062841,0.03080151008311218,0.10784810680878289
 2_enseef_enve_genELtask_29_02_06,0.29489291598023065,0.14991762767710048,0.6828665568369028,0.6096246529758885,0.5012496436543203,0.039840166474235134,0.20415698015983666
 2_enseef_rt10v_genELtask_6_00_05,0.0015598752099832012,0.5035997120230381,0.5035997120230381,0.0,0.0,2.546640055463102e-15,-4.547571527612682e-16
 2_ense_rt10v_genELtask_36_03_02,0.27586206896551724,0.20689655172413793,0.20689655172413793,0.3754822886448377,0.22991363660166875,0.054761137545793405,0.11374640208002078
@@ -239,111 +239,111 @@ log,ratio_variants_per_number_of_traces,ratio_most_common_variant,ratio_top_10_v
 2_rmcv_rt10v_genELtask_12_01_00,1.0,0.08333333333333333,0.08333333333333333,0.45231085353309364,0.43519213980278576,0.16514608936760622,0.2427574303877492
 2_enve_rt10v_genELtask_109_09_09,0.06818993216708319,0.24491253123884327,0.8996786861835059,0.8673884726104196,0.3431019316874335,0.1201020315034845,0.1942958947991436
 2_enve_rt10v_genELtask_80_07_02,0.8734284204965663,0.01341785525620708,0.2138404648705758,0.7000159115393306,0.6218838633681831,0.07757545211157915,0.2621631612758759
-2_enve_rutpt_genELtask_48_04_03,0.25925925925925924,0.24074074074074073,0.24074074074074073,0.38257028438007107,0.2126868779315399,0.03911059205320834,0.09910643174300457
+2_enve_rvpnot_genELtask_48_04_03,0.25925925925925924,0.24074074074074073,0.24074074074074073,0.38257028438007107,0.2126868779315399,0.03911059205320834,0.09910643174300457
 2_enve_rt10v_genELtask_64_05_08,0.24334600760456274,0.25475285171102663,0.7490494296577946,0.5066807619897978,0.4794203095924818,0.062026818890137066,0.20911468018277765
-2_rt10v_rutpt_genELtask_49_04_04,0.3793103448275862,0.26436781609195403,0.41379310344827586,0.6170307544701875,0.357424170045835,0.0744209862188654,0.16782750028784751
+2_rt10v_rvpnot_genELtask_49_04_04,0.3793103448275862,0.26436781609195403,0.41379310344827586,0.6170307544701875,0.357424170045835,0.0744209862188654,0.16782750028784751
 2_enself_rmcv_genELtask_24_02_01,0.01081665765278529,0.10005408328826393,0.19469983775013522,0.7095927907435143,0.2809708916191249,0.15600026455721008,0.19250942915433888
 2_ense_enself_genELtask_47_04_02,0.2086673889490791,0.25395449620801736,0.6147345612134345,0.8589802177614073,0.39431929515589226,0.22489058861896516,0.27425910317395397
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 2_enseef_rt10v_genELtask_21_01_09,0.08450202044212028,0.25683384834799144,0.9058711671024483,0.6862764001833938,0.26606134142159177,0.023814340349533402,0.10831503009791116
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 2_ense_rt10v_genELtask_57_05_01,1.0,0.006666666666666667,0.1,0.5102807117432726,0.5015289717069972,0.06833762471996664,0.21555759725553392
 2_ense_enseef_genELtask_1_00_00,0.0025864095932283094,0.5043498706795203,0.5043498706795203,0.0,0.0,7.473261707331885e-15,0.0
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 2_enve_rmcv_genELtask_90_08_01,0.16251556662515568,0.10149439601494396,0.7654628476546285,0.7618969979103319,0.3488611583052632,0.06336942587057696,0.15720717657801694
 2_rmcv_rt10v_genELtask_29_02_06,0.44840230487166055,0.12624410686223153,0.5961236249345206,0.604473812783234,0.5757274143886218,0.05258030723866992,0.23615453007896575
 2_enseef_enve_genELtask_31_02_08,0.16261171797418073,0.24528301886792453,0.814299900695134,0.790956306110328,0.2879936536118941,0.054503267612948686,0.12985485823816556
 2_ense_enve_genELtask_42_03_08,0.01684700725927955,0.2532529790439666,0.5569100123270785,0.8093285216433496,0.3029351949964097,0.1602985783623636,0.20390300070227407
 2_ense_enve_genELtask_73_06_06,0.5989656793606017,0.1476257639868359,0.460742830277386,0.6001689565686317,0.5848493126902227,0.07609034216138655,0.2490630239283694
 2_ense_enve_genELtask_43_03_09,0.052930883639545054,0.2446777486147565,0.5701370662000583,0.8498744182570178,0.33624831602572935,0.18690457682213985,0.2317046265725234
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 2_rmcv_rt10v_genELtask_54_04_09,0.013009757317988492,0.42006504878658996,0.9056792594445834,0.5862046909986534,0.0918813316008443,0.006891360624649284,0.037237209578735504
 2_ense_enve_genELtask_52_04_07,0.45454545454545453,0.24,0.56,0.6864883769342432,0.3972178603498358,0.09515681359097054,0.18993089954505074
 2_rmcv_rt10v_genELtask_39_03_05,0.5333333333333333,0.36666666666666664,0.5,0.5270149315281448,0.43071533422413444,0.10463582341305287,0.20986648314647313
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 2_enve_rmcv_genELtask_100_09_00,0.9897970259416042,0.00021708455443395202,0.10908498860306089,0.8453838706256215,0.6217128322686242,0.37114184388753946,0.43509476466641256
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 2_ense_enve_genELtask_38_03_04,0.19318181818181818,0.3181818181818182,0.3181818181818182,0.4323018232725984,0.21596434768890624,0.049041550785924516,0.10474245919269466
 2_rmcv_rt10v_genELtask_55_04_10,0.033902625541677285,0.4088707621718073,0.9530971195513638,0.5916927705655913,0.14764489344927378,0.017014713263953804,0.062911268066326
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 2_enve_rt10v_genELtask_86_07_08,0.19177879639213366,0.1273103652225344,0.8000887180245453,0.6860680720129388,0.47272073876739407,0.03665228049279297,0.19112397415172433
 2_ense_enseef_genELtask_36_03_02,0.016761286834279535,0.12841308461746417,0.5477155988104894,0.81757394462252,0.3017996998651472,0.16009570477515675,0.20166053731375874
 2_ense_enve_genELtask_29_02_06,0.11224489795918367,0.2530612244897959,0.7510204081632653,0.6023618120023844,0.20666076557863647,0.05016799825589512,0.1018963587241109
 2_ense_enve_genELtask_86_07_08,1.0,0.00012236906510034264,0.09997552618697993,0.8012072398304192,0.6421081090158219,0.3695782486569548,0.43890952333995004
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 2_ense_enseef_genELtask_83_07_05,1.0,0.00010009008107296567,0.09998999099189271,0.7833077950948318,0.6637136599574901,0.3713893005508535,0.446266362534846
 2_ense_enseef_genELtask_25_02_02,0.02759381898454746,0.2588300220750552,0.5568432671081678,0.7830249707259856,0.2904626489754093,0.15041900418941212,0.19604792463125695
 2_rmcv_rt10v_genELtask_38_03_04,0.5802469135802469,0.30864197530864196,0.43209876543209874,0.5449766190213845,0.4625750410600026,0.07362228620802724,0.20529237802197206
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 2_enve_rt10v_genELtask_60_05_04,0.5454545454545454,0.2727272727272727,0.4318181818181818,0.5051467122301984,0.44951599902189926,0.07940470491266163,0.20417709469418027
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 2_enseef_rmcv_genELtask_25_02_02,0.6260869565217392,0.2,0.43478260869565216,0.44044255790793213,0.44271480048853556,0.08239413030758509,0.20317677019587796
 2_enself_rt10v_genELtask_7_00_06,0.4252386002120891,0.13414634146341464,0.6071049840933191,0.5845355586517406,0.561454918236543,0.038673104945239026,0.22397098202659466
 2_rmcv_rt10v_genELtask_2_00_01,1.0,0.00010384215991692627,0.1,0.8035076975616727,0.6352520830731573,0.36048740406460916,0.4291953685744355
 2_enseef_rmcv_genELtask_37_03_03,0.4737903225806452,0.26411290322580644,0.5725806451612904,0.7757655325345933,0.4694702799054282,0.23169359286087465,0.3014141926482471
 2_ense_rt10v_genELtask_6_00_05,0.00319693094629156,0.5003196930946292,0.5003196930946292,0.0,0.0,-4.578302287027697e-15,0.0
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 2_ense_enself_genELtask_78_07_00,0.9916525781910397,0.00042265426880811494,0.10745984784446323,0.6818091936552488,0.6628424662050423,0.06895795937018075,0.27299588014506926
 2_enve_rmcv_genELtask_40_03_06,0.00975609756097561,0.551219512195122,0.0,0.3074497498641964,0.07609500640214371,0.04809680564770084,0.05715571554672752
 2_rmcv_rt10v_genELtask_20_01_08,0.13463426607756504,0.1018654884634266,0.8000736377025037,0.771295486835231,0.3362591080615595,0.037828899355761364,0.13995832898981683
 2_ense_rmcv_genELtask_46_04_01,0.11209808582509696,0.10108845239584636,0.8120855748780182,0.7725966385412378,0.3442763478822593,0.059774186686932504,0.15321277124000215
 2_ense_rmcv_genELtask_18_01_06,0.08,0.52,0.0,0.3074497498641964,0.10130038164028596,0.06610450529037168,0.07721645898858422
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 2_ense_enseef_genELtask_70_06_03,0.9972401103955841,0.0018399264029438822,0.10211591536338546,0.724336660636945,0.6166055020592973,0.14787513906808894,0.2934965768961928
 2_enve_rmcv_genELtask_37_03_03,0.1,0.3,0.3,0.23115761362605888,0.12002962698793322,0.018358404953821968,0.052794819304296185
 2_enseef_enself_genELtask_48_04_03,0.9583065035415326,0.000643915003219575,0.13747585318737926,0.809867401546759,0.5782541706732083,0.334729310230363,0.39417869806656497
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 2_enseef_enve_genELtask_21_01_09,0.13018289219584234,0.2524139497898444,0.7349767124843803,0.8724449818942733,0.2696495696470786,0.03586180052656945,0.11404857291316693
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 2_ense_enve_genELtask_30_02_07,0.0635386119257087,0.25733137829912023,0.9337732160312805,0.6738098475094876,0.1999818525631695,0.029115786191157483,0.08750575580721748
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+2_enve_rvpnot_genELtask_99_08_10,1.0,0.0001690045631232043,0.09988169680581375,0.799977223796531,0.640383631910304,0.3685425363880637,0.4379235719821849
 2_ense_enve_genELtask_40_03_06,0.03277467411545624,0.12700186219739293,0.7221601489757914,0.5947160958430704,0.2533981999054099,0.0311431046824999,0.10746352648140392
 2_enself_enve_genELtask_6_00_05,0.3023255813953488,0.17054263565891473,0.46511627906976744,0.4973568940354535,0.3280453073325207,0.04822598629603691,0.1453281829033385
-2_enself_rutpt_genELtask_2_00_01,0.10176991150442478,0.23893805309734514,0.6393805309734514,0.5322213949490472,0.21319515406189085,0.03072342007702191,0.09437418196206221
+2_enself_rvpnot_genELtask_2_00_01,0.10176991150442478,0.23893805309734514,0.6393805309734514,0.5322213949490472,0.21319515406189085,0.03072342007702191,0.09437418196206221
 2_enself_enve_genELtask_1_00_00,0.0026067776218167233,0.501102867455384,0.501102867455384,0.0,0.0,-6.810189895984675e-17,-3.609400644871878e-15
 2_enve_rmcv_genELtask_104_09_04,0.0037136066547831252,0.3422459893048128,0.594622697563874,0.8909504129629577,0.21090335380048103,0.026735160378988727,0.10085831330768422
-2_enve_rutpt_genELtask_86_07_08,0.869810655062919,0.013407032811948725,0.2170998471127837,0.70905740361614,0.6098176049043782,0.07712728261624814,0.257469021786149
-2_rt10v_rutpt_genELtask_101_09_01,0.09905466502260583,0.3686806411837238,0.8882038635429511,0.6251196899811376,0.29977600013765887,0.04047241415032912,0.13059217457909608
+2_enve_rvpnot_genELtask_86_07_08,0.869810655062919,0.013407032811948725,0.2170998471127837,0.70905740361614,0.6098176049043782,0.07712728261624814,0.257469021786149
+2_rt10v_rvpnot_genELtask_101_09_01,0.09905466502260583,0.3686806411837238,0.8882038635429511,0.6251196899811376,0.29977600013765887,0.04047241415032912,0.13059217457909608
 2_enve_rt10v_genELtask_91_08_02,0.9996524157108099,0.0006951685783802572,0.10010427528675704,0.8002752103916206,0.6072425862732564,0.36098433758319076,0.42309407805468824
 2_ense_enself_genELtask_81_07_03,1.0,0.00023736055067647758,0.09992879183479705,0.7658359341913594,0.6500131547581898,0.3626600313644517,0.4365876844443246
-2_ense_rutpt_genELtask_51_04_06,0.627906976744186,0.27906976744186046,0.37209302325581395,0.558384700087116,0.40485226376390304,0.09661754309058429,0.19285137003477257
+2_ense_rvpnot_genELtask_51_04_06,0.627906976744186,0.27906976744186046,0.37209302325581395,0.558384700087116,0.40485226376390304,0.09661754309058429,0.19285137003477257
 2_enve_rt10v_genELtask_75_06_08,0.23708086785009863,0.13136094674556212,0.7420118343195267,0.6005781687884665,0.5050408696540936,0.028824075899169067,0.1995877088385617
 2_enself_rmcv_genELtask_34_03_00,1.0,0.00016142050040355126,0.09991928974979822,0.7850560742767585,0.6538034713016272,0.36854774608685437,0.44179968493655924
 2_enseef_rt10v_genELtask_23_02_00,0.5833333333333334,0.4166666666666667,0.0,0.44418611703376093,0.37274238284153316,0.09693751393724663,0.19555281983213932
 2_enseef_enve_genELtask_17_01_05,0.46153846153846156,0.21153846153846154,0.3076923076923077,0.5574625556191232,0.3853587210180783,0.0782543117636948,0.18096811968511958
-2_ense_rutpt_genELtask_43_03_09,0.8125,0.1875,0.1875,0.4817830830728342,0.3060857513378478,0.07166179861354416,0.14536265038691892
+2_ense_rvpnot_genELtask_43_03_09,0.8125,0.1875,0.1875,0.4817830830728342,0.3060857513378478,0.07166179861354416,0.14536265038691892
 2_ense_enseef_genELtask_48_04_03,0.110485384821668,0.2475194422097077,0.8943416465540359,0.8351035465746178,0.39680065127482733,0.10674930308961883,0.20486571104132703
 2_enve_rt10v_genELtask_46_04_01,1.0,0.045454545454545456,0.09090909090909091,0.40024899112774553,0.401246736283437,0.10727383549928544,0.20020761104820178
 2_enve_rmcv_genELtask_92_08_03,0.44106463878326996,0.24714828897338403,0.5342205323193916,0.7972297613442549,0.4270619813847052,0.08025054905553752,0.1919293846254568
 2_ense_enve_genELtask_1_00_00,0.0025864095932283094,0.5043498706795203,0.5043498706795203,0.0,0.0,7.473261707331885e-15,0.0
-2_ense_rutpt_genELtask_66_05_10,0.987012987012987,0.025974025974025976,0.1038961038961039,0.5159892252130439,0.4999734475465653,0.10274044797748526,0.23225986224195067
+2_ense_rvpnot_genELtask_66_05_10,0.987012987012987,0.025974025974025976,0.1038961038961039,0.5159892252130439,0.4999734475465653,0.10274044797748526,0.23225986224195067
 2_enseef_enve_genELtask_16_01_04,0.22916666666666666,0.20833333333333334,0.3958333333333333,0.40168598280831413,0.24596498762070543,0.029935020945679732,0.10766848262252789
 2_enve_rmcv_genELtask_58_05_02,0.5373134328358209,0.22388059701492538,0.40298507462686567,0.48503262882778997,0.4169063986076437,0.059254903179160115,0.18264825578212993
-2_rt10v_rutpt_genELtask_90_08_01,0.09908657664813345,0.1272835583796664,0.7950754567116759,0.7249830768547969,0.3400318850932857,0.04967216309276817,0.14753454215637338
+2_rt10v_rvpnot_genELtask_90_08_01,0.09908657664813345,0.1272835583796664,0.7950754567116759,0.7249830768547969,0.3400318850932857,0.04967216309276817,0.14753454215637338
 2_enself_enve_genELtask_29_02_06,0.6666666666666666,0.16666666666666666,0.3055555555555556,0.6292840916651702,0.475782381169641,0.1918647293996813,0.2774078864989419
 2_enve_rmcv_genELtask_79_07_01,0.5363292734145317,0.10415791684166317,0.5086098278034439,0.7000985055175301,0.5406974223868796,0.07473774430120915,0.2322458172819938
-2_ense_rutpt_genELtask_52_04_07,0.6818181818181818,0.25,0.38636363636363635,0.4739238769186882,0.4505683665469095,0.0976918339464617,0.2145274436450869
-2_rt10v_rutpt_genELtask_79_07_01,0.09985528219971057,0.12156295224312591,0.6808972503617945,0.6681469462041987,0.3230999495038559,0.060464763042918375,0.1473857016636812
-2_ense_rutpt_genELtask_87_07_09,0.966898768077129,0.004392072844134976,0.1297268344938404,0.6819471388068389,0.6462920379925329,0.07503885156416033,0.26973837902531095
-2_ense_rutpt_genELtask_55_04_10,1.0,0.04,0.08,0.4092429248404182,0.40446079826798087,0.06565450963292607,0.18158921705763814
-2_enve_rutpt_genELtask_38_03_04,0.32142857142857145,0.39285714285714285,0.0,0.2815314387437779,0.21169892664053308,0.02720905239408195,0.09143018139880967
+2_ense_rvpnot_genELtask_52_04_07,0.6818181818181818,0.25,0.38636363636363635,0.4739238769186882,0.4505683665469095,0.0976918339464617,0.2145274436450869
+2_rt10v_rvpnot_genELtask_79_07_01,0.09985528219971057,0.12156295224312591,0.6808972503617945,0.6681469462041987,0.3230999495038559,0.060464763042918375,0.1473857016636812
+2_ense_rvpnot_genELtask_87_07_09,0.966898768077129,0.004392072844134976,0.1297268344938404,0.6819471388068389,0.6462920379925329,0.07503885156416033,0.26973837902531095
+2_ense_rvpnot_genELtask_55_04_10,1.0,0.04,0.08,0.4092429248404182,0.40446079826798087,0.06565450963292607,0.18158921705763814
+2_enve_rvpnot_genELtask_38_03_04,0.32142857142857145,0.39285714285714285,0.0,0.2815314387437779,0.21169892664053308,0.02720905239408195,0.09143018139880967
 2_enself_enve_genELtask_31_02_08,0.2810404389351758,0.06157285104653526,0.7470026417394838,0.8020974201772291,0.5695363415823138,0.1998163209875276,0.3148465631538932
-2_enself_rutpt_genELtask_55_04_10,0.9998974253769617,0.00020514924607652066,0.10001025746230383,0.8194553151143993,0.7411017820217208,0.39511214985768045,0.48858916961185483
-2_rmcv_rutpt_genELtask_35_03_01,0.10077198786876206,0.24993107251171767,0.9092914254204577,0.6662719084591626,0.3059717257697536,0.035757435147494875,0.12910720254390906
+2_enself_rvpnot_genELtask_55_04_10,0.9998974253769617,0.00020514924607652066,0.10001025746230383,0.8194553151143993,0.7411017820217208,0.39511214985768045,0.48858916961185483
+2_rmcv_rvpnot_genELtask_35_03_01,0.10077198786876206,0.24993107251171767,0.9092914254204577,0.6662719084591626,0.3059717257697536,0.035757435147494875,0.12910720254390906
 2_enseef_rt10v_genELtask_46_04_01,1.0,0.0003418803418803419,0.09982905982905983,0.7763763279376865,0.6422398138541945,0.31380418597067344,0.399180129959901
-2_enve_rutpt_genELtask_57_05_01,0.1111111111111111,0.6666666666666666,0.0,0.4999999999999999,0.139495957203692,0.030830755985680865,0.06837355055371165
-2_rmcv_rutpt_genELtask_57_05_01,0.0013049588436057017,0.5020076289901626,0.5020076289901626,0.0,0.0,1.421321929360257e-14,0.0
+2_enve_rvpnot_genELtask_57_05_01,0.1111111111111111,0.6666666666666666,0.0,0.4999999999999999,0.139495957203692,0.030830755985680865,0.06837355055371165
+2_rmcv_rvpnot_genELtask_57_05_01,0.0013049588436057017,0.5020076289901626,0.5020076289901626,0.0,0.0,1.421321929360257e-14,0.0
 2_enve_rt10v_genELtask_71_06_04,0.6643059490084986,0.2355996222851747,0.40179414542020775,0.6001057755063985,0.5793400740816261,0.062127322662328,0.24010862015395681
 2_ense_enself_genELtask_68_06_01,0.761175367302282,0.0053141606752110035,0.31478587058455765,0.7531935860701843,0.5967503579781167,0.07214099869029295,0.2497593466653437
 2_ense_rt10v_genELtask_58_05_02,0.7633136094674556,0.07692307692307693,0.2958579881656805,0.524932721495906,0.5047899048327706,0.08542377579901086,0.22763418596207466
@@ -351,19 +351,19 @@ log,ratio_variants_per_number_of_traces,ratio_most_common_variant,ratio_top_10_v
 2_enseef_rt10v_genELtask_47_04_02,0.9921259842519685,0.007874015748031496,0.1062992125984252,0.7436225529832745,0.5711923633227731,0.3332148480591861,0.3954048890300234
 2_ense_rt10v_genELtask_79_07_01,1.0,0.00010415581710238516,0.09998958441828976,0.7789801981612675,0.6810201795770177,0.3644583350485039,0.4462805066055299
 2_ense_rt10v_genELtask_50_04_05,0.5364238410596026,0.2847682119205298,0.4966887417218543,0.6437490496674219,0.4703330942732151,0.12921919966151293,0.23647786415319852
-2_ense_rutpt_genELtask_71_06_04,0.3881051175656985,0.12697095435684647,0.6506224066390042,0.6567635962703023,0.6014998112318214,0.06855764199482094,0.2522619629357698
+2_ense_rvpnot_genELtask_71_06_04,0.3881051175656985,0.12697095435684647,0.6506224066390042,0.6567635962703023,0.6014998112318214,0.06855764199482094,0.2522619629357698
 2_ense_enself_genELtask_36_03_02,0.016810168101681018,0.25297252972529727,0.5563755637556376,0.8093285216433496,0.302896123447337,0.16030872784612715,0.20389406805804183
 2_enseef_enself_genELtask_59_05_03,1.0,0.00018460402436773122,0.09987077718294259,0.7775844395167896,0.6479500318666848,0.365615893812746,0.4378640889511959
 2_enseef_enve_genELtask_2_00_01,0.0013028663058729205,0.49779514932852276,0.49779514932852276,0.11914304012564805,0.06534339459007388,0.005110285429115894,0.026182908398533412
 2_ense_rmcv_genELtask_57_05_01,0.29049844236760125,0.12383177570093458,0.6978193146417445,0.6185605423502091,0.5100440532767193,0.04935306983718457,0.2114624220370411
-2_enself_rutpt_genELtask_21_01_09,0.8992176103964992,0.013791274366794854,0.19069088980241347,0.6856096487400428,0.6295739028676153,0.07547519077226403,0.26411515549590164
-2_enself_rutpt_genELtask_1_00_00,0.0013547311379741558,0.49781158816173404,0.49781158816173404,0.10218453802326659,0.0675166343463946,0.004116340451626956,0.02671499353043635
-2_enseef_rutpt_genELtask_44_03_10,1.0,0.0006393861892583121,0.09974424552429667,0.7290606136887061,0.6395080235119,0.15313171306700482,0.30310461985396187
+2_enself_rvpnot_genELtask_21_01_09,0.8992176103964992,0.013791274366794854,0.19069088980241347,0.6856096487400428,0.6295739028676153,0.07547519077226403,0.26411515549590164
+2_enself_rvpnot_genELtask_1_00_00,0.0013547311379741558,0.49781158816173404,0.49781158816173404,0.10218453802326659,0.0675166343463946,0.004116340451626956,0.02671499353043635
+2_enseef_rvpnot_genELtask_44_03_10,1.0,0.0006393861892583121,0.09974424552429667,0.7290606136887061,0.6395080235119,0.15313171306700482,0.30310461985396187
 2_enseef_enself_genELtask_36_03_02,0.20917573872472783,0.2391135303265941,0.7951010886469674,0.8444560133795129,0.4429981438289011,0.19916032772964534,0.2776008086788132
 2_enve_rt10v_genELtask_97_08_08,0.12171877612439391,0.1260658752716937,0.79969904698211,0.7694178670024279,0.3676608611862521,0.056639640397514714,0.16038201097121524
 2_ense_rmcv_genELtask_78_07_00,1.0,0.00010003000900270081,0.09992997899369811,0.7833211622447188,0.6637142181686587,0.3713938786321146,0.44626927597771393
 2_ense_enseef_genELtask_69_06_02,0.764126149802891,0.005256241787122208,0.3120893561103811,0.7518665967768557,0.5972165683871318,0.07214013670664875,0.24994366904884743
-2_rt10v_rutpt_genELtask_68_06_01,0.08356545961002786,0.24535747446610956,0.6032961931290622,0.8372714394861207,0.3602349959538146,0.19967398724448748,0.24717809112009154
+2_rt10v_rvpnot_genELtask_68_06_01,0.08356545961002786,0.24535747446610956,0.6032961931290622,0.8372714394861207,0.3602349959538146,0.19967398724448748,0.24717809112009154
 2_ense_enve_genELtask_62_05_06,0.6486486486486487,0.12162162162162163,0.2972972972972973,0.5987897455683974,0.525552674154493,0.07561118305981229,0.23037955648743713
 2_ense_enseef_genELtask_13_01_01,0.026578073089700997,0.5083056478405316,0.0,0.1740530650312864,0.10198069029611968,0.01237384910245176,0.043519148640779326
 2_ense_enseef_genELtask_35_03_01,0.031334688346883466,0.12652439024390244,0.7335704607046071,0.5915279556422066,0.2518333074465074,0.0312108819736288,0.10693418972580752
@@ -374,95 +374,95 @@ log,ratio_variants_per_number_of_traces,ratio_most_common_variant,ratio_top_10_v
 2_enve_rt10v_genELtask_82_07_04,0.6661613098847786,0.0661006670709521,0.40024257125530627,0.672249151728684,0.5796518064398385,0.06815187140942756,0.24348854205954132
 2_ense_enve_genELtask_48_04_03,0.2830188679245283,0.3018867924528302,0.3018867924528302,0.2976186528786419,0.33441781052697933,0.04907876271990908,0.1524889268148327
 2_enself_rmcv_genELtask_3_00_02,0.6,0.15,0.15,0.3389522680780381,0.2655041226965246,0.06731954349325184,0.13227886361492472
-2_rt10v_rutpt_genELtask_52_04_07,0.6667763157894737,0.06151315789473684,0.3996710526315789,0.6820205360852811,0.579248179708277,0.06244191945416506,0.24039112211371946
-2_rt10v_rutpt_genELtask_61_05_05,0.5433911882510013,0.1068090787716956,0.5013351134846462,0.6936744365145042,0.5258616232094979,0.07097841656529008,0.22531839241753018
+2_rt10v_rvpnot_genELtask_52_04_07,0.6667763157894737,0.06151315789473684,0.3996710526315789,0.6820205360852811,0.579248179708277,0.06244191945416506,0.24039112211371946
+2_rt10v_rvpnot_genELtask_61_05_05,0.5433911882510013,0.1068090787716956,0.5013351134846462,0.6936744365145042,0.5258616232094979,0.07097841656529008,0.22531839241753018
 2_enseef_enself_genELtask_49_04_04,1.0,0.0001998001998001998,0.0999000999000999,0.7826287958648518,0.6425827775941395,0.3652836235829632,0.4361479574757356
 2_enseef_enve_genELtask_1_00_00,0.0025864095932283094,0.5043498706795203,0.5043498706795203,0.0,0.0,7.473261707331885e-15,0.0
 2_enve_rt10v_genELtask_87_07_09,0.06019636246579752,0.2502816674714309,0.903750201191051,0.6686518108797188,0.22893871558051357,0.03027657595879006,0.09870494386128849
 2_enself_rt10v_genELtask_5_00_04,0.6661613098847786,0.0661006670709521,0.40024257125530627,0.672249151728684,0.5796518064398385,0.06815187140942756,0.24348854205954132
-2_enself_rutpt_genELtask_15_01_03,0.29977628635346754,0.1309967685806612,0.7017151379567487,0.7651685138918896,0.5126735885740216,0.067947545802514,0.21874266825922745
+2_enself_rvpnot_genELtask_15_01_03,0.29977628635346754,0.1309967685806612,0.7017151379567487,0.7651685138918896,0.5126735885740216,0.067947545802514,0.21874266825922745
 2_ense_rt10v_genELtask_61_05_05,0.516439205955335,0.02760545905707196,0.5009305210918115,0.7745136122766582,0.4929459161110574,0.10276588539798542,0.22710175413488484
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 2_enve_rt10v_genELtask_84_07_06,0.45558086560364464,0.2460136674259681,0.5899772209567198,0.6900910233148569,0.4236890810238842,0.10161554537368604,0.20221421119650995
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 2_enve_rt10v_genELtask_85_07_07,0.2954140694568121,0.18410507569011575,0.715939447907391,0.700017560023879,0.4721637847691348,0.05584266803213225,0.199149846635136
 2_enseef_enself_genELtask_24_02_01,0.40627943485086343,0.2552590266875981,0.6342229199372057,0.7237630019929844,0.4694623510927801,0.06066621034601321,0.19888589789103306
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 2_enve_rt10v_genELtask_56_05_00,0.0003090871625798475,0.49320008242324337,0.0,0.4999999999999999,0.0852421620693921,0.01948216805543355,0.04226742439364094
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 2_enseef_rt10v_genELtask_29_02_06,0.4347048300536673,0.24329159212880144,0.5957066189624329,0.6819866763662089,0.42114189450261236,0.09408246450097306,0.19892527550719316
 2_enve_rt10v_genELtask_26_02_03,0.2222222222222222,0.26666666666666666,0.26666666666666666,0.22915618276080518,0.1300169486432834,0.018828712697632134,0.057118426737216836
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 2_enve_rt10v_genELtask_102_09_02,0.0013012361743656475,0.24632400780741703,0.24632400780741703,0.8752746979566216,0.21375086821159336,0.049826064706148324,0.11777605704729466
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 2_ense_rmcv_genELtask_15_01_03,0.001971900419028839,0.2578259797880207,0.0,0.7632300273809491,0.13516226549562854,0.019666957207139804,0.06524562750652554
 2_ense_enve_genELtask_18_01_06,0.0013430029546065002,0.24576954069298954,0.0,0.594439569964495,0.14396643688882182,0.06265961176403209,0.08783928954389866
 2_enve_rmcv_genELtask_81_07_03,0.38219895287958117,0.3089005235602094,0.612565445026178,0.6810138342248351,0.36964289136155537,0.06861744535906125,0.16905873396339655
 2_enve_rt10v_genELtask_83_07_05,0.48951911220715166,0.07336621454993834,0.5413070283600493,0.7024083466841994,0.5087944795328581,0.06494299743605994,0.21598512046753815
 2_ense_enve_genELtask_53_04_08,0.1758498111530771,0.045434347922683846,0.7716063097089536,0.8005099998768914,0.3912189839489091,0.05030821207679507,0.1652212546093527
 2_enseef_rmcv_genELtask_45_04_00,1.0,0.0002092487968194183,0.09981167608286252,0.8010268154561254,0.6294363890676796,0.35323453226291174,0.4235015078424366
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 2_enve_rt10v_genELtask_48_04_03,0.75,0.2222222222222222,0.3055555555555556,0.41580850709223377,0.40105083683640147,0.09939495266075477,0.19817630664922217
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 2_ense_rmcv_genELtask_17_01_05,0.02531645569620253,0.5158227848101266,0.0,0.1740530650312864,0.10118276410294973,0.012244273914122975,0.04313738559435483
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 2_ense_enve_genELtask_16_01_04,0.125,0.4270833333333333,0.4270833333333333,0.37977164790872353,0.1622854692880737,0.036260454323699315,0.07973718201783332
 2_ense_enve_genELtask_51_04_06,0.0732949876746097,0.2580115036976171,0.924404272801972,0.6000234339995398,0.3993433083670912,0.028582784747735415,0.16064195470862228
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 2_enseef_rmcv_genELtask_14_01_02,0.32,0.2,0.2,0.37499469519394973,0.2398189463899838,0.06188592032426653,0.12075482792730102
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 2_enself_rmcv_genELtask_45_04_00,1.0,0.0001012965964343598,0.09997974068071312,0.8041718984850963,0.6457628074879497,0.3712767004508105,0.4411375913173955
 2_rmcv_rt10v_genELtask_15_01_03,0.7704280155642024,0.09727626459143969,0.3035019455252918,0.5586330306075828,0.5163177132435344,0.0605368526629282,0.21854530558450153
 2_ense_rmcv_genELtask_35_03_01,0.05998967475477542,0.16427465152297366,0.859370160041301,0.7304451215612114,0.29080919521576903,0.02997793964457697,0.11984877400796907
 2_enseef_enve_genELtask_27_02_04,0.6,0.2777777777777778,0.45555555555555555,0.4865160966404545,0.4658942136580921,0.06805462783080964,0.20499107635551528
 2_ense_enve_genELtask_3_00_02,0.0016329196603527107,0.3718702373176573,0.3718702373176573,0.20363683619624154,0.0764214182151027,0.00497356134154834,0.030589120141464723
 2_ense_rmcv_genELtask_47_04_02,0.30198019801980197,0.22277227722772278,0.6584158415841584,0.7471547312464346,0.4038893039608062,0.1551549209277123,0.23129472369676313
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 2_ense_enself_genELtask_25_02_02,0.0031762837480148226,0.0907005470266455,0.0907005470266455,0.6618275952141233,0.19246728598059074,0.11902182829030933,0.1394710875120019
 2_enseef_rt10v_genELtask_43_03_09,0.09552042160737813,0.24989020641194554,0.9003074220465525,0.8418244132849267,0.3991000337039671,0.11677473026170786,0.21293022099653602
 2_enseef_enve_genELtask_18_01_06,0.03487903225806452,0.1276209677419355,0.7213709677419354,0.5998737255903773,0.25531830594536575,0.03414178561179253,0.10969598463993006
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 2_ense_rt10v_genELtask_41_03_07,0.06613080014614542,0.13664596273291926,0.6996711728169529,0.622377964892789,0.29327204162470544,0.039979479224951174,0.12633827013351837
 2_ense_enve_genELtask_85_07_07,1.0,0.00014405070584845865,0.09997118985883031,0.7837118091476817,0.6531592748479932,0.35914166799672065,0.43414871665573956
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 2_enseef_rmcv_genELtask_36_03_02,0.3014705882352941,0.19852941176470587,0.5955882352941176,0.708578356776798,0.5119588010160566,0.19052663999712707,0.2978106945198214
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 2_ense_enself_genELtask_1_00_00,0.0022731969415168423,0.5067162636908452,0.5067162636908452,0.0,0.0,8.012961916646188e-15,0.0
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 2_enself_rt10v_genELtask_13_01_01,0.9961795606494747,0.0019102196752626551,0.10315186246418338,0.7010523634251199,0.6196832373347432,0.10028681113328293,0.27161496058235857
 2_ense_rmcv_genELtask_36_03_02,0.22916666666666666,0.20833333333333334,0.3958333333333333,0.40168598280831413,0.24596498762070543,0.029935020945679732,0.10766848262252789
 2_enself_rmcv_genELtask_13_01_01,0.2582322357019064,0.10051993067590988,0.6386481802426344,0.729248154106986,0.3840925019894245,0.06902428031906946,0.1727491553497898
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 2_enseef_enve_genELtask_53_04_08,0.6159527326440177,0.001292466765140325,0.20937961595273263,0.869519969473638,0.5589565747011223,0.33736277212869825,0.3953397577290522
 2_ense_enve_genELtask_64_05_08,0.31623711340206184,0.1229381443298969,0.6494845360824743,0.8030423577628137,0.5185450256707083,0.08625280114640559,0.22974721047461572
 2_enself_rt10v_genELtask_6_00_05,0.5433911882510013,0.1068090787716956,0.5013351134846462,0.6936744365145042,0.5258616232094979,0.07097841656529008,0.22531839241753018
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 2_enve_rt10v_genELtask_88_07_10,0.015869541424975375,0.49786582029112403,0.9579730765021341,0.6472237231040165,0.13362239599550835,0.017704826406289068,0.0575526298728438
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 2_enve_rmcv_genELtask_91_08_02,0.13818035750150634,0.20094396465153647,0.723036754368347,0.8784420223411401,0.35430814284936696,0.18787216435704485,0.23538045341623623
-2_enseef_rutpt_genELtask_37_03_03,0.296979006656426,0.06810035842293907,0.710189452124936,0.7784347481464543,0.5699306714904235,0.18959348845193588,0.3086060205396162
+2_enseef_rvpnot_genELtask_37_03_03,0.296979006656426,0.06810035842293907,0.710189452124936,0.7784347481464543,0.5699306714904235,0.18959348845193588,0.3086060205396162
 2_ense_rmcv_genELtask_26_02_03,0.0702656383890317,0.2570694087403599,0.8886032562125107,0.6299754360953089,0.1990478474817289,0.024139519269408775,0.0845225525446484
 2_enself_enve_genELtask_32_02_09,0.2619600557361821,0.13098002786809104,0.5993961913608917,0.8811558495647152,0.3824161520826712,0.2168925377941807,0.26475857699453814
-2_rmcv_rutpt_genELtask_22_01_10,1.0,0.1,0.1,0.42981722297645075,0.4126522969207665,0.13128722196118042,0.2163437707737449
+2_rmcv_rvpnot_genELtask_22_01_10,1.0,0.1,0.1,0.42981722297645075,0.4126522969207665,0.13128722196118042,0.2163437707737449
 2_enseef_enve_genELtask_15_01_03,0.17777777777777778,0.4444444444444444,0.0,0.25051895835594074,0.1571106189305964,0.02366800002033687,0.0689984383974066
-2_enve_rutpt_genELtask_98_08_09,0.8924839905628581,0.003370407819346141,0.19666329625884732,0.7976536746222977,0.6051694753683879,0.1151442877863892,0.27259308430368995
+2_enve_rvpnot_genELtask_98_08_09,0.8924839905628581,0.003370407819346141,0.19666329625884732,0.7976536746222977,0.6051694753683879,0.1151442877863892,0.27259308430368995
 2_ense_enself_genELtask_35_03_01,0.10117713004484305,0.24957959641255606,0.8783632286995515,0.7065132851227635,0.30052144038920375,0.0406935342038492,0.12905128054365855
 2_ense_rt10v_genELtask_25_02_02,0.32,0.2,0.2,0.37499469519394973,0.2398189463899838,0.06188592032426653,0.12075482792730102
-2_rt10v_rutpt_genELtask_80_07_02,0.1993997999333111,0.12870956985661888,0.7917639213071024,0.6831475616036886,0.4750582811403656,0.03672681784281664,0.19206206477852675
-2_rt10v_rutpt_genELtask_92_08_03,0.29977628635346754,0.1309967685806612,0.7017151379567487,0.7651685138918896,0.5126735885740216,0.067947545802514,0.21874266825922745
+2_rt10v_rvpnot_genELtask_80_07_02,0.1993997999333111,0.12870956985661888,0.7917639213071024,0.6831475616036886,0.4750582811403656,0.03672681784281664,0.19206206477852675
+2_rt10v_rvpnot_genELtask_92_08_03,0.29977628635346754,0.1309967685806612,0.7017151379567487,0.7651685138918896,0.5126735885740216,0.067947545802514,0.21874266825922745
 2_enve_rt10v_genELtask_23_02_00,0.75,0.25,0.0,0.2831603769466446,0.27745696722380864,0.06489388494715419,0.13671190564550428
 2_enself_rt10v_genELtask_8_00_07,0.28636959370904325,0.12844036697247707,0.7018348623853211,0.6413055471588315,0.49672911062139924,0.04463873319780641,0.20435926571648755
 2_enseef_enve_genELtask_19_01_07,0.08996486940583473,0.252634794562395,0.8962883763555827,0.6995695821563233,0.2391841278207606,0.028082589006025237,0.10025710960556052
 2_ense_rt10v_genELtask_51_04_06,0.45217391304347826,0.2956521739130435,0.5652173913043478,0.613215564511786,0.40315738052926153,0.10424819354563676,0.19830997121204794
 2_enseef_rt10v_genELtask_11_00_10,0.01593536079003479,0.3670744024239704,0.9541016720906744,0.6816705538868325,0.10820687391454807,0.01550907730536071,0.047017460362925666
 2_rmcv_rt10v_genELtask_67_06_00,0.08,0.52,0.0,0.3109175070825711,0.08390280721095665,0.0622188642316662,0.07060332870822822
-2_ense_rutpt_genELtask_36_03_02,0.19965603502188867,0.09537210756722952,0.7543777360850532,0.794364911969114,0.3548793205192863,0.0689798857741429,0.16183663117422442
+2_ense_rvpnot_genELtask_36_03_02,0.19965603502188867,0.09537210756722952,0.7543777360850532,0.794364911969114,0.3548793205192863,0.0689798857741429,0.16183663117422442
 2_enve_rt10v_genELtask_79_07_01,1.0,0.0012048192771084338,0.1,0.6995442371093478,0.6252933317570496,0.11595428340349441,0.28048951226640645
 2_enve_rt10v_genELtask_72_06_05,0.5552763819095478,0.12814070351758794,0.5,0.5874283677778875,0.5748476231040041,0.0547695075800935,0.235128669020601
 2_enself_rmcv_genELtask_25_02_02,0.112,0.224,0.384,0.7624028497623528,0.4071310604049214,0.14935246760473175,0.23914108043733187
diff --git a/data/grid_1obj/grid_1objectives_ense.csv b/data/grid_1obj/grid_1objectives_ense.csv
deleted file mode 100644
index ff4ffc1eb17e62d994d7a0a4884c0b192503f417..0000000000000000000000000000000000000000
--- a/data/grid_1obj/grid_1objectives_ense.csv
+++ /dev/null
@@ -1,12 +0,0 @@
-task,epa_normalized_sequence_entropy
-task_1,0.0
-task_2,0.1
-task_3,0.2
-task_4,0.3
-task_5,0.4
-task_6,0.5
-task_7,0.6
-task_8,0.7
-task_9,0.8
-task_10,0.9
-task_11,1.0
diff --git a/data/grid_1obj/grid_1objectives_enseef.csv b/data/grid_1obj/grid_1objectives_enseef.csv
deleted file mode 100644
index 7f6ffb06ab9dc5d2516317786d72a46e673fbc59..0000000000000000000000000000000000000000
--- a/data/grid_1obj/grid_1objectives_enseef.csv
+++ /dev/null
@@ -1,12 +0,0 @@
-task,epa_normalized_sequence_entropy_exponential_forgetting
-task_1,0.0
-task_2,0.1
-task_3,0.2
-task_4,0.3
-task_5,0.4
-task_6,0.5
-task_7,0.6
-task_8,0.7
-task_9,0.8
-task_10,0.9
-task_11,1.0
diff --git a/data/grid_1obj/grid_1objectives_enself.csv b/data/grid_1obj/grid_1objectives_enself.csv
deleted file mode 100644
index 6a966a985daf04cdf2642c56a160d524345be4cf..0000000000000000000000000000000000000000
--- a/data/grid_1obj/grid_1objectives_enself.csv
+++ /dev/null
@@ -1,12 +0,0 @@
-task,epa_normalized_sequence_entropy_linear_forgetting
-task_1,0.0
-task_2,0.1
-task_3,0.2
-task_4,0.3
-task_5,0.4
-task_6,0.5
-task_7,0.6
-task_8,0.7
-task_9,0.8
-task_10,0.9
-task_11,1.0
diff --git a/data/grid_1obj/grid_1objectives_enve.csv b/data/grid_1obj/grid_1objectives_enve.csv
deleted file mode 100644
index 5381a65a28b3118bd7b0581c2cee1cb85e2a8776..0000000000000000000000000000000000000000
--- a/data/grid_1obj/grid_1objectives_enve.csv
+++ /dev/null
@@ -1,12 +0,0 @@
-task,epa_normalized_variant_entropy
-task_1,0.0
-task_2,0.1
-task_3,0.2
-task_4,0.3
-task_5,0.4
-task_6,0.5
-task_7,0.6
-task_8,0.7
-task_9,0.8
-task_10,0.9
-task_11,1.0
\ No newline at end of file
diff --git a/data/grid_1obj/grid_1objectives_rmcv.csv b/data/grid_1obj/grid_1objectives_rmcv.csv
deleted file mode 100644
index c3addb378b0a62e5d1ada43f7d5f52fdf4245de3..0000000000000000000000000000000000000000
--- a/data/grid_1obj/grid_1objectives_rmcv.csv
+++ /dev/null
@@ -1,12 +0,0 @@
-task,ratio_most_common_variant
-task_1,0.0
-task_2,0.1
-task_3,0.2
-task_4,0.3
-task_5,0.4
-task_6,0.5
-task_7,0.6
-task_8,0.7
-task_9,0.8
-task_10,0.9
-task_11,1.0
diff --git a/data/grid_1obj/grid_1objectives_rt10v.csv b/data/grid_1obj/grid_1objectives_rt10v.csv
deleted file mode 100644
index 38b8e81f2672cc214a3a4ac447edcd5c6fba648d..0000000000000000000000000000000000000000
--- a/data/grid_1obj/grid_1objectives_rt10v.csv
+++ /dev/null
@@ -1,12 +0,0 @@
-task,ratio_top_10_variants
-task_1,0.0
-task_2,0.1
-task_3,0.2
-task_4,0.3
-task_5,0.4
-task_6,0.5
-task_7,0.6
-task_8,0.7
-task_9,0.8
-task_10,0.9
-task_11,1.0
diff --git a/data/grid_1obj/grid_1objectives_rutpt.csv b/data/grid_1obj/grid_1objectives_rutpt.csv
deleted file mode 100644
index 1fbe9e1d23883b8851ca2ee4459dc248b678405d..0000000000000000000000000000000000000000
--- a/data/grid_1obj/grid_1objectives_rutpt.csv
+++ /dev/null
@@ -1,12 +0,0 @@
-task,ratio_unique_traces_per_trace
-task_1,0.0
-task_2,0.1
-task_3,0.2
-task_4,0.3
-task_5,0.4
-task_6,0.5
-task_7,0.6
-task_8,0.7
-task_9,0.8
-task_10,0.9
-task_11,1.0
diff --git a/data/grid_2obj/grid_2objectives_enve_rutpt.csv b/data/grid_2obj/grid_2objectives_ense_rvpnot.csv
similarity index 96%
rename from data/grid_2obj/grid_2objectives_enve_rutpt.csv
rename to data/grid_2obj/grid_2objectives_ense_rvpnot.csv
index e2f17cf9d1d06e513eb01036ca1958075ba9ff06..ed702cd3a0c202db977524430e1545012019d0ec 100644
--- a/data/grid_2obj/grid_2objectives_enve_rutpt.csv
+++ b/data/grid_2obj/grid_2objectives_ense_rvpnot.csv
@@ -1,4 +1,4 @@
-task,epa_normalized_variant_entropy,ratio_unique_traces_per_trace
+task,epa_normalized_sequence_entropy,ratio_variants_per_number_of_traces
 task_1,0.0,0.0
 task_2,0.0,0.1
 task_3,0.0,0.2
diff --git a/data/grid_2obj/grid_2objectives_enseef_rutpt.csv b/data/grid_2obj/grid_2objectives_enseef_rvpnot.csv
similarity index 98%
rename from data/grid_2obj/grid_2objectives_enseef_rutpt.csv
rename to data/grid_2obj/grid_2objectives_enseef_rvpnot.csv
index ece05829e1a9f8173fceeec64c400c204c291c74..2eec80ba1124fd323471f8d9e367091f6426a495 100644
--- a/data/grid_2obj/grid_2objectives_enseef_rutpt.csv
+++ b/data/grid_2obj/grid_2objectives_enseef_rvpnot.csv
@@ -1,4 +1,4 @@
-task,epa_normalized_sequence_entropy_exponential_forgetting,ratio_unique_traces_per_trace
+task,epa_normalized_sequence_entropy_exponential_forgetting,ratio_variants_per_number_of_traces
 task_1,0.0,0.0
 task_2,0.0,0.1
 task_3,0.0,0.2
diff --git a/data/grid_2obj/grid_2objectives_enself_rutpt.csv b/data/grid_2obj/grid_2objectives_enself_rvpnot.csv
similarity index 95%
rename from data/grid_2obj/grid_2objectives_enself_rutpt.csv
rename to data/grid_2obj/grid_2objectives_enself_rvpnot.csv
index 3da75d9397666c13e0a8a0e70d9d5ef3e3333dab..cf22736b93a20635ca9d45cc32ff6e203e5efabd 100644
--- a/data/grid_2obj/grid_2objectives_enself_rutpt.csv
+++ b/data/grid_2obj/grid_2objectives_enself_rvpnot.csv
@@ -1,4 +1,4 @@
-task,epa_normalized_sequence_entropy_linear_forgetting,ratio_unique_traces_per_trace
+task,epa_normalized_sequence_entropy_linear_forgetting,ratio_variants_per_number_of_traces
 task_1,0.0,0.0
 task_2,0.0,0.1
 task_3,0.0,0.2
diff --git a/data/grid_2obj/grid_2objectives_rt10v_rutpt.csv b/data/grid_2obj/grid_2objectives_enve_rvpnot.csv
similarity index 96%
rename from data/grid_2obj/grid_2objectives_rt10v_rutpt.csv
rename to data/grid_2obj/grid_2objectives_enve_rvpnot.csv
index ed090903cd850f374a370f650ad3b3b2ae5f77fb..ebc6f3864318f36bd70f7f1de3b76b87f20f871d 100644
--- a/data/grid_2obj/grid_2objectives_rt10v_rutpt.csv
+++ b/data/grid_2obj/grid_2objectives_enve_rvpnot.csv
@@ -1,4 +1,4 @@
-task,ratio_top_10_variants,ratio_unique_traces_per_trace
+task,epa_normalized_variant_entropy,ratio_variants_per_number_of_traces
 task_1,0.0,0.0
 task_2,0.0,0.1
 task_3,0.0,0.2
diff --git a/data/grid_2obj/grid_2objectives_ense_rutpt.csv b/data/grid_2obj/grid_2objectives_rmcv_rvpnot.csv
similarity index 96%
rename from data/grid_2obj/grid_2objectives_ense_rutpt.csv
rename to data/grid_2obj/grid_2objectives_rmcv_rvpnot.csv
index b43cbd240677fa41ee59bc52ba9ea870a61cb21c..d2de5439e9cb623661b6ee9a6a288b4679f86933 100644
--- a/data/grid_2obj/grid_2objectives_ense_rutpt.csv
+++ b/data/grid_2obj/grid_2objectives_rmcv_rvpnot.csv
@@ -1,4 +1,4 @@
-task,epa_normalized_sequence_entropy,ratio_unique_traces_per_trace
+task,ratio_most_common_variant,ratio_variants_per_number_of_traces
 task_1,0.0,0.0
 task_2,0.0,0.1
 task_3,0.0,0.2
diff --git a/data/grid_2obj/grid_2objectives_rmcv_rutpt.csv b/data/grid_2obj/grid_2objectives_rt10v_rvpnot.csv
similarity index 96%
rename from data/grid_2obj/grid_2objectives_rmcv_rutpt.csv
rename to data/grid_2obj/grid_2objectives_rt10v_rvpnot.csv
index 00b7285bfd7efbd9efb4ea78afc35ef2da09a416..713328ed3cfb7dcc4f47ff4b1be6dd56d858541e 100644
--- a/data/grid_2obj/grid_2objectives_rmcv_rutpt.csv
+++ b/data/grid_2obj/grid_2objectives_rt10v_rvpnot.csv
@@ -1,4 +1,4 @@
-task,ratio_most_common_variant,ratio_unique_traces_per_trace
+task,ratio_top_10_variants,ratio_variants_per_number_of_traces
 task_1,0.0,0.0
 task_2,0.0,0.1
 task_3,0.0,0.2
diff --git a/data/test/2_bpic_features.csv b/data/test/2_bpic_features.csv
new file mode 100644
index 0000000000000000000000000000000000000000..f83d7b5026f3d9dcdd76c2d602931168034a8353
--- /dev/null
+++ b/data/test/2_bpic_features.csv
@@ -0,0 +1,3 @@
+log,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,eventropy_trace,eventropy_prefix,eventropy_global_block,eventropy_lempel_ziv,eventropy_k_block_diff_1,eventropy_k_block_diff_3,eventropy_k_block_diff_5,eventropy_k_block_ratio_1,eventropy_k_block_ratio_3,eventropy_k_block_ratio_5,eventropy_knn_3,eventropy_knn_5,eventropy_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,eventropy_global_block_flattened,eventropy_lempel_ziv_flattened,eventropy_prefix_flattened
+Sepsis_Cases_Event_Log,1050,846,0.805714285714285,3,185,14.48952380952381,13,8,11.470474925273926,131.57179501133788,9,16,7,12.281860759040903,1.7464004837799152,10.47731701485374,7.250526815880918,87.0376906898399,0.791639192292468,6.769403523350811,0.04861329147043401,0.005285190999476001,0.000575614861329,0.000209314495028,0.000104657247514,0.0,5.2328623757195225e-05,0.0,0.0,0.000104657247514,2.612850778156251,4.931206347805768,0.033333333333333,0.12,0.215238095238095,0.274285714285714,0.355238095238095,0.5971428571428571,0.7980952380952381,1.241134751773049,1.759408518249193,13.637101374069475,217.44268017168216,16,6,3383,950.875,788.0,1008.5815457239935,1017236.734375,101.75,1085.25,983.5,1.391238560701821,1.05777753209275,6,6,995,175.0,12.0,366.73787187399483,134496.66666666666,7.75,17.0,9.25,1.7883562472303312,1.199106773708694,14,2,393,75.0,32.5,112.91400014423114,12749.57142857143,14.0,53.5,39.5,2.004413358907822,2.500757934341361,9.334,10.227,14.501,1.7269999999999999,3.238,1.712,1.104,3.238,2.262,1.871,4.956,4.49,4.191,40624.49329803771,0.6957588422064961,76528.6794749776,0.5223430410751391,32139.284589305265,0.219365233602993,43880.53919110408,0.299504635939686,,,
+CoSeLoG_WABO_1,937,916,0.9775880469583771,2,95,41.56243329775881,43,40,16.678023092416094,278.1564542711645,36,51,15,36.71275216938179,1.784073253119976,28.84499612652788,-0.16821637154603802,0.17918482321640303,0.40127638757174006,6.750635463329985,0.006311609919555001,0.009524793151329002,0.006311609919555001,0.014229811454998001,0.039820520765196,0.016869211966812,0.008147714623426,0.0037869659517330003,0.002065617791854,0.00045902617596700005,1.7771796608234571,2.353958246469541,0.009605122732123,0.032017075773746004,0.07043756670224101,0.11953041622198501,0.21771611526147203,0.511205976520811,0.7556029882604051,1.022925764192139,0.33126487599778903,19.52280427642022,422.82376078444236,381,1,937,102.21522309711285,15.0,193.12603388747905,37297.6649651077,3.0,81.0,78.0,2.463005335171609,5.5066536611772605,11,1,899,85.18181818181819,2.0,257.3832721066592,66246.14876033057,1.0,7.5,6.5,2.844783898567343,6.0957042298129664,101,1,292,9.277227722772277,2.0,31.163929012921322,971.1904715223994,1.0,5.0,4.0,7.672745189703872,64.72182800579148,9.806000000000001,13.867,18.357,3.2640000000000002,6.888,1.299,0.582,6.888,3.542,2.403,5.413,4.929,4.629,195166.2442745276,0.6466967918841,247624.8365497508,0.601566424410453,120536.03113478613,0.292823733970692,154887.76808660102,0.37627599125765404,18.361,3.276,13.885
diff --git a/data/2_grid_test.csv b/data/test/2_grid_test.csv
similarity index 100%
rename from data/2_grid_test.csv
rename to data/test/2_grid_test.csv
diff --git a/data/benchmark_test_2.csv b/data/test/benchmark_test_2.csv
similarity index 100%
rename from data/benchmark_test_2.csv
rename to data/test/benchmark_test_2.csv
diff --git a/data/bpic_features.csv b/data/test/bpic_features.csv
similarity index 92%
rename from data/bpic_features.csv
rename to data/test/bpic_features.csv
index 5d196d5f25f4572a3edd9e3ed1c95948f8bd8b3e..9db58f93a7588cd458eef8658bb35e73ee7b30fa 100644
--- a/data/bpic_features.csv
+++ b/data/test/bpic_features.csv
@@ -1,4 +1,4 @@
-log,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,Log Nature,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,accumulated_time_time_min,accumulated_time_time_max,accumulated_time_time_mean,accumulated_time_time_median,accumulated_time_time_mode,accumulated_time_time_std,accumulated_time_time_variance,accumulated_time_time_q1,accumulated_time_time_q3,accumulated_time_time_iqr,accumulated_time_time_geometric_mean,accumulated_time_time_geometric_std,accumulated_time_time_harmonic_mean,accumulated_time_time_skewness,accumulated_time_time_kurtosis,accumulated_time_time_coefficient_variation,accumulated_time_time_entropy,accumulated_time_time_skewness_hist,accumulated_time_time_kurtosis_hist,execution_time_time_min,execution_time_time_max,execution_time_time_mean,execution_time_time_median,execution_time_time_mode,execution_time_time_std,execution_time_time_variance,execution_time_time_q1,execution_time_time_q3,execution_time_time_iqr,execution_time_time_geometric_mean,execution_time_time_geometric_std,execution_time_time_harmonic_mean,execution_time_time_skewness,execution_time_time_kurtosis,execution_time_time_coefficient_variation,execution_time_time_entropy,execution_time_time_skewness_hist,execution_time_time_kurtosis_hist,remaining_time_time_min,remaining_time_time_max,remaining_time_time_mean,remaining_time_time_median,remaining_time_time_mode,remaining_time_time_std,remaining_time_time_variance,remaining_time_time_q1,remaining_time_time_q3,remaining_time_time_iqr,remaining_time_time_geometric_mean,remaining_time_time_geometric_std,remaining_time_time_harmonic_mean,remaining_time_time_skewness,remaining_time_time_kurtosis,remaining_time_time_coefficient_variation,remaining_time_time_entropy,remaining_time_time_skewness_hist,remaining_time_time_kurtosis_hist,within_day_time_min,within_day_time_max,within_day_time_mean,within_day_time_median,within_day_time_mode,within_day_time_std,within_day_time_variance,within_day_time_q1,within_day_time_q3,within_day_time_iqr,within_day_time_geometric_mean,within_day_time_geometric_std,within_day_time_harmonic_mean,within_day_time_skewness,within_day_time_kurtosis,within_day_time_coefficient_variation,within_day_time_entropy,within_day_time_skewness_hist,within_day_time_kurtosis_hist
+log,n_traces,n_unique_traces,ratio_variants_per_number_of_traces,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,Log Nature,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,accumulated_time_time_min,accumulated_time_time_max,accumulated_time_time_mean,accumulated_time_time_median,accumulated_time_time_mode,accumulated_time_time_std,accumulated_time_time_variance,accumulated_time_time_q1,accumulated_time_time_q3,accumulated_time_time_iqr,accumulated_time_time_geometric_mean,accumulated_time_time_geometric_std,accumulated_time_time_harmonic_mean,accumulated_time_time_skewness,accumulated_time_time_kurtosis,accumulated_time_time_coefficient_variation,accumulated_time_time_entropy,accumulated_time_time_skewness_hist,accumulated_time_time_kurtosis_hist,execution_time_time_min,execution_time_time_max,execution_time_time_mean,execution_time_time_median,execution_time_time_mode,execution_time_time_std,execution_time_time_variance,execution_time_time_q1,execution_time_time_q3,execution_time_time_iqr,execution_time_time_geometric_mean,execution_time_time_geometric_std,execution_time_time_harmonic_mean,execution_time_time_skewness,execution_time_time_kurtosis,execution_time_time_coefficient_variation,execution_time_time_entropy,execution_time_time_skewness_hist,execution_time_time_kurtosis_hist,remaining_time_time_min,remaining_time_time_max,remaining_time_time_mean,remaining_time_time_median,remaining_time_time_mode,remaining_time_time_std,remaining_time_time_variance,remaining_time_time_q1,remaining_time_time_q3,remaining_time_time_iqr,remaining_time_time_geometric_mean,remaining_time_time_geometric_std,remaining_time_time_harmonic_mean,remaining_time_time_skewness,remaining_time_time_kurtosis,remaining_time_time_coefficient_variation,remaining_time_time_entropy,remaining_time_time_skewness_hist,remaining_time_time_kurtosis_hist,within_day_time_min,within_day_time_max,within_day_time_mean,within_day_time_median,within_day_time_mode,within_day_time_std,within_day_time_variance,within_day_time_q1,within_day_time_q3,within_day_time_iqr,within_day_time_geometric_mean,within_day_time_geometric_std,within_day_time_harmonic_mean,within_day_time_skewness,within_day_time_kurtosis,within_day_time_coefficient_variation,within_day_time_entropy,within_day_time_skewness_hist,within_day_time_kurtosis_hist
 BPIC15_2,832,828,0.9951923076923076,1,132,53.31009615384615,54.0,61,19.89497651105348,395.8100903753698,44.0,62.0,18.0,48.15011097917017,1.6953108255055442,37.583741492631816,0.0541383907866727,0.8049916722455452,0.3731934088739797,6.6467154289258925,0.0038534938344098,0.0048627422196124,0.0046792425132119,0.0239467116852613,0.0237632119788608,0.0082574867880211,0.0047709923664122,0.0013762477980035,0.0006422489724016,0.0001834997064004,0.0541383907866727,0.8049916722455452,0.0024038461538461,0.0144230769230769,0.0540865384615384,0.1033653846153846,0.203125,0.5024038461538461,0.7512019230769231,1.0048309178743962,0.0693367154319194,14.283026792978164,202.00485436893203,410,1,830,108.18048780487806,12.0,187.5881623228515,35189.31864366448,3.0,125.5,122.5,2.1294119001489484,3.808278466770415,14,1,731,59.42857142857143,1.0,186.71740078284623,34863.387755102034,1.0,8.25,7.25,3.300411469802443,8.960767075527839,82,1,216,10.146341463414634,1.0,35.31879964786925,1247.4176085663291,1.0,3.0,2.0,5.098791193232185,25.861991394282988,9.691,14.524,19.448,3.859,7.105,7.105,7.105,7.105,7.105,7.105,5.545,5.039,4.721,Real,240512.2242485009,0.6279728735030676,285876.9226982823,0.6023712370019746,150546.57168151825,0.3172166670686898,185312.93742252485,0.3904728730604407,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
 BPI_Challenge_2018,43809,28457,0.6495697231162546,24,2973,57.39154055102833,49.0,49,34.87213051501663,1216.065486656354,44.0,59.0,15.0,53.775007740790905,1.3673968195217023,51.6515023255421,26.12645867504185,1720.3996647748236,0.6076179551934296,10.59875768208314,0.0033846328873849,5.263453617722996e-06,9.28844756068764e-07,0.0,0.0,0.0,0.0,0.0,7.740372967239698e-08,7.740372967239698e-08,26.12645867504185,1720.3996647748236,0.0269807573786208,0.2903741240384396,0.3730055468054509,0.4153712707434545,0.4803350909630441,0.6752037252619325,0.837590449451026,1.53948061988263,12.487438103768865,64.62568045475237,5083.4558063165005,41,17,466141,61323.56097560976,7530.0,120522.24741658216,14525612122.343842,902.0,45907.0,45005.0,2.444006846537922,4.7732537682944125,4,2,38623,10952.25,2592.0,16111.407548302535,259577453.1875,36.5,13507.75,13471.25,1.098736017040351,-0.714799753613248,21,1,34830,2086.1428571428573,13.0,7431.744980540056,55230833.45578231,2.0,193.0,191.0,4.062386890920656,14.95282428002514,13.191,16.272,20.972,1.023,-0.01,1.855,0.511,1.403,3.572,2.001,7.849,7.371,7.067,Real,11563842.153239768,0.7120788464629594,21146257.119093828,0.5706879719331716,14140225.903138256,0.3816115919659581,15576076.832943872,0.4203618469408319,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
 Receipt_WABO_CoSeLoG,1434,116,0.0808926080892608,1,25,5.981171548117155,6.0,6,2.166128830112964,4.692114108646557,6.0,6.0,0.0,5.414708441482159,1.7049649652198722,4.356444755372117,1.276525010246869,12.296005610487518,0.3621579506100023,7.197192878385,0.0360297536029753,0.008135750813575,0.341120409112041,0.0235355648535564,0.0037773128777312,0.0017433751743375,0.0002905625290562,0.0014528126452812,0.0,0.0005811250581125,1.276525010246869,12.296005610487518,0.4972105997210599,0.4972105997210599,0.796373779637378,0.8870292887029289,0.9302649930264992,0.9595536959553695,0.9797768479776848,12.362068965517242,68.36027740401485,9.380686726353323,92.2819193173858,27,1,1434,317.6666666666667,27.0,553.3898230870318,306240.2962962963,8.0,50.0,42.0,1.342950616318748,-0.1780942423969453,1,1434,1434,1434.0,1434.0,0.0,0.0,1434.0,1434.0,0.0,,,14,1,828,102.42857142857144,6.0,225.87155461384123,51017.95918367348,1.25,33.25,32.0,2.471765166310402,4.8465409223704325,3.209,4.746,7.019,0.385,2.672,2.966,0.804,1.484,2.966,2.966,3.26,2.845,2.584,Real,2382.325855313024,0.6893625408247437,18296.27229411094,0.235532333261429,7814.867608807029,0.1006026786464005,10728.696951225804,0.1381131076951861,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/data/grid_experiments/grid_1objectives_rt10v.csv b/data/test/grid_experiments/rt10v.csv
similarity index 92%
rename from data/grid_experiments/grid_1objectives_rt10v.csv
rename to data/test/grid_experiments/rt10v.csv
index 38b8e81f2672cc214a3a4ac447edcd5c6fba648d..265675f2b83685fff82d62c4d4a50d0b5ddc085f 100644
--- a/data/grid_experiments/grid_1objectives_rt10v.csv
+++ b/data/test/grid_experiments/rt10v.csv
@@ -9,4 +9,4 @@ task_7,0.6
 task_8,0.7
 task_9,0.8
 task_10,0.9
-task_11,1.0
+task_11,1.0
\ No newline at end of file
diff --git a/data/test/grid_feat.csv b/data/test/grid_feat.csv
index cc94b2798d29d20bacd82b2e536a1840f6d45c24..d8fa4c9a9984b2a100b1a86f7f413c867072a9ea 100644
--- a/data/test/grid_feat.csv
+++ b/data/test/grid_feat.csv
@@ -1,3 +1,5 @@
 log,ratio_top_20_variants,epa_normalized_sequence_entropy_linear_forgetting
 experiment1,0.2,0.4
 experiment2,0.4,0.7
+experiment3,NaN,0.4
+experiment4,0.2,NaN
diff --git a/data/grid_objectives.csv b/data/test/grid_objectives.csv
similarity index 100%
rename from data/grid_objectives.csv
rename to data/test/grid_objectives.csv
diff --git a/data/validation/2_ense_enseef_feat.csv b/data/validation/2_ense_enseef_feat.csv
new file mode 100644
index 0000000000000000000000000000000000000000..ff76c4c2d5dcfe85b44370eaa592653d9a791789
--- /dev/null
+++ b/data/validation/2_ense_enseef_feat.csv
@@ -0,0 +1,3 @@
+epa_normalized_sequence_entropy,epa_normalized_sequence_entropy_exponential_forgetting,log
+0.617035580430171,0.25759383686118104,CoSeLoG_WABO_1
+0.547597168193871,0.22387845232743803,Sepsis_Cases_Event_Log
diff --git a/data/validation/genELexperiment1_04_02.json b/data/validation/genELexperiment1_04_02.json
new file mode 100644
index 0000000000000000000000000000000000000000..4944d0400f452913104ef73d5b89c58b6f128641
--- /dev/null
+++ b/data/validation/genELexperiment1_04_02.json
@@ -0,0 +1 @@
+{"ratio_top_20_variants": 0.20017714791851196, "epa_normalized_sequence_entropy_linear_forgetting": 0.052097205658647734, "log": "genELexperiment1_04_02", "target_similarity": 0.7418932364693804}
\ No newline at end of file
diff --git a/data/validation/genELexperiment2_07_04.json b/data/validation/genELexperiment2_07_04.json
new file mode 100644
index 0000000000000000000000000000000000000000..dd0d0d7a0936b45fb5bc938becae11a270dd8c06
--- /dev/null
+++ b/data/validation/genELexperiment2_07_04.json
@@ -0,0 +1 @@
+{"ratio_top_20_variants": 0.38863337713534823, "epa_normalized_sequence_entropy_linear_forgetting": 0.052097205658647734, "log": "genELexperiment2_07_04", "target_similarity": 0.6067951985524301}
\ No newline at end of file
diff --git a/data/validation/genELexperiment3_04_nan.json b/data/validation/genELexperiment3_04_nan.json
new file mode 100644
index 0000000000000000000000000000000000000000..d0f3c3bfd7e4dcd3ebf2c6e07016f36834c5f331
--- /dev/null
+++ b/data/validation/genELexperiment3_04_nan.json
@@ -0,0 +1 @@
+{"epa_normalized_sequence_entropy_linear_forgetting": 0.052097205658647734, "log": "genELexperiment3_04_nan", "target_similarity": 0.7418932612931086}
\ No newline at end of file
diff --git a/data/validation/genELexperiment4_nan_02.json b/data/validation/genELexperiment4_nan_02.json
new file mode 100644
index 0000000000000000000000000000000000000000..37fa897b5a82ec2281412c4bbb4447c13e381fd9
--- /dev/null
+++ b/data/validation/genELexperiment4_nan_02.json
@@ -0,0 +1 @@
+{"ratio_top_20_variants": 0.2, "log": "genELexperiment4_nan_02", "target_similarity": 1.0}
\ No newline at end of file
diff --git a/data/validation/test_benchmark.csv b/data/validation/test_benchmark.csv
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/data/validation/test_feat.csv b/data/validation/test_feat.csv
new file mode 100644
index 0000000000000000000000000000000000000000..4fc31b76a77db79690e087ecd20582c3760433a4
--- /dev/null
+++ b/data/validation/test_feat.csv
@@ -0,0 +1,3 @@
+log,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,ratio_variants_per_number_of_traces
+gen_el_168,0.13580246913580246,0.5709876543209876,0.6920749183939835,0.6241163465815115,0.06011912975523125,0.2577500062839078,0.44135802469135804
+gen_el_169,0.25813692480359146,0.6846240179573513,0.6517697077716751,0.4929433574247866,0.06332152226023505,0.21109493857555106,0.3153759820426487
diff --git a/gedi/augmentation.py b/gedi/augmentation.py
index ee45e50d17ab7f276e8566f8cfd8fa2745ada64d..bf489ee44125a455e386950522b6094e94ef14d6 100644
--- a/gedi/augmentation.py
+++ b/gedi/augmentation.py
@@ -1,8 +1,7 @@
 import pandas as pd
 from collections import Counter
 from datetime import datetime as dt
-from imblearn.over_sampling import SMOTE, SVMSMOTE, BorderlineSMOTE, KMeansSMOTE
-from sklearn.preprocessing import Normalizer
+from imblearn.over_sampling import SMOTE
 from gedi.utils.matrix_tools import insert_missing_data
 from utils.param_keys import INPUT_PATH, OUTPUT_PATH
 from utils.param_keys.augmentation import AUGMENTATION_PARAMS, NO_SAMPLES, FEATURE_SELECTION, METHOD
diff --git a/gedi/benchmark.py b/gedi/benchmark.py
index 44660405f1b535bc6683cb34e61ef6541291fd69..85a18635fc52d2601eaefc641c85ed90dc4739f0 100644
--- a/gedi/benchmark.py
+++ b/gedi/benchmark.py
@@ -5,15 +5,12 @@ import pandas as pd
 import subprocess
 
 from datetime import datetime as dt
-from functools import partial, partialmethod
+from functools import partialmethod
 from itertools import repeat
-from pathlib import Path
-from pm4py import read_xes, convert_to_bpmn, read_bpmn, convert_to_petri_net, check_soundness
+from pm4py import convert_to_bpmn, read_bpmn, convert_to_petri_net, check_soundness
 from pm4py import discover_petri_net_inductive, discover_petri_net_ilp, discover_petri_net_heuristics
-from pm4py import fitness_alignments, fitness_token_based_replay
-from pm4py import precision_alignments, precision_token_based_replay
-from pm4py.algo.evaluation.generalization import algorithm as generalization_evaluator
-from pm4py.algo.evaluation.simplicity import algorithm as simplicity_evaluator
+from pm4py import fitness_alignments
+from pm4py import precision_alignments
 from pm4py.objects.bpmn.obj import BPMN
 from pm4py.objects.log.importer.xes import importer as xes_importer
 from gedi.utils.io_helpers import dump_features_json
@@ -34,7 +31,7 @@ class BenchmarkTest:
                 event_logs = [""]
             else:
                 try:
-                    event_logs =[filename for filename in os.listdir(log_path) if filename.endswith(".xes")]
+                    event_logs =sorted([filename for filename in os.listdir(log_path) if filename.endswith(".xes")])
                 except FileNotFoundError:
                     print(f"        FAILED: Cannot find {params[INPUT_PATH]}" )
                     return
@@ -58,7 +55,7 @@ class BenchmarkTest:
                 path_to_json = path_to_json.rsplit("/",1)[0]
              df = pd.DataFrame()
              # Iterate over the files in the directory
-             for filename in os.listdir(path_to_json):
+             for filename in sorted(os.listdir(path_to_json)):
                  if filename.endswith('.json'):
                      i_path = os.path.join(path_to_json, filename)
                      with open(i_path) as f:
@@ -74,11 +71,12 @@ class BenchmarkTest:
         benchmark_results.to_csv(self.filepath, index=False)
 
         self.results = benchmark_results
+        print(benchmark_results)
         print(f"SUCCESS: BenchmarkTest took {dt.now()-start} sec for {len(params[MINERS])} miners"+\
               f" and {len(benchmark_results)} event-logs. Saved benchmark to {self.filepath}.")
         print("========================= ~ BenchmarkTest =============================")
 
-    def benchmark_wrapper(self, event_log, log_counter=0, miners=['inductive']):
+    def benchmark_wrapper(self, event_log, log_counter=0, miners=['ind']):
         dump_path = os.path.join(self.params[OUTPUT_PATH],
                                  os.path.split(self.params[INPUT_PATH])[-1])
         dump_path= os.path.join(self.params[OUTPUT_PATH],
@@ -94,22 +92,23 @@ class BenchmarkTest:
         else:
             log_name = "gen_el_"+str(log_counter)
             results = {"log": event_log}
-            
+
         for miner in miners:
             miner_cols = [f"fitness_{miner}", f"precision_{miner}", f"fscore_{miner}", f"size_{miner}", f"cfc_{miner}", f"pnsize_{miner}"]# f"generalization_{miner}",f"simplicity_{miner}"]
             start_miner = dt.now()
-            benchmark_results =  self.benchmark_discovery(results['log'],  miner, self.params)
+            benchmark_results =  [round(x, 4) for x in self.benchmark_discovery(results['log'],  miner, self.params)]
             results[f"fitness_{miner}"] = benchmark_results[0]
             results[f"precision_{miner}"] = benchmark_results[1]
-            results[f"fscore_{miner}"] = 2*(benchmark_results[0]*benchmark_results[1]/(benchmark_results[0]+ benchmark_results[1]))
-            results[f"size_{miner}"]=benchmark_results[2]
-            results[f"pnsize_{miner}"]=benchmark_results[4]
-            results[f"cfc_{miner}"]=benchmark_results[3]
+            results[f"fscore_{miner}"] = round(2*(benchmark_results[0]*benchmark_results[1]/
+                                                  (benchmark_results[0]+ benchmark_results[1])), 4)
+            results[f"size_{miner}"]= benchmark_results[2]
+            results[f"pnsize_{miner}"]= benchmark_results[4]
+            results[f"cfc_{miner}"]= benchmark_results[3]
 
         results['log'] = log_name
 
         print(f"    SUCCESS: {miner} miner for {results} took {dt.now()-start_miner} sec.")
-        dump_features_json(results, dump_path, log_name, content_type="benchmark")
+        dump_features_json(results, os.path.join(dump_path, log_name), content_type="benchmark")
         return
 
     def split_miner_wrapper(self, log_path="data/real_event_logs/BPI_Challenges/BPI_Challenge_2012.xes"):
@@ -186,6 +185,10 @@ class BenchmarkTest:
             if miner == 'imf':
                 miner = 'inductive'
                 miner_params = f', noise_threshold={NOISE_THRESHOLD}'
+            elif miner == 'ind':
+                miner = 'inductive'
+            elif miner == 'heu':
+                miner = 'heuristics'
             net, im, fm = eval(f"discover_petri_net_{miner}(log {miner_params})")
             bpmn_graph = convert_to_bpmn(net, im, fm)
         fitness = fitness_alignments(log, net, im, fm)['log_fitness']
diff --git a/gedi/features.py b/gedi/features.py
index 0a0fb1b5c19f6e10497aad2870cdd5b16c52e040..620e373f3b99453a50b0db55166ffc8dac7624a7 100644
--- a/gedi/features.py
+++ b/gedi/features.py
@@ -1,14 +1,12 @@
 import json
 import multiprocessing
-import numpy as np
 import pandas as pd
 import os
 
 from datetime import datetime as dt
 from functools import partial
 from feeed.feature_extractor import extract_features
-from pathlib import Path, PurePath
-from sklearn.impute import SimpleImputer
+from pathlib import Path
 from utils.param_keys import INPUT_PATH
 from utils.param_keys.features import FEATURE_PARAMS, FEATURE_SET
 from gedi.utils.io_helpers import dump_features_json
@@ -28,7 +26,7 @@ class EventLogFile:
         return str(os.path.join(self.root_path, self.filename))
 
 class EventLogFeatures(EventLogFile):
-    def __init__(self, filename, folder_path='data/event_log', params=None, logs=None, ft_params=None):
+    def __init__(self, filename=None, folder_path='data/event_log', params=None, logs=None, ft_params=None):
         super().__init__(filename, folder_path)
         if ft_params == None:
             self.params = None
@@ -37,7 +35,12 @@ class EventLogFeatures(EventLogFile):
         elif ft_params.get(FEATURE_PARAMS) == None:
             self.params = {FEATURE_SET: None}
         else:
+            #TODO: Replace hotfix
             self.params=ft_params.get(FEATURE_PARAMS)
+            if 'ratio_variants_per_number_of_traces' in self.params.get(FEATURE_SET):#HOTFIX
+                self.params[FEATURE_SET] = ['ratio_unique_traces_per_trace'\
+                                                if feat=='ratio_variants_per_number_of_traces'\
+                                                else feat for feat in self.params.get(FEATURE_SET)]
 
         # TODO: handle parameters in main, not in features. Move to main.py
         if ft_params[INPUT_PATH]:
@@ -50,7 +53,7 @@ class EventLogFeatures(EventLogFile):
                 # Check if directory exists, if not, create it
                 if not os.path.exists(input_path):
                     os.makedirs(input_path)
-                self.filename = os.listdir(input_path)
+                self.filename = sorted(os.listdir(input_path))
 
         try:
             start = dt.now()
@@ -85,6 +88,7 @@ class EventLogFeatures(EventLogFile):
                     self.filename = [ filename for filename in self.filename if filename.endswith(".xes")]
 
                 # TODO: only include xes logs in self.filename, otherwise it will result in less rows. Implement skip exception with warning
+                #self.extract_features_wrapper(self.filename[0], feature_set=self.params[FEATURE_SET]) #TESTING ONLY
                 try:
                     num_cores = multiprocessing.cpu_count() if len(
                         self.filename) >= multiprocessing.cpu_count() else len(self.filename)
@@ -142,6 +146,9 @@ class EventLogFeatures(EventLogFile):
             file_path = os.path.join(self.root_path, file)
             print(f"  INFO: Starting FEEED for {file_path} and {feature_set}")
             features = extract_features(file_path, feature_set)
+            #TODO: Replace hotfix
+            if features.get('ratio_unique_traces_per_trace'):#HOTFIX
+                features['ratio_variants_per_number_of_traces']=features.pop('ratio_unique_traces_per_trace')
 
         except Exception as e:
             print("ERROR: for ",file.rsplit(".", 1)[0], feature_set, "skipping and continuing with next log.")
@@ -150,6 +157,6 @@ class EventLogFeatures(EventLogFile):
 
         identifier = file.rsplit(".", 1)[0]
         print(f"  DONE: {file_path}. FEEED computed {feature_set}")
-        dump_features_json(features, self.root_path, identifier)
+        dump_features_json(features, os.path.join(self.root_path,identifier))
         return features
 
diff --git a/gedi/generator.py b/gedi/generator.py
index 788a3aadb84760b72362c1d8bb097c4b2649d800..741a3534bdf810fb2106270b58e1dc07cd4266a3 100644
--- a/gedi/generator.py
+++ b/gedi/generator.py
@@ -2,7 +2,6 @@ import multiprocessing
 import os
 import pandas as pd
 import random
-
 from ConfigSpace import Configuration, ConfigurationSpace
 from datetime import datetime as dt
 from feeed.activities import Activities as activities
@@ -20,9 +19,11 @@ from pm4py.sim import play_out
 from smac import HyperparameterOptimizationFacade, Scenario
 from utils.param_keys import OUTPUT_PATH, INPUT_PATH
 from utils.param_keys.generator import GENERATOR_PARAMS, EXPERIMENT, CONFIG_SPACE, N_TRIALS
-from gedi.utils.io_helpers import get_output_key_value_location, dump_features_json, read_csvs
-
-
+from gedi.utils.io_helpers import get_output_key_value_location, dump_features_json, compute_similarity
+from gedi.utils.io_helpers import read_csvs
+import xml.etree.ElementTree as ET
+import re
+from xml.dom import minidom
 
 """
    Parameters
@@ -72,13 +73,72 @@ def get_tasks(experiment, output_path="", reference_feature=None):
         raise FileNotFoundError(f"{experiment} not found. Please check path in filesystem.")
     return tasks, output_path
 
+
+def removeextralines(elem):
+    hasWords = re.compile("\\w")
+    for element in elem.iter():
+        if not re.search(hasWords,str(element.tail)):
+            element.tail=""
+        if not re.search(hasWords,str(element.text)):
+            element.text = ""
+
+def add_extension_before_traces(xes_file):
+    # Register the namespace
+    ET.register_namespace('', "http://www.xes-standard.org/")
+
+    # Parse the original XML
+    tree = ET.parse(xes_file)
+    root = tree.getroot()
+
+    # Add extensions
+    extensions = [
+        {'name': 'Lifecycle', 'prefix': 'lifecycle', 'uri': 'http://www.xes-standard.org/lifecycle.xesext'},
+        {'name': 'Time', 'prefix': 'time', 'uri': 'http://www.xes-standard.org/time.xesext'},
+        {'name': 'Concept', 'prefix': 'concept', 'uri': 'http://www.xes-standard.org/concept.xesext'}
+    ]
+
+    for ext in extensions:
+        extension_elem = ET.Element('extension', ext)
+        root.insert(0, extension_elem)
+
+    # Add global variables
+    globals = [
+        {
+            'scope': 'event',
+            'attributes': [
+                {'key': 'lifecycle:transition', 'value': 'complete'},
+                {'key': 'concept:name', 'value': '__INVALID__'},
+                {'key': 'time:timestamp', 'value': '1970-01-01T01:00:00.000+01:00'}
+            ]
+        },
+        {
+            'scope': 'trace',
+            'attributes': [
+                {'key': 'concept:name', 'value': '__INVALID__'}
+            ]
+        }
+    ]
+
+    for global_var in globals:
+        global_elem = ET.Element('global', {'scope': global_var['scope']})
+        for attr in global_var['attributes']:
+            string_elem = ET.SubElement(global_elem, 'string', {'key': attr['key'], 'value': attr['value']})
+        root.insert(len(extensions), global_elem)
+
+
+    # Pretty print the Xes
+    removeextralines(root)
+    xml_str = minidom.parseString(ET.tostring(root)).toprettyxml()
+    with open(xes_file, "w") as f:
+        f.write(xml_str)
+
 class GenerateEventLogs():
     # TODO: Clarify nomenclature: experiment, task, objective as in notebook (https://github.com/lmu-dbs/gedi/blob/main/notebooks/grid_objectives.ipynb)
     def __init__(self, params):
         print("=========================== Generator ==========================")
         print(f"INFO: Running with {params}")
         start = dt.now()
-        if params.get(OUTPUT_PATH) == None:
+        if params.get(OUTPUT_PATH) is None:
             self.output_path = 'data/generated'
         else:
             self.output_path = params.get(OUTPUT_PATH)
@@ -91,17 +151,21 @@ class GenerateEventLogs():
 
         self.params = params.get(GENERATOR_PARAMS)
         experiment = self.params.get(EXPERIMENT)
-        if experiment!= None:
+        if experiment is not None:
             tasks, output_path = get_tasks(experiment, self.output_path)
             self.output_path = output_path
 
+        if 'ratio_variants_per_number_of_traces' in tasks.columns:#HOTFIX
+            tasks=tasks.rename(columns={"ratio_variants_per_number_of_traces": "ratio_unique_traces_per_trace"})
+
         if tasks is not None:
+            self.feature_keys = sorted([feature for feature in tasks.columns.tolist() if feature != "log"])
             num_cores = multiprocessing.cpu_count() if len(tasks) >= multiprocessing.cpu_count() else len(tasks)
             #self.generator_wrapper([*tasks.iterrows()][0])# For testing
             with multiprocessing.Pool(num_cores) as p:
                 print(f"INFO: Generator starting at {start.strftime('%H:%M:%S')} using {num_cores} cores for {len(tasks)} tasks...")
                 random.seed(RANDOM_SEED)
-                log_config = p.map(self.generator_wrapper, tasks.iterrows())
+                log_config = p.map(self.generator_wrapper, [(index, row) for index, row in tasks.iterrows()])
             self.log_config = log_config
 
         else:
@@ -111,9 +175,14 @@ class GenerateEventLogs():
                 self.configs = [self.configs]
             temp = self.generate_optimized_log(self.configs[0])
             self.log_config = [temp]
+            #TODO: Replace hotfix
+            if self.params[EXPERIMENT].get('ratio_unique_traces_per_trace'):#HOTFIX
+                self.params[EXPERIMENT]['ratio_variants_per_number_of_traces']=self.params[EXPERIMENT].pop('ratio_unique_traces_per_trace')
+
             save_path = get_output_key_value_location(self.params[EXPERIMENT],
                                              self.output_path, "genEL")+".xes"
             write_xes(temp['log'], save_path)
+            add_extension_before_traces(save_path)
             print("SUCCESS: Saved generated event log in", save_path)
         print(f"SUCCESS: Generator took {dt.now()-start} sec. Generated {len(self.log_config)} event logs.")
         print(f"         Saved generated logs in {self.output_path}")
@@ -125,7 +194,7 @@ class GenerateEventLogs():
         except IndexError:
             identifier = task[0]+1
         task = task[1].loc[lambda x, identifier=identifier: x!=identifier]
-        self.objectives = task.to_dict()
+        self.objectives = task.dropna().to_dict()
         random.seed(RANDOM_SEED)
         self.configs = self.optimize()
 
@@ -136,14 +205,27 @@ class GenerateEventLogs():
             log_config = self.generate_optimized_log(self.configs)
 
         identifier = 'genEL'+str(identifier)
-        save_path = get_output_key_value_location(self.objectives,
-                                         self.output_path, identifier)+".xes"
+        #TODO: Replace hotfix
+        if self.objectives.get('ratio_unique_traces_per_trace'):#HOTFIX
+            self.objectives['ratio_variants_per_number_of_traces']=self.objectives.pop('ratio_unique_traces_per_trace')
+
+        save_path = get_output_key_value_location(task.to_dict(),
+                                         self.output_path, identifier, self.feature_keys)+".xes"
 
         write_xes(log_config['log'], save_path)
+        add_extension_before_traces(save_path)
         print("SUCCESS: Saved generated event log in", save_path)
         features_to_dump = log_config['metafeatures']
-        features_to_dump['log'] = identifier.replace('genEL', '')
-        dump_features_json(features_to_dump, self.output_path, identifier, objectives=self.objectives)
+
+        #TODO: Replace hotfix
+        if features_to_dump.get('ratio_unique_traces_per_trace'):#HOTFIX
+            features_to_dump['ratio_variants_per_number_of_traces']=features_to_dump.pop('ratio_unique_traces_per_trace')
+        features_to_dump['log']= os.path.split(save_path)[1].split(".")[0]
+        # calculating the manhattan distance of the generated log to the target features
+        #features_to_dump['distance_to_target'] = calculate_manhattan_distance(self.objectives, features_to_dump)
+        features_to_dump['target_similarity'] = compute_similarity(self.objectives, features_to_dump)
+        dump_features_json(features_to_dump, save_path)
+
         return log_config
 
     def generate_optimized_log(self, config):
@@ -165,9 +247,10 @@ class GenerateEventLogs():
         log = play_out(tree, parameters={"num_traces": config["num_traces"]})
 
         for i, trace in enumerate(log):
-            trace.attributes['concept:name']=str(i)
+            trace.attributes['concept:name'] = str(i)
             for j, event in enumerate(trace):
-                event['time:timestamp']=dt.now()
+                event['time:timestamp'] = dt.now()
+                event['lifecycle:transition'] = "complete"
         random.seed(RANDOM_SEED)
         metafeatures = self.compute_metafeatures(log)
         return {
@@ -203,6 +286,7 @@ class GenerateEventLogs():
             trace.attributes['concept:name'] = str(i)
             for j, event in enumerate(trace):
                 event['time:timestamp'] = dt.fromtimestamp(j * 1000)
+                event['lifecycle:transition'] = "complete"
 
         metafeatures_computation = {}
         for ft_name in self.objectives.keys():
@@ -219,7 +303,7 @@ class GenerateEventLogs():
         return log_evaluation
 
     def optimize(self):
-        if self.params.get(CONFIG_SPACE) ==  None:
+        if self.params.get(CONFIG_SPACE) is None:
             configspace = ConfigurationSpace({
                 "mode": (5, 40),
                 "sequence": (0.01, 1),
diff --git a/gedi/plotter.py b/gedi/plotter.py
index 8bc512e90346b3b31d561f4a3dbaee9c85745aab..b71a877c8d3ae9b04e9ac386a3984228c4d57b8e 100644
--- a/gedi/plotter.py
+++ b/gedi/plotter.py
@@ -12,14 +12,13 @@ from matplotlib.axes import Axes
 from matplotlib.figure import Figure
 from matplotlib.lines import Line2D
 from utils.param_keys import PLOT_TYPE, PROJECTION, EXPLAINED_VAR, PLOT_3D_MAP
-from utils.param_keys import INPUT_PATH, OUTPUT_PATH, PIPELINE_STEP
+from utils.param_keys import OUTPUT_PATH, PIPELINE_STEP
 from utils.param_keys.generator import GENERATOR_PARAMS, EXPERIMENT, PLOT_REFERENCE_FEATURE
 from utils.param_keys.plotter import REAL_EVENTLOG_PATH, FONT_SIZE, BOXPLOT_WIDTH
 from collections import defaultdict
 
 from sklearn.preprocessing import Normalizer, StandardScaler
 from sklearn.decomposition import PCA
-from sklearn.metrics.pairwise import euclidean_distances
 from gedi.generator import get_tasks
 from gedi.utils.io_helpers import get_keys_abbreviation
 from gedi.utils.io_helpers import read_csvs, select_instance
@@ -263,7 +262,7 @@ class BenchmarkPlotter:
         corr = df.corr()
 
         if mean == 'methods':
-            for method in ['inductive', 'heuristics', 'ilp']:
+            for method in ['inductive', 'heu', 'ilp']:
                 method_cols = [col for col in corr.columns if col.startswith(method)]
                 corr[method+'_avg'] = corr.loc[:, corr.columns.isin(method_cols)].mean(axis=1)
         elif mean == 'metrics':
@@ -274,7 +273,7 @@ class BenchmarkPlotter:
         avg_cols = [col for col in corr.columns if col.endswith('_avg')]
 
         benchmark_result_cols = [col for col in corr.columns if col.startswith('inductive')
-                                or col.startswith('heuristics') or col.startswith('ilp')]
+                                or col.startswith('heu') or col.startswith('ilp')]
 
         corr = corr[:][~corr.index.isin(benchmark_result_cols)]
 
@@ -298,7 +297,7 @@ class BenchmarkPlotter:
 
     def plot_miners_correlation(self, benchmark, output_path=None):
         benchmark_result_cols = [col for col in benchmark.columns if col.startswith('inductive')
-                                or col.startswith('heuristics') or col.startswith('ilp')]
+                                or col.startswith('heu') or col.startswith('ilp')]
         df = benchmark.loc[:, benchmark.columns!='log']
         df = df.loc[:, df.columns.isin(benchmark_result_cols)]
 
@@ -985,10 +984,10 @@ class GenerationPlotter(object):
             print(e)
         ratio_most_common_variant = 2.021278 / 11.0
         ratio_top_10_variants = 0.07378 / 11.0
-        ratio_unique_traces_per_trace = 0.016658 / 11.0
+        ratio_variants_per_number_of_traces = 0.016658 / 11.0
         result_df['ratio_most_common_variant']['ratio_most_common_variant'] = ratio_most_common_variant
         result_df['ratio_top_10_variants']['ratio_top_10_variants'] = ratio_top_10_variants
-        result_df['ratio_unique_traces_per_trace']['ratio_unique_traces_per_trace'] = ratio_unique_traces_per_trace
+        result_df['ratio_variants_per_number_of_traces']['ratio_variants_per_number_of_traces'] = ratio_variants_per_number_of_traces
 
         abbrvs_key = get_keys_abbreviation(keys)
         result_df.columns = abbrvs_key.split("_")
diff --git a/gedi/utils/array_tools.py b/gedi/utils/array_tools.py
deleted file mode 100644
index acf212d78288d5620f1266a2fac974c46d4c63f8..0000000000000000000000000000000000000000
--- a/gedi/utils/array_tools.py
+++ /dev/null
@@ -1,57 +0,0 @@
-import numpy as np
-
-
-def split_list_in_half(array):
-    """
-    https://stackoverflow.com/questions/752308/split-list-into-smaller-lists-split-in-half
-    :param array: array (list, ndarray)
-    :return: tuple with the first and the second half of the list
-    """
-    half = len(array) // 2
-    return array[:half], array[half:]
-
-
-def rescale_center(symmetrical_array, stat_func: callable = np.median):
-    """
-    Rescales the data in the array center, and eliminates all the other values on the sides (setting to 0)
-    :param symmetrical_array: (nd)array symmetrical
-    :param stat_func: Some statistical function of an array: np.median (default), np.mean, ...
-    :return:
-    """
-    symmetrical_array = rescale_array(symmetrical_array, stat_func)
-    return extinct_side_values(symmetrical_array)
-
-
-def rescale_array(array, stat_func: callable = np.median, interp_range=None, lower_bound=None):
-    """
-    Rescale an array from a range, of its statistical value (mean, median, min, ...) to the maximum,
-    into a new range `interp_range` (default: 0-1)
-    :param array:
-    :param stat_func: Some statistical function of an array: np.median (default), np.mean, ...
-    :param interp_range: the new range for the interpolation
-    :param lower_bound: 
-    :return: the new interpolated array
-    """
-    if interp_range is None:
-        interp_range = [0, 1]
-
-    if lower_bound is None:
-        lower_bound = stat_func(array)
-
-    return np.interp(array, [lower_bound, array.max()], interp_range)
-
-
-def extinct_side_values(symmetrical_array, smaller_than=0):
-    """
-    Takes an array and searches the first value from the center smaller than the given value.
-    All the border values from that criteria are zeroed
-    :param symmetrical_array: symmetrical (nd)array
-    :param smaller_than: int
-        Sets the criteria to find the index between the center and the border.
-    :return:
-    """
-    right_i = np.argmax(split_list_in_half(symmetrical_array)[1] <= smaller_than)
-    center_i = len(symmetrical_array) // 2
-    new_y = np.zeros_like(symmetrical_array)
-    new_y[center_i - right_i:center_i + right_i] = symmetrical_array[center_i - right_i:center_i + right_i]
-    return new_y
diff --git a/execute_grid_experiments.py b/gedi/utils/execute_grid_experiments.py
similarity index 76%
rename from execute_grid_experiments.py
rename to gedi/utils/execute_grid_experiments.py
index ca85e3271036f4bfe889706322e4399c7e6797b4..0de606f63b91163e1713ee4b4afc83d2ab24cd1d 100644
--- a/execute_grid_experiments.py
+++ b/gedi/utils/execute_grid_experiments.py
@@ -1,5 +1,6 @@
 import multiprocessing
 import os
+import sys
 
 from datetime import datetime as dt
 from gedi.utils.io_helpers import sort_files
@@ -9,19 +10,22 @@ from tqdm import tqdm
 def multi_experiment_wrapper(config_file, i=0):
     print(f"=========================STARTING EXPERIMENT #{i+1}=======================")
     print(f"INFO: Executing with {config_file}")
-    os.system(f"python -W ignore main.py -o config_files/options/baseline.json -a {config_file}")
+    os.system(f"python -W ignore main.py -a {config_file}")
     print(f"=========================FINISHED EXPERIMENT #{i+1}=======================")
 
 if __name__ == '__main__':
-    EXPERIMENTS_FOLDER = os.path.join('config_files','algorithm','34_bpic_features')
-    EXPERIMENTS_FOLDER = os.path.join('config_files','algorithm','grid_1obj')
-    EXPERIMENTS_FOLDER = os.path.join('config_files','algorithm','grid_experiments')
-    EXPERIMENTS_FOLDER = os.path.join('config_files','algorithm','test')
+    EXPERIMENTS_FOLDER = sys.argv[1]
+    """
+    Following args run the following experiments:
+    - config_files/algorithm/grid_1obj
+    - config_files/algorithm/grid_experiments
+    - config_files/algorithm/test
+    """
     start = dt.now()
 
     experiment_list = list(tqdm(sort_files(os.listdir(EXPERIMENTS_FOLDER))))
     experiment_list = [os.path.join(EXPERIMENTS_FOLDER, config_file) for config_file in experiment_list]
-    experiment_list = experiment_list[:10]
+    #experiment_list = experiment_list[:10]
 
     print(f"========================STARTING MULTIPLE EXPERIMENTS=========================")
     print(f"INFO: {EXPERIMENTS_FOLDER} contains config files for {len(experiment_list)}.")
diff --git a/gedi/utils/iGEDI_interface.png b/gedi/utils/iGEDI_interface.png
new file mode 100644
index 0000000000000000000000000000000000000000..f097edc07658e784df127e52d1d961fab22fbec7
Binary files /dev/null and b/gedi/utils/iGEDI_interface.png differ
diff --git a/gedi/utils/io_helpers.py b/gedi/utils/io_helpers.py
index 4d90afa253baed17509ffb251d9a866f7f5760ab..2968d64eb3353f60c3d4e95127bb666df23bd0b6 100644
--- a/gedi/utils/io_helpers.py
+++ b/gedi/utils/io_helpers.py
@@ -4,9 +4,10 @@ import os
 import pandas as pd
 import re
 import shutil
-
+import numpy as np
 from collections import defaultdict
-from pathlib import Path, PurePath
+from pathlib import PurePath
+from scipy.spatial.distance import euclidean
 
 def select_instance(source_dir, log_path, destination=os.path.join("output","generated","instance_selection")):
     os.makedirs(destination, exist_ok=True)
@@ -52,30 +53,62 @@ def get_keys_abbreviation(obj_keys):
         abbreviated_keys.append(abbreviated_key)
     return '_'.join(abbreviated_keys)
 
-def get_output_key_value_location(obj, output_path, identifier):
+def get_output_key_value_location(obj, output_path, identifier, obj_keys=None):
     obj_sorted = dict(sorted(obj.items()))
-    obj_keys = [*obj_sorted.keys()]
-    folder_path = os.path.join(output_path, f"{len(obj_keys)}_{get_keys_abbreviation(obj_keys)}")
+    if obj_keys is None:
+        obj_keys = [*obj_sorted.keys()]
 
     obj_values = [round(x, 4) for x in [*obj_sorted.values()]]
-    obj_values_joined = '_'.join(map(str, obj_values)).replace('.', '')
-    generated_file_name = f"{identifier}_{obj_values_joined}"
+
+    if len(obj_keys) > 7:
+        folder_path = os.path.join(output_path, f"{len(obj_keys)}_features")
+        generated_file_name = f"{identifier}"
+    else:
+        folder_path = os.path.join(output_path, f"{len(obj_keys)}_{get_keys_abbreviation(obj_keys)}")
+        obj_values_joined = '_'.join(map(str, obj_values)).replace('.', '')
+        generated_file_name = f"{identifier}_{obj_values_joined}"
+
 
     os.makedirs(folder_path, exist_ok=True)
     save_path = os.path.join(folder_path, generated_file_name)
     return save_path
 
-def dump_features_json(features: dict, output_path, identifier, objectives=None, content_type="features"):
-    output_parts = PurePath(output_path).parts
-    feature_dir = os.path.join(output_parts[0], content_type,
+def dump_features_json(features: dict, output_path, content_type="features"):
+    output_parts = PurePath(output_path.split(".xes")[0]).parts
+    features_path = os.path.join(output_parts[0], content_type,
                                    *output_parts[1:])
-    if objectives is not None:
-        json_path = get_output_key_value_location(objectives,
-                                                feature_dir, identifier)+".json"
-    else:
-        json_path = os.path.join(feature_dir, identifier)+".json"
+    json_path = features_path+'.json'
 
     os.makedirs(os.path.split(json_path)[0], exist_ok=True)
     with open(json_path, 'w') as fp:
         json.dump(features, fp, default=int)
         print(f"SUCCESS: Saved {len(features)-1} {content_type} in {json_path}")#-1 because 'log' is not a feature
+
+def compute_similarity(v1, v2):
+
+    # Convert all values to float except for the value for the key "Log"
+    v1 = {k: (float(v) if k != "log" else v) for k, v in v1.items()}
+    v2 = {k: (float(v) if k != "log" else v) for k, v in v2.items()}
+
+    # HOTFIX: Rename 'ratio_unique_traces_per_trace'
+    if 'ratio_unique_traces_per_trace' in v1:
+        v1['ratio_variants_per_number_of_traces'] = v1.pop('ratio_unique_traces_per_trace')
+
+    # Filter out non-numeric values and ensure the same keys exist in both dictionaries
+    common_keys = set(v1.keys()).intersection(set(v2.keys()))
+    numeric_keys = [k for k in common_keys if isinstance(v1[k], (int, float)) and isinstance(v2[k], (int, float))]
+
+    # Create vectors from the filtered keys
+    vec1 = np.array([v1[k] for k in numeric_keys])
+    vec2 = np.array([v2[k] for k in numeric_keys])
+
+    if len(vec1) == 0 or len(vec2) == 0:
+        print("[ERROR]: No common numeric keys found for (Edit) Distance calculation.")
+        return None
+
+    else:
+        # Calculate Euclidean Similarity
+        target_similarity = 1 / (1 + euclidean(vec1, vec2))
+        # print("VECTORS: ", vec1, vec2, target_similarity)
+
+        return target_similarity
diff --git a/gedi/utils/logo.png b/gedi/utils/logo.png
new file mode 100644
index 0000000000000000000000000000000000000000..32bb08e6dff8b610f6f3fd78431def9e96b7fa48
Binary files /dev/null and b/gedi/utils/logo.png differ
diff --git a/main.py b/main.py
index 8844ed02817956e17d09289cee9fd004419a9b74..fc9f9c295686fb1a674f7e972eb04fa044b1f125 100644
--- a/main.py
+++ b/main.py
@@ -8,7 +8,6 @@ from gedi.benchmark import BenchmarkTest
 from gedi.plotter import BenchmarkPlotter, FeaturesPlotter, AugmentationPlotter, GenerationPlotter
 from utils.default_argparse import ArgParser
 from utils.param_keys import *
-from utils.param_keys.run_options import *
 
 def run(kwargs:dict, model_paramas_list: list, filename_list:list):
     """
@@ -22,37 +21,26 @@ def run(kwargs:dict, model_paramas_list: list, filename_list:list):
     @return:
     """
     params = kwargs[PARAMS]
-    run_option = params[RUN_OPTION]
     ft = EventLogFeatures(None)
     augmented_ft = InstanceAugmentator()
     gen = pd.DataFrame(columns=['log'])
 
-    if run_option == BASELINE:
-        for model_params in model_params_list:
-            if model_params.get(PIPELINE_STEP) == 'instance_augmentation':
-                augmented_ft = InstanceAugmentator(aug_params=model_params, samples=ft.feat)
-                AugmentationPlotter(augmented_ft, model_params)
-            elif model_params.get(PIPELINE_STEP) == 'event_logs_generation':
-                gen = pd.DataFrame(GenerateEventLogs(model_params).log_config)
-                #gen = pd.read_csv("output/features/generated/grid_2objectives_enseef_enve/2_enseef_enve_feat.csv")
-                GenerationPlotter(gen, model_params, output_path="output/plots")
-            elif model_params.get(PIPELINE_STEP) == 'benchmark_test':
-                benchmark = BenchmarkTest(model_params, event_logs=gen['log'])
-                # BenchmarkPlotter(benchmark.features, output_path="output/plots")
-            elif model_params.get(PIPELINE_STEP) == 'feature_extraction':
-                ft = EventLogFeatures(**kwargs, logs=gen['log'], ft_params=model_params)
-                FeaturesPlotter(ft.feat, model_params)
-            elif model_params.get(PIPELINE_STEP) == "evaluation_plotter":
-                GenerationPlotter(gen, model_params, output_path=model_params['output_path'], input_path=model_params['input_path'])
-
-    elif run_option == COMPARE:
-        if params[N_COMPONENTS] != 2:
-            raise ValueError(f'The parameter `{N_COMPONENTS}` has to be 2, but it\'s {params[N_COMPONENTS]}.')
-        ft = EventLogFeatures(**kwargs)
-        FeatureAnalyser(ft, params).compare(model_params_list)
-    else:
-        raise InvalidRunningOptionError(f'The run_option: `{run_option}` in the (json) configuration '
-                                        f'does not exists or it is not a loading option.\n')
+    for model_params in model_params_list:
+        if model_params.get(PIPELINE_STEP) == 'instance_augmentation':
+            augmented_ft = InstanceAugmentator(aug_params=model_params, samples=ft.feat)
+            AugmentationPlotter(augmented_ft, model_params)
+        elif model_params.get(PIPELINE_STEP) == 'event_logs_generation':
+            gen = pd.DataFrame(GenerateEventLogs(model_params).log_config)
+            #gen = pd.read_csv("output/features/generated/grid_2objectives_enseef_enve/2_enseef_enve_feat.csv")
+            #GenerationPlotter(gen, model_params, output_path="output/plots")
+        elif model_params.get(PIPELINE_STEP) == 'benchmark_test':
+            benchmark = BenchmarkTest(model_params, event_logs=gen['log'])
+            # BenchmarkPlotter(benchmark.features, output_path="output/plots")
+        elif model_params.get(PIPELINE_STEP) == 'feature_extraction':
+            ft = EventLogFeatures(**kwargs, logs=gen['log'], ft_params=model_params)
+            FeaturesPlotter(ft.feat, model_params)
+        elif model_params.get(PIPELINE_STEP) == "evaluation_plotter":
+            GenerationPlotter(gen, model_params, output_path=model_params['output_path'], input_path=model_params['input_path'])
 
 
 if __name__=='__main__':
@@ -60,13 +48,7 @@ if __name__=='__main__':
     print(f'INFO: GEDI starting {start_gedi}')
 
     args = ArgParser().parse('GEDI main')
-    run_params = config.get_run_params(args.run_params_json)
-    filename_list, kwargs = config.get_files_and_kwargs(run_params)
-
-    if args.result_load_files is None:
-        model_params_list = config.get_model_params_list(args.alg_params_json)
-        run(kwargs, model_params_list, filename_list)
-    else:
-        load(args.result_load_files, kwargs)
+    model_params_list = config.get_model_params_list(args.alg_params_json)
+    run({'params':""}, model_params_list, [])
 
     print(f'SUCCESS: GEDI took {dt.now()-start_gedi} sec.')
diff --git a/notebooks/experiment_generator.ipynb b/notebooks/experiment_generator.ipynb
deleted file mode 100644
index 937c9c2317bbf95fe0549842df30dab60e57f4d8..0000000000000000000000000000000000000000
--- a/notebooks/experiment_generator.ipynb
+++ /dev/null
@@ -1,3110 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "08ee6ee0",
-   "metadata": {},
-   "source": [
-    "## Grid Objectives\n",
-    "Iterating between min and max for each column\n",
-    "\n",
-    "### Glossary\n",
-    "- **task**: Refers to the set of values (row) and corresponding keys to be aimed at sequentially.\n",
-    "- **objective**: Refers to one key (column) and respective value to be aimed at simultaneously during a task.\n",
-    "- **experiment**: Refers to one file containing a multiple of objectives and tasks for a fixed number of each, respectively. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "e5aa7223",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import itertools\n",
-    "import json\n",
-    "import numpy as np\n",
-    "import os\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "472fd031",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Features between 0 and 1: \n",
-    "normalized_feature_names = ['ratio_unique_traces_per_trace', 'trace_len_hist1', 'trace_len_hist2',\n",
-    "                            'trace_len_hist3', 'trace_len_hist4', 'trace_len_hist5', 'trace_len_hist7',\n",
-    "                            'trace_len_hist8', 'trace_len_hist9', 'ratio_most_common_variant', \n",
-    "                            'ratio_top_1_variants', 'ratio_top_5_variants', 'ratio_top_10_variants', \n",
-    "                            'ratio_top_20_variants', 'ratio_top_50_variants', 'ratio_top_75_variants', \n",
-    "                            'epa_normalized_variant_entropy', 'epa_normalized_sequence_entropy', \n",
-    "                            'epa_normalized_sequence_entropy_linear_forgetting', 'epa_normalized_sequence_entropy_exponential_forgetting']\n",
-    "\n",
-    "normalized_feature_names = ['ratio_unique_traces_per_trace', 'ratio_most_common_variant', \n",
-    "                            'ratio_top_10_variants', 'epa_normalized_variant_entropy', 'epa_normalized_sequence_entropy', \n",
-    "                            'epa_normalized_sequence_entropy_linear_forgetting', 'epa_normalized_sequence_entropy_exponential_forgetting']\n",
-    "\n",
-    "def abbrev_obj_keys(obj_keys):\n",
-    "    abbreviated_keys = []\n",
-    "    for obj_key in obj_keys:\n",
-    "        key_slices = obj_key.split(\"_\")\n",
-    "        chars = []\n",
-    "        for key_slice in key_slices:\n",
-    "            for idx, single_char in enumerate(key_slice):\n",
-    "                if idx == 0 or single_char.isdigit():\n",
-    "                    chars.append(single_char)\n",
-    "        abbreviated_key = ''.join(chars)\n",
-    "        abbreviated_keys.append(abbreviated_key)\n",
-    "    return '_'.join(abbreviated_keys) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "2be119c8",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "21 [('epa_normalized_sequence_entropy_linear_forgetting', 'ratio_top_10_variants'), ('epa_normalized_sequence_entropy_exponential_forgetting', 'ratio_unique_traces_per_trace'), ('ratio_top_10_variants', 'ratio_unique_traces_per_trace'), ('epa_normalized_sequence_entropy', 'ratio_most_common_variant'), ('ratio_most_common_variant', 'ratio_top_10_variants'), ('epa_normalized_sequence_entropy', 'epa_normalized_sequence_entropy_linear_forgetting'), ('epa_normalized_sequence_entropy', 'epa_normalized_variant_entropy'), ('epa_normalized_sequence_entropy_exponential_forgetting', 'ratio_most_common_variant'), ('epa_normalized_variant_entropy', 'ratio_top_10_variants'), ('epa_normalized_sequence_entropy_exponential_forgetting', 'epa_normalized_sequence_entropy_linear_forgetting'), ('epa_normalized_sequence_entropy_exponential_forgetting', 'epa_normalized_variant_entropy'), ('epa_normalized_sequence_entropy_linear_forgetting', 'ratio_unique_traces_per_trace'), ('epa_normalized_sequence_entropy', 'ratio_top_10_variants'), ('ratio_most_common_variant', 'ratio_unique_traces_per_trace'), ('epa_normalized_sequence_entropy_linear_forgetting', 'ratio_most_common_variant'), ('epa_normalized_sequence_entropy_exponential_forgetting', 'ratio_top_10_variants'), ('epa_normalized_sequence_entropy_linear_forgetting', 'epa_normalized_variant_entropy'), ('epa_normalized_variant_entropy', 'ratio_unique_traces_per_trace'), ('epa_normalized_variant_entropy', 'ratio_most_common_variant'), ('epa_normalized_sequence_entropy', 'epa_normalized_sequence_entropy_exponential_forgetting'), ('epa_normalized_sequence_entropy', 'ratio_unique_traces_per_trace')]\n",
-      "121\n",
-      "Saved experiment in ../data/grid_2obj/grid_2objectives_enself_rt10v.csv\n",
-      "Saved experiment config in ../config_files/algorithm/grid_2obj/generator_grid_2objectives_enself_rt10v.json\n",
-      "Saved experiment in ../data/grid_2obj/grid_2objectives_enseef_rutpt.csv\n",
-      "Saved experiment config in ../config_files/algorithm/grid_2obj/generator_grid_2objectives_enseef_rutpt.json\n",
-      "Saved experiment in ../data/grid_2obj/grid_2objectives_rt10v_rutpt.csv\n",
-      "Saved experiment config in ../config_files/algorithm/grid_2obj/generator_grid_2objectives_rt10v_rutpt.json\n",
-      "Saved experiment in ../data/grid_2obj/grid_2objectives_ense_rmcv.csv\n",
-      "Saved experiment config in ../config_files/algorithm/grid_2obj/generator_grid_2objectives_ense_rmcv.json\n",
-      "Saved experiment in ../data/grid_2obj/grid_2objectives_rmcv_rt10v.csv\n",
-      "Saved experiment config in ../config_files/algorithm/grid_2obj/generator_grid_2objectives_rmcv_rt10v.json\n",
-      "Saved experiment in ../data/grid_2obj/grid_2objectives_ense_enself.csv\n",
-      "Saved experiment config in ../config_files/algorithm/grid_2obj/generator_grid_2objectives_ense_enself.json\n",
-      "Saved experiment in ../data/grid_2obj/grid_2objectives_ense_enve.csv\n",
-      "Saved experiment config in ../config_files/algorithm/grid_2obj/generator_grid_2objectives_ense_enve.json\n",
-      "Saved experiment in ../data/grid_2obj/grid_2objectives_enseef_rmcv.csv\n",
-      "Saved experiment config in ../config_files/algorithm/grid_2obj/generator_grid_2objectives_enseef_rmcv.json\n",
-      "Saved experiment in ../data/grid_2obj/grid_2objectives_enve_rt10v.csv\n",
-      "Saved experiment config in ../config_files/algorithm/grid_2obj/generator_grid_2objectives_enve_rt10v.json\n",
-      "Saved experiment in ../data/grid_2obj/grid_2objectives_enseef_enself.csv\n",
-      "Saved experiment config in ../config_files/algorithm/grid_2obj/generator_grid_2objectives_enseef_enself.json\n",
-      "Saved experiment in ../data/grid_2obj/grid_2objectives_enseef_enve.csv\n",
-      "Saved experiment config in ../config_files/algorithm/grid_2obj/generator_grid_2objectives_enseef_enve.json\n",
-      "Saved experiment in ../data/grid_2obj/grid_2objectives_enself_rutpt.csv\n",
-      "Saved experiment config in ../config_files/algorithm/grid_2obj/generator_grid_2objectives_enself_rutpt.json\n",
-      "Saved experiment in ../data/grid_2obj/grid_2objectives_ense_rt10v.csv\n",
-      "Saved experiment config in ../config_files/algorithm/grid_2obj/generator_grid_2objectives_ense_rt10v.json\n",
-      "Saved experiment in ../data/grid_2obj/grid_2objectives_rmcv_rutpt.csv\n",
-      "Saved experiment config in ../config_files/algorithm/grid_2obj/generator_grid_2objectives_rmcv_rutpt.json\n",
-      "Saved experiment in ../data/grid_2obj/grid_2objectives_enself_rmcv.csv\n",
-      "Saved experiment config in ../config_files/algorithm/grid_2obj/generator_grid_2objectives_enself_rmcv.json\n",
-      "Saved experiment in ../data/grid_2obj/grid_2objectives_enseef_rt10v.csv\n",
-      "Saved experiment config in ../config_files/algorithm/grid_2obj/generator_grid_2objectives_enseef_rt10v.json\n",
-      "Saved experiment in ../data/grid_2obj/grid_2objectives_enself_enve.csv\n",
-      "Saved experiment config in ../config_files/algorithm/grid_2obj/generator_grid_2objectives_enself_enve.json\n",
-      "Saved experiment in ../data/grid_2obj/grid_2objectives_enve_rutpt.csv\n",
-      "Saved experiment config in ../config_files/algorithm/grid_2obj/generator_grid_2objectives_enve_rutpt.json\n",
-      "Saved experiment in ../data/grid_2obj/grid_2objectives_enve_rmcv.csv\n",
-      "Saved experiment config in ../config_files/algorithm/grid_2obj/generator_grid_2objectives_enve_rmcv.json\n",
-      "Saved experiment in ../data/grid_2obj/grid_2objectives_ense_enseef.csv\n",
-      "Saved experiment config in ../config_files/algorithm/grid_2obj/generator_grid_2objectives_ense_enseef.json\n",
-      "Saved experiment in ../data/grid_2obj/grid_2objectives_ense_rutpt.csv\n",
-      "Saved experiment config in ../config_files/algorithm/grid_2obj/generator_grid_2objectives_ense_rutpt.json\n",
-      "None\n"
-     ]
-    }
-   ],
-   "source": [
-    "def write_generator_experiment(experiment_path, objectives=[\"ratio_top_20_variants\", \"epa_normalized_sequence_entropy_linear_forgetting\"]):\n",
-    "    first_dir = os.path.split(experiment_path[3:])[-1].replace(\".csv\",\"\")\n",
-    "    second_dir = first_dir.replace(\"grid_\",\"\").replace(\"objectives\",\"\")\n",
-    "\n",
-    "    experiment = [\n",
-    "      {\n",
-    "        'pipeline_step': 'event_logs_generation',\n",
-    "        'output_path':'output/generated/grid_2obj',\n",
-    "        'generator_params': {\n",
-    "          \"experiment\": {\"input_path\": experiment_path[3:],\n",
-    "            \"objectives\": objectives},\n",
-    "          'config_space': {\n",
-    "            'mode': [5, 20],\n",
-    "            'sequence': [0.01, 1],\n",
-    "            'choice': [0.01, 1],\n",
-    "            'parallel': [0.01, 1],\n",
-    "            'loop': [0.01, 1],\n",
-    "            'silent': [0.01, 1],\n",
-    "            'lt_dependency': [0.01, 1],\n",
-    "            'num_traces': [10, 10001],\n",
-    "            'duplicate': [0],\n",
-    "            'or': [0]\n",
-    "          },\n",
-    "          'n_trials': 200\n",
-    "        }\n",
-    "      },\n",
-    "      {\n",
-    "        'pipeline_step': 'feature_extraction',\n",
-    "        'input_path': os.path.join('output','features', 'generated', 'grid_2obj', first_dir, second_dir),\n",
-    "        'feature_params': {'feature_set':['simple_stats', 'trace_length', 'trace_variant', 'activities', 'start_activities', 'end_activities', 'eventropies', 'epa_based']},\n",
-    "        'output_path': 'output/plots',\n",
-    "        'real_eventlog_path': 'data/34_bpic_features.csv',\n",
-    "        'plot_type': 'boxplot'\n",
-    "  }\n",
-    "    ]\n",
-    "\n",
-    "    #print(\"EXPERIMENT:\", experiment[1]['input_path'])\n",
-    "    output_path = os.path.join('..', 'config_files','algorithm','grid_2obj')\n",
-    "    os.makedirs(output_path, exist_ok=True)\n",
-    "    output_path = os.path.join(output_path, f'generator_{os.path.split(experiment_path)[-1].split(\".\")[0]}.json') \n",
-    "    with open(output_path, 'w') as f:\n",
-    "        json.dump(experiment, f, ensure_ascii=False)\n",
-    "    print(f\"Saved experiment config in {output_path}\")\n",
-    "    \n",
-    "    return experiment\n",
-    "\n",
-    "def create_objectives_grid(objectives, n_para_obj=2):\n",
-    "    parameters_o = \"objectives, \"\n",
-    "    if n_para_obj==1:\n",
-    "        experiments = [[exp] for exp in objectives]\n",
-    "    else:\n",
-    "        experiments = eval(f\"[exp for exp in list(itertools.product({(parameters_o*n_para_obj)[:-2]})) if exp[0]!=exp[1]]\")\n",
-    "    experiments = list(set([tuple(sorted(exp)) for exp in experiments]))\n",
-    "    print(len(experiments), experiments)\n",
-    "    \n",
-    "    parameters = \"np.around(np.arange(0, 1.1,0.1),2), \"\n",
-    "    tasks = eval(f\"list(itertools.product({(parameters*n_para_obj)[:-2]}))\")\n",
-    "    tasks = [(f'task_{i+1}',)+task for i, task in enumerate(tasks)]\n",
-    "    print(len(tasks))\n",
-    "    for exp in experiments:\n",
-    "        df = pd.DataFrame(data=tasks, columns=[\"task\", *exp])\n",
-    "        experiment_path = os.path.join('..','data', 'grid_2obj')\n",
-    "        os.makedirs(experiment_path, exist_ok=True)\n",
-    "        experiment_path = os.path.join(experiment_path, f\"grid_{len(df.columns)-1}objectives_{abbrev_obj_keys(exp)}.csv\") \n",
-    "        df.to_csv(experiment_path, index=False)\n",
-    "        print(f\"Saved experiment in {experiment_path}\")\n",
-    "        write_generator_experiment(experiment_path, objectives=exp)\n",
-    "    #df.to_csv(f\"../data/grid_{}objectives_{abbrev_obj_keys(objectives.tolist())}.csv\" ,index=False)\n",
-    "        \n",
-    "exp_test = create_objectives_grid(normalized_feature_names, n_para_obj=2)        \n",
-    "print(exp_test)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "56ab613b",
-   "metadata": {},
-   "source": [
-    "### Helper prototypes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "dfd1a302",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = pd.DataFrame(columns=[\"log\",\"ratio_top_20_variants\", \"epa_normalized_sequence_entropy_linear_forgetting\"])    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "218946b7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "k=0\n",
-    "for i in np.arange(0, 1.1,0.2):\n",
-    "    for j in np.arange(0,0.55,0.1):\n",
-    "        k+=1\n",
-    "        new_entry = pd.Series({'log':f\"objective_{k}\", \"ratio_top_20_variants\":round(i,1),\n",
-    "                   \"epa_normalized_sequence_entropy_linear_forgetting\":round(j,1)})\n",
-    "        df = pd.concat([\n",
-    "                df, \n",
-    "                pd.DataFrame([new_entry], columns=new_entry.index)]\n",
-    "           ).reset_index(drop=True)\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "b1e3bb5a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.to_csv(\"../data/grid_objectives.csv\" ,index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "5de45389",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>log</th>\n",
-       "      <th>ratio_top_20_variants</th>\n",
-       "      <th>normalized_sequence_entropy_linear_forgetting</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>objective_1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>objective_2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>objective_3</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>objective_4</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>objective_5</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>objective_6</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>objective_7</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>objective_8</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>0.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>objective_9</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>0.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>objective_10</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>0.3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>objective_11</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>0.4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>objective_12</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>0.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>objective_13</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>objective_14</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>0.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>objective_15</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>0.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>objective_16</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>0.3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>objective_17</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>0.4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>objective_18</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>0.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>objective_19</td>\n",
-       "      <td>0.6</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>objective_20</td>\n",
-       "      <td>0.6</td>\n",
-       "      <td>0.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>objective_21</td>\n",
-       "      <td>0.6</td>\n",
-       "      <td>0.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>objective_22</td>\n",
-       "      <td>0.6</td>\n",
-       "      <td>0.3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>objective_23</td>\n",
-       "      <td>0.6</td>\n",
-       "      <td>0.4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>objective_24</td>\n",
-       "      <td>0.6</td>\n",
-       "      <td>0.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>objective_25</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>objective_26</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>objective_27</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>objective_28</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>objective_29</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>objective_30</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>0.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>objective_31</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>objective_32</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>objective_33</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33</th>\n",
-       "      <td>objective_34</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34</th>\n",
-       "      <td>objective_35</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>35</th>\n",
-       "      <td>objective_36</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.5</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             log  ratio_top_20_variants   \n",
-       "0    objective_1                    0.0  \\\n",
-       "1    objective_2                    0.0   \n",
-       "2    objective_3                    0.0   \n",
-       "3    objective_4                    0.0   \n",
-       "4    objective_5                    0.0   \n",
-       "5    objective_6                    0.0   \n",
-       "6    objective_7                    0.2   \n",
-       "7    objective_8                    0.2   \n",
-       "8    objective_9                    0.2   \n",
-       "9   objective_10                    0.2   \n",
-       "10  objective_11                    0.2   \n",
-       "11  objective_12                    0.2   \n",
-       "12  objective_13                    0.4   \n",
-       "13  objective_14                    0.4   \n",
-       "14  objective_15                    0.4   \n",
-       "15  objective_16                    0.4   \n",
-       "16  objective_17                    0.4   \n",
-       "17  objective_18                    0.4   \n",
-       "18  objective_19                    0.6   \n",
-       "19  objective_20                    0.6   \n",
-       "20  objective_21                    0.6   \n",
-       "21  objective_22                    0.6   \n",
-       "22  objective_23                    0.6   \n",
-       "23  objective_24                    0.6   \n",
-       "24  objective_25                    0.8   \n",
-       "25  objective_26                    0.8   \n",
-       "26  objective_27                    0.8   \n",
-       "27  objective_28                    0.8   \n",
-       "28  objective_29                    0.8   \n",
-       "29  objective_30                    0.8   \n",
-       "30  objective_31                    1.0   \n",
-       "31  objective_32                    1.0   \n",
-       "32  objective_33                    1.0   \n",
-       "33  objective_34                    1.0   \n",
-       "34  objective_35                    1.0   \n",
-       "35  objective_36                    1.0   \n",
-       "\n",
-       "    normalized_sequence_entropy_linear_forgetting  \n",
-       "0                                             0.0  \n",
-       "1                                             0.1  \n",
-       "2                                             0.2  \n",
-       "3                                             0.3  \n",
-       "4                                             0.4  \n",
-       "5                                             0.5  \n",
-       "6                                             0.0  \n",
-       "7                                             0.1  \n",
-       "8                                             0.2  \n",
-       "9                                             0.3  \n",
-       "10                                            0.4  \n",
-       "11                                            0.5  \n",
-       "12                                            0.0  \n",
-       "13                                            0.1  \n",
-       "14                                            0.2  \n",
-       "15                                            0.3  \n",
-       "16                                            0.4  \n",
-       "17                                            0.5  \n",
-       "18                                            0.0  \n",
-       "19                                            0.1  \n",
-       "20                                            0.2  \n",
-       "21                                            0.3  \n",
-       "22                                            0.4  \n",
-       "23                                            0.5  \n",
-       "24                                            0.0  \n",
-       "25                                            0.1  \n",
-       "26                                            0.2  \n",
-       "27                                            0.3  \n",
-       "28                                            0.4  \n",
-       "29                                            0.5  \n",
-       "30                                            0.0  \n",
-       "31                                            0.1  \n",
-       "32                                            0.2  \n",
-       "33                                            0.3  \n",
-       "34                                            0.4  \n",
-       "35                                            0.5  "
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c12bc19d",
-   "metadata": {},
-   "source": [
-    "## Objectives from real logs\n",
-    "(Feature selection)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "39ac74bb",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(34, 178)\n",
-      "34  Event-Logs:  ['BPI2016_Complaints' 'BPI2016_Questions' 'BPI2016_Werkmap_Messages'\n",
-      " 'BPIC15_1' 'BPIC15_2' 'BPIC15_3' 'BPIC15_4' 'BPIC15_5'\n",
-      " 'BPI_Challenge_2012' 'BPI_Challenge_2013_closed_problems'\n",
-      " 'BPI_Challenge_2013_incidents' 'BPI_Challenge_2013_open_problems'\n",
-      " 'BPI_Challenge_2017' 'BPI_Challenge_2017_Offer_log' 'BPI_Challenge_2018'\n",
-      " 'BPI_Challenge_2019' 'CoSeLoG_WABO_1' 'CoSeLoG_WABO_2' 'CoSeLoG_WABO_3'\n",
-      " 'CoSeLoG_WABO_4' 'CoSeLoG_WABO_5' 'Detail_Change'\n",
-      " 'Detail_Incident_Activity' 'Detail_Interaction' 'DomesticDeclarations'\n",
-      " 'Hospital_log' 'InternationalDeclarations' 'PermitLog'\n",
-      " 'PrepaidTravelCost' 'Receipt_WABO_CoSeLoG' 'RequestForPayment'\n",
-      " 'Road_Traffic_Fine_Management_Process' 'Sepsis_Cases_Event_Log' 'finale']\n"
-     ]
-    },
-    {
-     "data": {
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-       "      <th>log</th>\n",
-       "      <th>n_traces</th>\n",
-       "      <th>n_unique_traces</th>\n",
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-       "      <th>trace_len_iqr</th>\n",
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-       "      <th>trace_len_harmonic_mean</th>\n",
-       "      <th>trace_len_skewness</th>\n",
-       "      <th>trace_len_kurtosis</th>\n",
-       "      <th>trace_len_coefficient_variation</th>\n",
-       "      <th>trace_len_entropy</th>\n",
-       "      <th>trace_len_hist1</th>\n",
-       "      <th>trace_len_hist2</th>\n",
-       "      <th>trace_len_hist3</th>\n",
-       "      <th>trace_len_hist4</th>\n",
-       "      <th>trace_len_hist5</th>\n",
-       "      <th>trace_len_hist6</th>\n",
-       "      <th>trace_len_hist7</th>\n",
-       "      <th>trace_len_hist8</th>\n",
-       "      <th>trace_len_hist9</th>\n",
-       "      <th>trace_len_hist10</th>\n",
-       "      <th>trace_len_skewness_hist</th>\n",
-       "      <th>trace_len_kurtosis_hist</th>\n",
-       "      <th>ratio_most_common_variant</th>\n",
-       "      <th>ratio_top_1_variants</th>\n",
-       "      <th>ratio_top_5_variants</th>\n",
-       "      <th>ratio_top_10_variants</th>\n",
-       "      <th>ratio_top_20_variants</th>\n",
-       "      <th>ratio_top_50_variants</th>\n",
-       "      <th>ratio_top_75_variants</th>\n",
-       "      <th>mean_variant_occurrence</th>\n",
-       "      <th>std_variant_occurrence</th>\n",
-       "      <th>skewness_variant_occurrence</th>\n",
-       "      <th>kurtosis_variant_occurrence</th>\n",
-       "      <th>n_unique_activities</th>\n",
-       "      <th>activities_min</th>\n",
-       "      <th>activities_max</th>\n",
-       "      <th>activities_mean</th>\n",
-       "      <th>activities_median</th>\n",
-       "      <th>activities_std</th>\n",
-       "      <th>activities_variance</th>\n",
-       "      <th>activities_q1</th>\n",
-       "      <th>activities_q3</th>\n",
-       "      <th>activities_iqr</th>\n",
-       "      <th>activities_skewness</th>\n",
-       "      <th>activities_kurtosis</th>\n",
-       "      <th>n_unique_start_activities</th>\n",
-       "      <th>start_activities_min</th>\n",
-       "      <th>start_activities_max</th>\n",
-       "      <th>start_activities_mean</th>\n",
-       "      <th>start_activities_median</th>\n",
-       "      <th>start_activities_std</th>\n",
-       "      <th>start_activities_variance</th>\n",
-       "      <th>start_activities_q1</th>\n",
-       "      <th>start_activities_q3</th>\n",
-       "      <th>start_activities_iqr</th>\n",
-       "      <th>start_activities_skewness</th>\n",
-       "      <th>start_activities_kurtosis</th>\n",
-       "      <th>n_unique_end_activities</th>\n",
-       "      <th>end_activities_min</th>\n",
-       "      <th>end_activities_max</th>\n",
-       "      <th>end_activities_mean</th>\n",
-       "      <th>end_activities_median</th>\n",
-       "      <th>end_activities_std</th>\n",
-       "      <th>end_activities_variance</th>\n",
-       "      <th>end_activities_q1</th>\n",
-       "      <th>end_activities_q3</th>\n",
-       "      <th>end_activities_iqr</th>\n",
-       "      <th>end_activities_skewness</th>\n",
-       "      <th>end_activities_kurtosis</th>\n",
-       "      <th>entropy_trace</th>\n",
-       "      <th>entropy_prefix</th>\n",
-       "      <th>entropy_global_block</th>\n",
-       "      <th>entropy_lempel_ziv</th>\n",
-       "      <th>entropy_k_block_diff_1</th>\n",
-       "      <th>entropy_k_block_diff_3</th>\n",
-       "      <th>entropy_k_block_diff_5</th>\n",
-       "      <th>entropy_k_block_ratio_1</th>\n",
-       "      <th>entropy_k_block_ratio_3</th>\n",
-       "      <th>entropy_k_block_ratio_5</th>\n",
-       "      <th>entropy_knn_3</th>\n",
-       "      <th>entropy_knn_5</th>\n",
-       "      <th>entropy_knn_7</th>\n",
-       "      <th>Log Nature</th>\n",
-       "      <th>epa_variant_entropy</th>\n",
-       "      <th>epa_normalized_variant_entropy</th>\n",
-       "      <th>epa_sequence_entropy</th>\n",
-       "      <th>epa_normalized_sequence_entropy</th>\n",
-       "      <th>epa_sequence_entropy_linear_forgetting</th>\n",
-       "      <th>epa_normalized_sequence_entropy_linear_forgetting</th>\n",
-       "      <th>epa_sequence_entropy_exponential_forgetting</th>\n",
-       "      <th>epa_normalized_sequence_entropy_exponential_forgetting</th>\n",
-       "      <th>accumulated_time_time_min</th>\n",
-       "      <th>accumulated_time_time_max</th>\n",
-       "      <th>accumulated_time_time_mean</th>\n",
-       "      <th>accumulated_time_time_median</th>\n",
-       "      <th>accumulated_time_time_mode</th>\n",
-       "      <th>accumulated_time_time_std</th>\n",
-       "      <th>accumulated_time_time_variance</th>\n",
-       "      <th>accumulated_time_time_q1</th>\n",
-       "      <th>accumulated_time_time_q3</th>\n",
-       "      <th>accumulated_time_time_iqr</th>\n",
-       "      <th>accumulated_time_time_geometric_mean</th>\n",
-       "      <th>accumulated_time_time_geometric_std</th>\n",
-       "      <th>accumulated_time_time_harmonic_mean</th>\n",
-       "      <th>accumulated_time_time_skewness</th>\n",
-       "      <th>accumulated_time_time_kurtosis</th>\n",
-       "      <th>accumulated_time_time_coefficient_variation</th>\n",
-       "      <th>accumulated_time_time_entropy</th>\n",
-       "      <th>accumulated_time_time_skewness_hist</th>\n",
-       "      <th>accumulated_time_time_kurtosis_hist</th>\n",
-       "      <th>execution_time_time_min</th>\n",
-       "      <th>execution_time_time_max</th>\n",
-       "      <th>execution_time_time_mean</th>\n",
-       "      <th>execution_time_time_median</th>\n",
-       "      <th>execution_time_time_mode</th>\n",
-       "      <th>execution_time_time_std</th>\n",
-       "      <th>execution_time_time_variance</th>\n",
-       "      <th>execution_time_time_q1</th>\n",
-       "      <th>execution_time_time_q3</th>\n",
-       "      <th>execution_time_time_iqr</th>\n",
-       "      <th>execution_time_time_geometric_mean</th>\n",
-       "      <th>execution_time_time_geometric_std</th>\n",
-       "      <th>execution_time_time_harmonic_mean</th>\n",
-       "      <th>execution_time_time_skewness</th>\n",
-       "      <th>execution_time_time_kurtosis</th>\n",
-       "      <th>execution_time_time_coefficient_variation</th>\n",
-       "      <th>execution_time_time_entropy</th>\n",
-       "      <th>execution_time_time_skewness_hist</th>\n",
-       "      <th>execution_time_time_kurtosis_hist</th>\n",
-       "      <th>remaining_time_time_min</th>\n",
-       "      <th>remaining_time_time_max</th>\n",
-       "      <th>remaining_time_time_mean</th>\n",
-       "      <th>remaining_time_time_median</th>\n",
-       "      <th>remaining_time_time_mode</th>\n",
-       "      <th>remaining_time_time_std</th>\n",
-       "      <th>remaining_time_time_variance</th>\n",
-       "      <th>remaining_time_time_q1</th>\n",
-       "      <th>remaining_time_time_q3</th>\n",
-       "      <th>remaining_time_time_iqr</th>\n",
-       "      <th>remaining_time_time_geometric_mean</th>\n",
-       "      <th>remaining_time_time_geometric_std</th>\n",
-       "      <th>remaining_time_time_harmonic_mean</th>\n",
-       "      <th>remaining_time_time_skewness</th>\n",
-       "      <th>remaining_time_time_kurtosis</th>\n",
-       "      <th>remaining_time_time_coefficient_variation</th>\n",
-       "      <th>remaining_time_time_entropy</th>\n",
-       "      <th>remaining_time_time_skewness_hist</th>\n",
-       "      <th>remaining_time_time_kurtosis_hist</th>\n",
-       "      <th>within_day_time_min</th>\n",
-       "      <th>within_day_time_max</th>\n",
-       "      <th>within_day_time_mean</th>\n",
-       "      <th>within_day_time_median</th>\n",
-       "      <th>within_day_time_mode</th>\n",
-       "      <th>within_day_time_std</th>\n",
-       "      <th>within_day_time_variance</th>\n",
-       "      <th>within_day_time_q1</th>\n",
-       "      <th>within_day_time_q3</th>\n",
-       "      <th>within_day_time_iqr</th>\n",
-       "      <th>within_day_time_geometric_mean</th>\n",
-       "      <th>within_day_time_geometric_std</th>\n",
-       "      <th>within_day_time_harmonic_mean</th>\n",
-       "      <th>within_day_time_skewness</th>\n",
-       "      <th>within_day_time_kurtosis</th>\n",
-       "      <th>within_day_time_coefficient_variation</th>\n",
-       "      <th>within_day_time_entropy</th>\n",
-       "      <th>within_day_time_skewness_hist</th>\n",
-       "      <th>within_day_time_kurtosis_hist</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>BPIC15_2</td>\n",
-       "      <td>832</td>\n",
-       "      <td>828</td>\n",
-       "      <td>0.995192</td>\n",
-       "      <td>1</td>\n",
-       "      <td>132</td>\n",
-       "      <td>53.310096</td>\n",
-       "      <td>54.0</td>\n",
-       "      <td>61</td>\n",
-       "      <td>19.894977</td>\n",
-       "      <td>395.810090</td>\n",
-       "      <td>44.0</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>48.150111</td>\n",
-       "      <td>1.695311</td>\n",
-       "      <td>37.583741</td>\n",
-       "      <td>0.054138</td>\n",
-       "      <td>0.804992</td>\n",
-       "      <td>0.373193</td>\n",
-       "      <td>6.646715</td>\n",
-       "      <td>0.003853</td>\n",
-       "      <td>0.004863</td>\n",
-       "      <td>4.679243e-03</td>\n",
-       "      <td>0.023947</td>\n",
-       "      <td>2.376321e-02</td>\n",
-       "      <td>8.257487e-03</td>\n",
-       "      <td>0.004771</td>\n",
-       "      <td>1.376248e-03</td>\n",
-       "      <td>6.422490e-04</td>\n",
-       "      <td>1.834997e-04</td>\n",
-       "      <td>0.054138</td>\n",
-       "      <td>0.804992</td>\n",
-       "      <td>0.002404</td>\n",
-       "      <td>0.014423</td>\n",
-       "      <td>0.054087</td>\n",
-       "      <td>0.103365</td>\n",
-       "      <td>0.203125</td>\n",
-       "      <td>0.502404</td>\n",
-       "      <td>0.751202</td>\n",
-       "      <td>1.004831</td>\n",
-       "      <td>0.069337</td>\n",
-       "      <td>14.283027</td>\n",
-       "      <td>202.004854</td>\n",
-       "      <td>410</td>\n",
-       "      <td>1</td>\n",
-       "      <td>830</td>\n",
-       "      <td>108.180488</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>187.588162</td>\n",
-       "      <td>3.518932e+04</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>125.5</td>\n",
-       "      <td>122.5</td>\n",
-       "      <td>2.129412</td>\n",
-       "      <td>3.808278</td>\n",
-       "      <td>14</td>\n",
-       "      <td>1</td>\n",
-       "      <td>731</td>\n",
-       "      <td>59.428571</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>186.717401</td>\n",
-       "      <td>3.486339e+04</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>8.25</td>\n",
-       "      <td>7.25</td>\n",
-       "      <td>3.300411</td>\n",
-       "      <td>8.960767</td>\n",
-       "      <td>82</td>\n",
-       "      <td>1</td>\n",
-       "      <td>216</td>\n",
-       "      <td>10.146341</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>35.318800</td>\n",
-       "      <td>1.247418e+03</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>5.098791</td>\n",
-       "      <td>25.861991</td>\n",
-       "      <td>9.691</td>\n",
-       "      <td>14.524</td>\n",
-       "      <td>19.448</td>\n",
-       "      <td>3.859</td>\n",
-       "      <td>7.105</td>\n",
-       "      <td>7.105</td>\n",
-       "      <td>7.105</td>\n",
-       "      <td>7.105</td>\n",
-       "      <td>7.105</td>\n",
-       "      <td>7.105</td>\n",
-       "      <td>5.545</td>\n",
-       "      <td>5.039</td>\n",
-       "      <td>4.721</td>\n",
-       "      <td>Real</td>\n",
-       "      <td>2.405122e+05</td>\n",
-       "      <td>0.627973</td>\n",
-       "      <td>2.858769e+05</td>\n",
-       "      <td>0.602371</td>\n",
-       "      <td>1.505466e+05</td>\n",
-       "      <td>0.317217</td>\n",
-       "      <td>1.853129e+05</td>\n",
-       "      <td>0.390473</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>BPI_Challenge_2018</td>\n",
-       "      <td>43809</td>\n",
-       "      <td>28457</td>\n",
-       "      <td>0.649570</td>\n",
-       "      <td>24</td>\n",
-       "      <td>2973</td>\n",
-       "      <td>57.391541</td>\n",
-       "      <td>49.0</td>\n",
-       "      <td>49</td>\n",
-       "      <td>34.872131</td>\n",
-       "      <td>1216.065487</td>\n",
-       "      <td>44.0</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>53.775008</td>\n",
-       "      <td>1.367397</td>\n",
-       "      <td>51.651502</td>\n",
-       "      <td>26.126459</td>\n",
-       "      <td>1720.399665</td>\n",
-       "      <td>0.607618</td>\n",
-       "      <td>10.598758</td>\n",
-       "      <td>0.003385</td>\n",
-       "      <td>0.000005</td>\n",
-       "      <td>9.288448e-07</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>7.740373e-08</td>\n",
-       "      <td>7.740373e-08</td>\n",
-       "      <td>26.126459</td>\n",
-       "      <td>1720.399665</td>\n",
-       "      <td>0.026981</td>\n",
-       "      <td>0.290374</td>\n",
-       "      <td>0.373006</td>\n",
-       "      <td>0.415371</td>\n",
-       "      <td>0.480335</td>\n",
-       "      <td>0.675204</td>\n",
-       "      <td>0.837590</td>\n",
-       "      <td>1.539481</td>\n",
-       "      <td>12.487438</td>\n",
-       "      <td>64.625680</td>\n",
-       "      <td>5083.455806</td>\n",
-       "      <td>41</td>\n",
-       "      <td>17</td>\n",
-       "      <td>466141</td>\n",
-       "      <td>61323.560976</td>\n",
-       "      <td>7530.0</td>\n",
-       "      <td>120522.247417</td>\n",
-       "      <td>1.452561e+10</td>\n",
-       "      <td>902.0</td>\n",
-       "      <td>45907.0</td>\n",
-       "      <td>45005.0</td>\n",
-       "      <td>2.444007</td>\n",
-       "      <td>4.773254</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>38623</td>\n",
-       "      <td>10952.250000</td>\n",
-       "      <td>2592.0</td>\n",
-       "      <td>16111.407548</td>\n",
-       "      <td>2.595775e+08</td>\n",
-       "      <td>36.5</td>\n",
-       "      <td>13507.75</td>\n",
-       "      <td>13471.25</td>\n",
-       "      <td>1.098736</td>\n",
-       "      <td>-0.714800</td>\n",
-       "      <td>21</td>\n",
-       "      <td>1</td>\n",
-       "      <td>34830</td>\n",
-       "      <td>2086.142857</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>7431.744981</td>\n",
-       "      <td>5.523083e+07</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>193.00</td>\n",
-       "      <td>191.00</td>\n",
-       "      <td>4.062387</td>\n",
-       "      <td>14.952824</td>\n",
-       "      <td>13.191</td>\n",
-       "      <td>16.272</td>\n",
-       "      <td>20.972</td>\n",
-       "      <td>1.023</td>\n",
-       "      <td>-0.010</td>\n",
-       "      <td>1.855</td>\n",
-       "      <td>0.511</td>\n",
-       "      <td>1.403</td>\n",
-       "      <td>3.572</td>\n",
-       "      <td>2.001</td>\n",
-       "      <td>7.849</td>\n",
-       "      <td>7.371</td>\n",
-       "      <td>7.067</td>\n",
-       "      <td>Real</td>\n",
-       "      <td>1.156384e+07</td>\n",
-       "      <td>0.712079</td>\n",
-       "      <td>2.114626e+07</td>\n",
-       "      <td>0.570688</td>\n",
-       "      <td>1.414023e+07</td>\n",
-       "      <td>0.381612</td>\n",
-       "      <td>1.557608e+07</td>\n",
-       "      <td>0.420362</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Receipt_WABO_CoSeLoG</td>\n",
-       "      <td>1434</td>\n",
-       "      <td>116</td>\n",
-       "      <td>0.080893</td>\n",
-       "      <td>1</td>\n",
-       "      <td>25</td>\n",
-       "      <td>5.981172</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2.166129</td>\n",
-       "      <td>4.692114</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5.414708</td>\n",
-       "      <td>1.704965</td>\n",
-       "      <td>4.356445</td>\n",
-       "      <td>1.276525</td>\n",
-       "      <td>12.296006</td>\n",
-       "      <td>0.362158</td>\n",
-       "      <td>7.197193</td>\n",
-       "      <td>0.036030</td>\n",
-       "      <td>0.008136</td>\n",
-       "      <td>3.411204e-01</td>\n",
-       "      <td>0.023536</td>\n",
-       "      <td>3.777313e-03</td>\n",
-       "      <td>1.743375e-03</td>\n",
-       "      <td>0.000291</td>\n",
-       "      <td>1.452813e-03</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>5.811251e-04</td>\n",
-       "      <td>1.276525</td>\n",
-       "      <td>12.296006</td>\n",
-       "      <td>0.497211</td>\n",
-       "      <td>0.497211</td>\n",
-       "      <td>0.796374</td>\n",
-       "      <td>0.887029</td>\n",
-       "      <td>0.930265</td>\n",
-       "      <td>0.959554</td>\n",
-       "      <td>0.979777</td>\n",
-       "      <td>12.362069</td>\n",
-       "      <td>68.360277</td>\n",
-       "      <td>9.380687</td>\n",
-       "      <td>92.281919</td>\n",
-       "      <td>27</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1434</td>\n",
-       "      <td>317.666667</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>553.389823</td>\n",
-       "      <td>3.062403e+05</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>42.0</td>\n",
-       "      <td>1.342951</td>\n",
-       "      <td>-0.178094</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1434</td>\n",
-       "      <td>1434</td>\n",
-       "      <td>1434.000000</td>\n",
-       "      <td>1434.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>1434.0</td>\n",
-       "      <td>1434.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>14</td>\n",
-       "      <td>1</td>\n",
-       "      <td>828</td>\n",
-       "      <td>102.428571</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>225.871555</td>\n",
-       "      <td>5.101796e+04</td>\n",
-       "      <td>1.25</td>\n",
-       "      <td>33.25</td>\n",
-       "      <td>32.00</td>\n",
-       "      <td>2.471765</td>\n",
-       "      <td>4.846541</td>\n",
-       "      <td>3.209</td>\n",
-       "      <td>4.746</td>\n",
-       "      <td>7.019</td>\n",
-       "      <td>0.385</td>\n",
-       "      <td>2.672</td>\n",
-       "      <td>2.966</td>\n",
-       "      <td>0.804</td>\n",
-       "      <td>1.484</td>\n",
-       "      <td>2.966</td>\n",
-       "      <td>2.966</td>\n",
-       "      <td>3.260</td>\n",
-       "      <td>2.845</td>\n",
-       "      <td>2.584</td>\n",
-       "      <td>Real</td>\n",
-       "      <td>2.382326e+03</td>\n",
-       "      <td>0.689363</td>\n",
-       "      <td>1.829627e+04</td>\n",
-       "      <td>0.235532</td>\n",
-       "      <td>7.814868e+03</td>\n",
-       "      <td>0.100603</td>\n",
-       "      <td>1.072870e+04</td>\n",
-       "      <td>0.138113</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>BPIC15_3</td>\n",
-       "      <td>1409</td>\n",
-       "      <td>1349</td>\n",
-       "      <td>0.957417</td>\n",
-       "      <td>3</td>\n",
-       "      <td>124</td>\n",
-       "      <td>42.356991</td>\n",
-       "      <td>42.0</td>\n",
-       "      <td>44</td>\n",
-       "      <td>16.138406</td>\n",
-       "      <td>260.448143</td>\n",
-       "      <td>38.0</td>\n",
-       "      <td>47.0</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>37.637731</td>\n",
-       "      <td>1.786726</td>\n",
-       "      <td>29.092933</td>\n",
-       "      <td>-0.009541</td>\n",
-       "      <td>1.543369</td>\n",
-       "      <td>0.381009</td>\n",
-       "      <td>7.167153</td>\n",
-       "      <td>0.006921</td>\n",
-       "      <td>0.004340</td>\n",
-       "      <td>1.630604e-02</td>\n",
-       "      <td>0.036953</td>\n",
-       "      <td>1.173096e-02</td>\n",
-       "      <td>4.105837e-03</td>\n",
-       "      <td>0.001584</td>\n",
-       "      <td>5.278933e-04</td>\n",
-       "      <td>1.173096e-04</td>\n",
-       "      <td>5.865481e-05</td>\n",
-       "      <td>-0.009541</td>\n",
-       "      <td>1.543369</td>\n",
-       "      <td>0.010646</td>\n",
-       "      <td>0.049681</td>\n",
-       "      <td>0.090135</td>\n",
-       "      <td>0.137686</td>\n",
-       "      <td>0.233499</td>\n",
-       "      <td>0.520937</td>\n",
-       "      <td>0.760114</td>\n",
-       "      <td>1.044477</td>\n",
-       "      <td>0.592348</td>\n",
-       "      <td>17.964130</td>\n",
-       "      <td>358.019511</td>\n",
-       "      <td>383</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1409</td>\n",
-       "      <td>155.825065</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>306.310544</td>\n",
-       "      <td>9.382615e+04</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>108.5</td>\n",
-       "      <td>103.5</td>\n",
-       "      <td>2.446349</td>\n",
-       "      <td>5.280931</td>\n",
-       "      <td>9</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1348</td>\n",
-       "      <td>156.555556</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>421.270858</td>\n",
-       "      <td>1.774691e+05</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>14.00</td>\n",
-       "      <td>11.00</td>\n",
-       "      <td>2.474158</td>\n",
-       "      <td>4.122971</td>\n",
-       "      <td>119</td>\n",
-       "      <td>1</td>\n",
-       "      <td>342</td>\n",
-       "      <td>11.840336</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>39.557210</td>\n",
-       "      <td>1.564773e+03</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>7.00</td>\n",
-       "      <td>6.00</td>\n",
-       "      <td>6.217217</td>\n",
-       "      <td>43.335525</td>\n",
-       "      <td>10.317</td>\n",
-       "      <td>14.226</td>\n",
-       "      <td>18.743</td>\n",
-       "      <td>3.182</td>\n",
-       "      <td>-0.007</td>\n",
-       "      <td>6.780</td>\n",
-       "      <td>6.780</td>\n",
-       "      <td>6.780</td>\n",
-       "      <td>6.780</td>\n",
-       "      <td>6.780</td>\n",
-       "      <td>5.701</td>\n",
-       "      <td>5.212</td>\n",
-       "      <td>4.900</td>\n",
-       "      <td>Real</td>\n",
-       "      <td>2.981464e+05</td>\n",
-       "      <td>0.661781</td>\n",
-       "      <td>3.975043e+05</td>\n",
-       "      <td>0.605676</td>\n",
-       "      <td>2.241393e+05</td>\n",
-       "      <td>0.341521</td>\n",
-       "      <td>2.657571e+05</td>\n",
-       "      <td>0.404934</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>BPI_Challenge_2019</td>\n",
-       "      <td>251734</td>\n",
-       "      <td>11973</td>\n",
-       "      <td>0.047562</td>\n",
-       "      <td>1</td>\n",
-       "      <td>990</td>\n",
-       "      <td>6.339720</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>13.057417</td>\n",
-       "      <td>170.496137</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>5.173569</td>\n",
-       "      <td>1.635822</td>\n",
-       "      <td>4.592844</td>\n",
-       "      <td>22.132989</td>\n",
-       "      <td>753.772202</td>\n",
-       "      <td>2.059621</td>\n",
-       "      <td>12.044057</td>\n",
-       "      <td>0.010078</td>\n",
-       "      <td>0.000020</td>\n",
-       "      <td>9.559579e-06</td>\n",
-       "      <td>0.000003</td>\n",
-       "      <td>3.614967e-07</td>\n",
-       "      <td>1.606652e-07</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>4.016630e-08</td>\n",
-       "      <td>8.033260e-08</td>\n",
-       "      <td>8.033260e-08</td>\n",
-       "      <td>22.132989</td>\n",
-       "      <td>753.772202</td>\n",
-       "      <td>0.199758</td>\n",
-       "      <td>0.871424</td>\n",
-       "      <td>0.929990</td>\n",
-       "      <td>0.946368</td>\n",
-       "      <td>0.959767</td>\n",
-       "      <td>0.976217</td>\n",
-       "      <td>0.988106</td>\n",
-       "      <td>21.025140</td>\n",
-       "      <td>594.255619</td>\n",
-       "      <td>64.772702</td>\n",
-       "      <td>4917.319751</td>\n",
-       "      <td>42</td>\n",
-       "      <td>2</td>\n",
-       "      <td>314097</td>\n",
-       "      <td>37998.166667</td>\n",
-       "      <td>1628.0</td>\n",
-       "      <td>80833.669206</td>\n",
-       "      <td>6.534082e+09</td>\n",
-       "      <td>202.0</td>\n",
-       "      <td>11536.0</td>\n",
-       "      <td>11334.0</td>\n",
-       "      <td>2.169648</td>\n",
-       "      <td>3.263594</td>\n",
-       "      <td>8</td>\n",
-       "      <td>2</td>\n",
-       "      <td>199867</td>\n",
-       "      <td>31466.750000</td>\n",
-       "      <td>869.0</td>\n",
-       "      <td>65387.493286</td>\n",
-       "      <td>4.275524e+09</td>\n",
-       "      <td>97.0</td>\n",
-       "      <td>14224.25</td>\n",
-       "      <td>14127.25</td>\n",
-       "      <td>2.059742</td>\n",
-       "      <td>2.535789</td>\n",
-       "      <td>32</td>\n",
-       "      <td>1</td>\n",
-       "      <td>181328</td>\n",
-       "      <td>7866.687500</td>\n",
-       "      <td>64.5</td>\n",
-       "      <td>31658.428996</td>\n",
-       "      <td>1.002256e+09</td>\n",
-       "      <td>9.00</td>\n",
-       "      <td>1027.25</td>\n",
-       "      <td>1018.25</td>\n",
-       "      <td>5.135607</td>\n",
-       "      <td>25.170543</td>\n",
-       "      <td>6.243</td>\n",
-       "      <td>8.811</td>\n",
-       "      <td>19.447</td>\n",
-       "      <td>0.346</td>\n",
-       "      <td>-0.041</td>\n",
-       "      <td>1.530</td>\n",
-       "      <td>0.840</td>\n",
-       "      <td>0.620</td>\n",
-       "      <td>3.244</td>\n",
-       "      <td>1.913</td>\n",
-       "      <td>7.333</td>\n",
-       "      <td>6.882</td>\n",
-       "      <td>6.601</td>\n",
-       "      <td>Real</td>\n",
-       "      <td>1.690369e+06</td>\n",
-       "      <td>0.645530</td>\n",
-       "      <td>7.477256e+06</td>\n",
-       "      <td>0.328029</td>\n",
-       "      <td>7.298458e+06</td>\n",
-       "      <td>0.320185</td>\n",
-       "      <td>7.300663e+06</td>\n",
-       "      <td>0.320282</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                    log  n_traces  n_unique_traces   \n",
-       "0              BPIC15_2       832              828  \\\n",
-       "1    BPI_Challenge_2018     43809            28457   \n",
-       "2  Receipt_WABO_CoSeLoG      1434              116   \n",
-       "3              BPIC15_3      1409             1349   \n",
-       "4    BPI_Challenge_2019    251734            11973   \n",
-       "\n",
-       "   ratio_unique_traces_per_trace  trace_len_min  trace_len_max   \n",
-       "0                       0.995192              1            132  \\\n",
-       "1                       0.649570             24           2973   \n",
-       "2                       0.080893              1             25   \n",
-       "3                       0.957417              3            124   \n",
-       "4                       0.047562              1            990   \n",
-       "\n",
-       "   trace_len_mean  trace_len_median  trace_len_mode  trace_len_std   \n",
-       "0       53.310096              54.0              61      19.894977  \\\n",
-       "1       57.391541              49.0              49      34.872131   \n",
-       "2        5.981172               6.0               6       2.166129   \n",
-       "3       42.356991              42.0              44      16.138406   \n",
-       "4        6.339720               5.0               5      13.057417   \n",
-       "\n",
-       "   trace_len_variance  trace_len_q1  trace_len_q3  trace_len_iqr   \n",
-       "0          395.810090          44.0          62.0           18.0  \\\n",
-       "1         1216.065487          44.0          59.0           15.0   \n",
-       "2            4.692114           6.0           6.0            0.0   \n",
-       "3          260.448143          38.0          47.0            9.0   \n",
-       "4          170.496137           5.0           6.0            1.0   \n",
-       "\n",
-       "   trace_len_geometric_mean  trace_len_geometric_std  trace_len_harmonic_mean   \n",
-       "0                 48.150111                 1.695311                37.583741  \\\n",
-       "1                 53.775008                 1.367397                51.651502   \n",
-       "2                  5.414708                 1.704965                 4.356445   \n",
-       "3                 37.637731                 1.786726                29.092933   \n",
-       "4                  5.173569                 1.635822                 4.592844   \n",
-       "\n",
-       "   trace_len_skewness  trace_len_kurtosis  trace_len_coefficient_variation   \n",
-       "0            0.054138            0.804992                         0.373193  \\\n",
-       "1           26.126459         1720.399665                         0.607618   \n",
-       "2            1.276525           12.296006                         0.362158   \n",
-       "3           -0.009541            1.543369                         0.381009   \n",
-       "4           22.132989          753.772202                         2.059621   \n",
-       "\n",
-       "   trace_len_entropy  trace_len_hist1  trace_len_hist2  trace_len_hist3   \n",
-       "0           6.646715         0.003853         0.004863     4.679243e-03  \\\n",
-       "1          10.598758         0.003385         0.000005     9.288448e-07   \n",
-       "2           7.197193         0.036030         0.008136     3.411204e-01   \n",
-       "3           7.167153         0.006921         0.004340     1.630604e-02   \n",
-       "4          12.044057         0.010078         0.000020     9.559579e-06   \n",
-       "\n",
-       "   trace_len_hist4  trace_len_hist5  trace_len_hist6  trace_len_hist7   \n",
-       "0         0.023947     2.376321e-02     8.257487e-03         0.004771  \\\n",
-       "1         0.000000     0.000000e+00     0.000000e+00         0.000000   \n",
-       "2         0.023536     3.777313e-03     1.743375e-03         0.000291   \n",
-       "3         0.036953     1.173096e-02     4.105837e-03         0.001584   \n",
-       "4         0.000003     3.614967e-07     1.606652e-07         0.000000   \n",
-       "\n",
-       "   trace_len_hist8  trace_len_hist9  trace_len_hist10   \n",
-       "0     1.376248e-03     6.422490e-04      1.834997e-04  \\\n",
-       "1     0.000000e+00     7.740373e-08      7.740373e-08   \n",
-       "2     1.452813e-03     0.000000e+00      5.811251e-04   \n",
-       "3     5.278933e-04     1.173096e-04      5.865481e-05   \n",
-       "4     4.016630e-08     8.033260e-08      8.033260e-08   \n",
-       "\n",
-       "   trace_len_skewness_hist  trace_len_kurtosis_hist   \n",
-       "0                 0.054138                 0.804992  \\\n",
-       "1                26.126459              1720.399665   \n",
-       "2                 1.276525                12.296006   \n",
-       "3                -0.009541                 1.543369   \n",
-       "4                22.132989               753.772202   \n",
-       "\n",
-       "   ratio_most_common_variant  ratio_top_1_variants  ratio_top_5_variants   \n",
-       "0                   0.002404              0.014423              0.054087  \\\n",
-       "1                   0.026981              0.290374              0.373006   \n",
-       "2                   0.497211              0.497211              0.796374   \n",
-       "3                   0.010646              0.049681              0.090135   \n",
-       "4                   0.199758              0.871424              0.929990   \n",
-       "\n",
-       "   ratio_top_10_variants  ratio_top_20_variants  ratio_top_50_variants   \n",
-       "0               0.103365               0.203125               0.502404  \\\n",
-       "1               0.415371               0.480335               0.675204   \n",
-       "2               0.887029               0.930265               0.959554   \n",
-       "3               0.137686               0.233499               0.520937   \n",
-       "4               0.946368               0.959767               0.976217   \n",
-       "\n",
-       "   ratio_top_75_variants  mean_variant_occurrence  std_variant_occurrence   \n",
-       "0               0.751202                 1.004831                0.069337  \\\n",
-       "1               0.837590                 1.539481               12.487438   \n",
-       "2               0.979777                12.362069               68.360277   \n",
-       "3               0.760114                 1.044477                0.592348   \n",
-       "4               0.988106                21.025140              594.255619   \n",
-       "\n",
-       "   skewness_variant_occurrence  kurtosis_variant_occurrence   \n",
-       "0                    14.283027                   202.004854  \\\n",
-       "1                    64.625680                  5083.455806   \n",
-       "2                     9.380687                    92.281919   \n",
-       "3                    17.964130                   358.019511   \n",
-       "4                    64.772702                  4917.319751   \n",
-       "\n",
-       "   n_unique_activities  activities_min  activities_max  activities_mean   \n",
-       "0                  410               1             830       108.180488  \\\n",
-       "1                   41              17          466141     61323.560976   \n",
-       "2                   27               1            1434       317.666667   \n",
-       "3                  383               1            1409       155.825065   \n",
-       "4                   42               2          314097     37998.166667   \n",
-       "\n",
-       "   activities_median  activities_std  activities_variance  activities_q1   \n",
-       "0               12.0      187.588162         3.518932e+04            3.0  \\\n",
-       "1             7530.0   120522.247417         1.452561e+10          902.0   \n",
-       "2               27.0      553.389823         3.062403e+05            8.0   \n",
-       "3               16.0      306.310544         9.382615e+04            5.0   \n",
-       "4             1628.0    80833.669206         6.534082e+09          202.0   \n",
-       "\n",
-       "   activities_q3  activities_iqr  activities_skewness  activities_kurtosis   \n",
-       "0          125.5           122.5             2.129412             3.808278  \\\n",
-       "1        45907.0         45005.0             2.444007             4.773254   \n",
-       "2           50.0            42.0             1.342951            -0.178094   \n",
-       "3          108.5           103.5             2.446349             5.280931   \n",
-       "4        11536.0         11334.0             2.169648             3.263594   \n",
-       "\n",
-       "   n_unique_start_activities  start_activities_min  start_activities_max   \n",
-       "0                         14                     1                   731  \\\n",
-       "1                          4                     2                 38623   \n",
-       "2                          1                  1434                  1434   \n",
-       "3                          9                     1                  1348   \n",
-       "4                          8                     2                199867   \n",
-       "\n",
-       "   start_activities_mean  start_activities_median  start_activities_std   \n",
-       "0              59.428571                      1.0            186.717401  \\\n",
-       "1           10952.250000                   2592.0          16111.407548   \n",
-       "2            1434.000000                   1434.0              0.000000   \n",
-       "3             156.555556                      8.0            421.270858   \n",
-       "4           31466.750000                    869.0          65387.493286   \n",
-       "\n",
-       "   start_activities_variance  start_activities_q1  start_activities_q3   \n",
-       "0               3.486339e+04                  1.0                 8.25  \\\n",
-       "1               2.595775e+08                 36.5             13507.75   \n",
-       "2               0.000000e+00               1434.0              1434.00   \n",
-       "3               1.774691e+05                  3.0                14.00   \n",
-       "4               4.275524e+09                 97.0             14224.25   \n",
-       "\n",
-       "   start_activities_iqr  start_activities_skewness  start_activities_kurtosis   \n",
-       "0                  7.25                   3.300411                   8.960767  \\\n",
-       "1              13471.25                   1.098736                  -0.714800   \n",
-       "2                  0.00                        NaN                        NaN   \n",
-       "3                 11.00                   2.474158                   4.122971   \n",
-       "4              14127.25                   2.059742                   2.535789   \n",
-       "\n",
-       "   n_unique_end_activities  end_activities_min  end_activities_max   \n",
-       "0                       82                   1                 216  \\\n",
-       "1                       21                   1               34830   \n",
-       "2                       14                   1                 828   \n",
-       "3                      119                   1                 342   \n",
-       "4                       32                   1              181328   \n",
-       "\n",
-       "   end_activities_mean  end_activities_median  end_activities_std   \n",
-       "0            10.146341                    1.0           35.318800  \\\n",
-       "1          2086.142857                   13.0         7431.744981   \n",
-       "2           102.428571                    6.0          225.871555   \n",
-       "3            11.840336                    2.0           39.557210   \n",
-       "4          7866.687500                   64.5        31658.428996   \n",
-       "\n",
-       "   end_activities_variance  end_activities_q1  end_activities_q3   \n",
-       "0             1.247418e+03               1.00               3.00  \\\n",
-       "1             5.523083e+07               2.00             193.00   \n",
-       "2             5.101796e+04               1.25              33.25   \n",
-       "3             1.564773e+03               1.00               7.00   \n",
-       "4             1.002256e+09               9.00            1027.25   \n",
-       "\n",
-       "   end_activities_iqr  end_activities_skewness  end_activities_kurtosis   \n",
-       "0                2.00                 5.098791                25.861991  \\\n",
-       "1              191.00                 4.062387                14.952824   \n",
-       "2               32.00                 2.471765                 4.846541   \n",
-       "3                6.00                 6.217217                43.335525   \n",
-       "4             1018.25                 5.135607                25.170543   \n",
-       "\n",
-       "   entropy_trace  entropy_prefix  entropy_global_block  entropy_lempel_ziv   \n",
-       "0          9.691          14.524                19.448               3.859  \\\n",
-       "1         13.191          16.272                20.972               1.023   \n",
-       "2          3.209           4.746                 7.019               0.385   \n",
-       "3         10.317          14.226                18.743               3.182   \n",
-       "4          6.243           8.811                19.447               0.346   \n",
-       "\n",
-       "   entropy_k_block_diff_1  entropy_k_block_diff_3  entropy_k_block_diff_5   \n",
-       "0                   7.105                   7.105                   7.105  \\\n",
-       "1                  -0.010                   1.855                   0.511   \n",
-       "2                   2.672                   2.966                   0.804   \n",
-       "3                  -0.007                   6.780                   6.780   \n",
-       "4                  -0.041                   1.530                   0.840   \n",
-       "\n",
-       "   entropy_k_block_ratio_1  entropy_k_block_ratio_3  entropy_k_block_ratio_5   \n",
-       "0                    7.105                    7.105                    7.105  \\\n",
-       "1                    1.403                    3.572                    2.001   \n",
-       "2                    1.484                    2.966                    2.966   \n",
-       "3                    6.780                    6.780                    6.780   \n",
-       "4                    0.620                    3.244                    1.913   \n",
-       "\n",
-       "   entropy_knn_3  entropy_knn_5  entropy_knn_7 Log Nature   \n",
-       "0          5.545          5.039          4.721       Real  \\\n",
-       "1          7.849          7.371          7.067       Real   \n",
-       "2          3.260          2.845          2.584       Real   \n",
-       "3          5.701          5.212          4.900       Real   \n",
-       "4          7.333          6.882          6.601       Real   \n",
-       "\n",
-       "   epa_variant_entropy  epa_normalized_variant_entropy  epa_sequence_entropy   \n",
-       "0         2.405122e+05                        0.627973          2.858769e+05  \\\n",
-       "1         1.156384e+07                        0.712079          2.114626e+07   \n",
-       "2         2.382326e+03                        0.689363          1.829627e+04   \n",
-       "3         2.981464e+05                        0.661781          3.975043e+05   \n",
-       "4         1.690369e+06                        0.645530          7.477256e+06   \n",
-       "\n",
-       "   epa_normalized_sequence_entropy  epa_sequence_entropy_linear_forgetting   \n",
-       "0                         0.602371                            1.505466e+05  \\\n",
-       "1                         0.570688                            1.414023e+07   \n",
-       "2                         0.235532                            7.814868e+03   \n",
-       "3                         0.605676                            2.241393e+05   \n",
-       "4                         0.328029                            7.298458e+06   \n",
-       "\n",
-       "   epa_normalized_sequence_entropy_linear_forgetting   \n",
-       "0                                           0.317217  \\\n",
-       "1                                           0.381612   \n",
-       "2                                           0.100603   \n",
-       "3                                           0.341521   \n",
-       "4                                           0.320185   \n",
-       "\n",
-       "   epa_sequence_entropy_exponential_forgetting   \n",
-       "0                                 1.853129e+05  \\\n",
-       "1                                 1.557608e+07   \n",
-       "2                                 1.072870e+04   \n",
-       "3                                 2.657571e+05   \n",
-       "4                                 7.300663e+06   \n",
-       "\n",
-       "   epa_normalized_sequence_entropy_exponential_forgetting   \n",
-       "0                                           0.390473       \\\n",
-       "1                                           0.420362        \n",
-       "2                                           0.138113        \n",
-       "3                                           0.404934        \n",
-       "4                                           0.320282        \n",
-       "\n",
-       "   accumulated_time_time_min  accumulated_time_time_max   \n",
-       "0                        NaN                        NaN  \\\n",
-       "1                        NaN                        NaN   \n",
-       "2                        NaN                        NaN   \n",
-       "3                        NaN                        NaN   \n",
-       "4                        NaN                        NaN   \n",
-       "\n",
-       "   accumulated_time_time_mean  accumulated_time_time_median   \n",
-       "0                         NaN                           NaN  \\\n",
-       "1                         NaN                           NaN   \n",
-       "2                         NaN                           NaN   \n",
-       "3                         NaN                           NaN   \n",
-       "4                         NaN                           NaN   \n",
-       "\n",
-       "   accumulated_time_time_mode  accumulated_time_time_std   \n",
-       "0                         NaN                        NaN  \\\n",
-       "1                         NaN                        NaN   \n",
-       "2                         NaN                        NaN   \n",
-       "3                         NaN                        NaN   \n",
-       "4                         NaN                        NaN   \n",
-       "\n",
-       "   accumulated_time_time_variance  accumulated_time_time_q1   \n",
-       "0                             NaN                       NaN  \\\n",
-       "1                             NaN                       NaN   \n",
-       "2                             NaN                       NaN   \n",
-       "3                             NaN                       NaN   \n",
-       "4                             NaN                       NaN   \n",
-       "\n",
-       "   accumulated_time_time_q3  accumulated_time_time_iqr   \n",
-       "0                       NaN                        NaN  \\\n",
-       "1                       NaN                        NaN   \n",
-       "2                       NaN                        NaN   \n",
-       "3                       NaN                        NaN   \n",
-       "4                       NaN                        NaN   \n",
-       "\n",
-       "   accumulated_time_time_geometric_mean  accumulated_time_time_geometric_std   \n",
-       "0                                   NaN                                  NaN  \\\n",
-       "1                                   NaN                                  NaN   \n",
-       "2                                   NaN                                  NaN   \n",
-       "3                                   NaN                                  NaN   \n",
-       "4                                   NaN                                  NaN   \n",
-       "\n",
-       "   accumulated_time_time_harmonic_mean  accumulated_time_time_skewness   \n",
-       "0                                  NaN                             NaN  \\\n",
-       "1                                  NaN                             NaN   \n",
-       "2                                  NaN                             NaN   \n",
-       "3                                  NaN                             NaN   \n",
-       "4                                  NaN                             NaN   \n",
-       "\n",
-       "   accumulated_time_time_kurtosis   \n",
-       "0                             NaN  \\\n",
-       "1                             NaN   \n",
-       "2                             NaN   \n",
-       "3                             NaN   \n",
-       "4                             NaN   \n",
-       "\n",
-       "   accumulated_time_time_coefficient_variation  accumulated_time_time_entropy   \n",
-       "0                                          NaN                            NaN  \\\n",
-       "1                                          NaN                            NaN   \n",
-       "2                                          NaN                            NaN   \n",
-       "3                                          NaN                            NaN   \n",
-       "4                                          NaN                            NaN   \n",
-       "\n",
-       "   accumulated_time_time_skewness_hist  accumulated_time_time_kurtosis_hist   \n",
-       "0                                  NaN                                  NaN  \\\n",
-       "1                                  NaN                                  NaN   \n",
-       "2                                  NaN                                  NaN   \n",
-       "3                                  NaN                                  NaN   \n",
-       "4                                  NaN                                  NaN   \n",
-       "\n",
-       "   execution_time_time_min  execution_time_time_max  execution_time_time_mean   \n",
-       "0                      NaN                      NaN                       NaN  \\\n",
-       "1                      NaN                      NaN                       NaN   \n",
-       "2                      NaN                      NaN                       NaN   \n",
-       "3                      NaN                      NaN                       NaN   \n",
-       "4                      NaN                      NaN                       NaN   \n",
-       "\n",
-       "   execution_time_time_median  execution_time_time_mode   \n",
-       "0                         NaN                       NaN  \\\n",
-       "1                         NaN                       NaN   \n",
-       "2                         NaN                       NaN   \n",
-       "3                         NaN                       NaN   \n",
-       "4                         NaN                       NaN   \n",
-       "\n",
-       "   execution_time_time_std  execution_time_time_variance   \n",
-       "0                      NaN                           NaN  \\\n",
-       "1                      NaN                           NaN   \n",
-       "2                      NaN                           NaN   \n",
-       "3                      NaN                           NaN   \n",
-       "4                      NaN                           NaN   \n",
-       "\n",
-       "   execution_time_time_q1  execution_time_time_q3  execution_time_time_iqr   \n",
-       "0                     NaN                     NaN                      NaN  \\\n",
-       "1                     NaN                     NaN                      NaN   \n",
-       "2                     NaN                     NaN                      NaN   \n",
-       "3                     NaN                     NaN                      NaN   \n",
-       "4                     NaN                     NaN                      NaN   \n",
-       "\n",
-       "   execution_time_time_geometric_mean  execution_time_time_geometric_std   \n",
-       "0                                 NaN                                NaN  \\\n",
-       "1                                 NaN                                NaN   \n",
-       "2                                 NaN                                NaN   \n",
-       "3                                 NaN                                NaN   \n",
-       "4                                 NaN                                NaN   \n",
-       "\n",
-       "   execution_time_time_harmonic_mean  execution_time_time_skewness   \n",
-       "0                                NaN                           NaN  \\\n",
-       "1                                NaN                           NaN   \n",
-       "2                                NaN                           NaN   \n",
-       "3                                NaN                           NaN   \n",
-       "4                                NaN                           NaN   \n",
-       "\n",
-       "   execution_time_time_kurtosis  execution_time_time_coefficient_variation   \n",
-       "0                           NaN                                        NaN  \\\n",
-       "1                           NaN                                        NaN   \n",
-       "2                           NaN                                        NaN   \n",
-       "3                           NaN                                        NaN   \n",
-       "4                           NaN                                        NaN   \n",
-       "\n",
-       "   execution_time_time_entropy  execution_time_time_skewness_hist   \n",
-       "0                          NaN                                NaN  \\\n",
-       "1                          NaN                                NaN   \n",
-       "2                          NaN                                NaN   \n",
-       "3                          NaN                                NaN   \n",
-       "4                          NaN                                NaN   \n",
-       "\n",
-       "   execution_time_time_kurtosis_hist  remaining_time_time_min   \n",
-       "0                                NaN                      NaN  \\\n",
-       "1                                NaN                      NaN   \n",
-       "2                                NaN                      NaN   \n",
-       "3                                NaN                      NaN   \n",
-       "4                                NaN                      NaN   \n",
-       "\n",
-       "   remaining_time_time_max  remaining_time_time_mean   \n",
-       "0                      NaN                       NaN  \\\n",
-       "1                      NaN                       NaN   \n",
-       "2                      NaN                       NaN   \n",
-       "3                      NaN                       NaN   \n",
-       "4                      NaN                       NaN   \n",
-       "\n",
-       "   remaining_time_time_median  remaining_time_time_mode   \n",
-       "0                         NaN                       NaN  \\\n",
-       "1                         NaN                       NaN   \n",
-       "2                         NaN                       NaN   \n",
-       "3                         NaN                       NaN   \n",
-       "4                         NaN                       NaN   \n",
-       "\n",
-       "   remaining_time_time_std  remaining_time_time_variance   \n",
-       "0                      NaN                           NaN  \\\n",
-       "1                      NaN                           NaN   \n",
-       "2                      NaN                           NaN   \n",
-       "3                      NaN                           NaN   \n",
-       "4                      NaN                           NaN   \n",
-       "\n",
-       "   remaining_time_time_q1  remaining_time_time_q3  remaining_time_time_iqr   \n",
-       "0                     NaN                     NaN                      NaN  \\\n",
-       "1                     NaN                     NaN                      NaN   \n",
-       "2                     NaN                     NaN                      NaN   \n",
-       "3                     NaN                     NaN                      NaN   \n",
-       "4                     NaN                     NaN                      NaN   \n",
-       "\n",
-       "   remaining_time_time_geometric_mean  remaining_time_time_geometric_std   \n",
-       "0                                 NaN                                NaN  \\\n",
-       "1                                 NaN                                NaN   \n",
-       "2                                 NaN                                NaN   \n",
-       "3                                 NaN                                NaN   \n",
-       "4                                 NaN                                NaN   \n",
-       "\n",
-       "   remaining_time_time_harmonic_mean  remaining_time_time_skewness   \n",
-       "0                                NaN                           NaN  \\\n",
-       "1                                NaN                           NaN   \n",
-       "2                                NaN                           NaN   \n",
-       "3                                NaN                           NaN   \n",
-       "4                                NaN                           NaN   \n",
-       "\n",
-       "   remaining_time_time_kurtosis  remaining_time_time_coefficient_variation   \n",
-       "0                           NaN                                        NaN  \\\n",
-       "1                           NaN                                        NaN   \n",
-       "2                           NaN                                        NaN   \n",
-       "3                           NaN                                        NaN   \n",
-       "4                           NaN                                        NaN   \n",
-       "\n",
-       "   remaining_time_time_entropy  remaining_time_time_skewness_hist   \n",
-       "0                          NaN                                NaN  \\\n",
-       "1                          NaN                                NaN   \n",
-       "2                          NaN                                NaN   \n",
-       "3                          NaN                                NaN   \n",
-       "4                          NaN                                NaN   \n",
-       "\n",
-       "   remaining_time_time_kurtosis_hist  within_day_time_min   \n",
-       "0                                NaN                  NaN  \\\n",
-       "1                                NaN                  NaN   \n",
-       "2                                NaN                  NaN   \n",
-       "3                                NaN                  NaN   \n",
-       "4                                NaN                  NaN   \n",
-       "\n",
-       "   within_day_time_max  within_day_time_mean  within_day_time_median   \n",
-       "0                  NaN                   NaN                     NaN  \\\n",
-       "1                  NaN                   NaN                     NaN   \n",
-       "2                  NaN                   NaN                     NaN   \n",
-       "3                  NaN                   NaN                     NaN   \n",
-       "4                  NaN                   NaN                     NaN   \n",
-       "\n",
-       "   within_day_time_mode  within_day_time_std  within_day_time_variance   \n",
-       "0                   NaN                  NaN                       NaN  \\\n",
-       "1                   NaN                  NaN                       NaN   \n",
-       "2                   NaN                  NaN                       NaN   \n",
-       "3                   NaN                  NaN                       NaN   \n",
-       "4                   NaN                  NaN                       NaN   \n",
-       "\n",
-       "   within_day_time_q1  within_day_time_q3  within_day_time_iqr   \n",
-       "0                 NaN                 NaN                  NaN  \\\n",
-       "1                 NaN                 NaN                  NaN   \n",
-       "2                 NaN                 NaN                  NaN   \n",
-       "3                 NaN                 NaN                  NaN   \n",
-       "4                 NaN                 NaN                  NaN   \n",
-       "\n",
-       "   within_day_time_geometric_mean  within_day_time_geometric_std   \n",
-       "0                             NaN                            NaN  \\\n",
-       "1                             NaN                            NaN   \n",
-       "2                             NaN                            NaN   \n",
-       "3                             NaN                            NaN   \n",
-       "4                             NaN                            NaN   \n",
-       "\n",
-       "   within_day_time_harmonic_mean  within_day_time_skewness   \n",
-       "0                            NaN                       NaN  \\\n",
-       "1                            NaN                       NaN   \n",
-       "2                            NaN                       NaN   \n",
-       "3                            NaN                       NaN   \n",
-       "4                            NaN                       NaN   \n",
-       "\n",
-       "   within_day_time_kurtosis  within_day_time_coefficient_variation   \n",
-       "0                       NaN                                    NaN  \\\n",
-       "1                       NaN                                    NaN   \n",
-       "2                       NaN                                    NaN   \n",
-       "3                       NaN                                    NaN   \n",
-       "4                       NaN                                    NaN   \n",
-       "\n",
-       "   within_day_time_entropy  within_day_time_skewness_hist   \n",
-       "0                      NaN                            NaN  \\\n",
-       "1                      NaN                            NaN   \n",
-       "2                      NaN                            NaN   \n",
-       "3                      NaN                            NaN   \n",
-       "4                      NaN                            NaN   \n",
-       "\n",
-       "   within_day_time_kurtosis_hist  \n",
-       "0                            NaN  \n",
-       "1                            NaN  \n",
-       "2                            NaN  \n",
-       "3                            NaN  \n",
-       "4                            NaN  "
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bpic_features = pd.read_csv(\"../data/34_bpic_features.csv\", index_col=None)\n",
-    "#bpic_features = pd.read_csv(\"../gedi/output/features/real_event_logs.csv\", index_col=None)\n",
-    "\n",
-    "#bpic_features = bpic_features.drop(['Unnamed: 0'], axis=1)\n",
-    "print(bpic_features.shape)\n",
-    "print(len(bpic_features), \" Event-Logs: \", bpic_features.sort_values('log')['log'].unique())\n",
-    "\n",
-    "#bpic_features.rename(columns={\"variant_entropy\":\"epa_variant_entropy\", \"normalized_variant_entropy\":\"epa_normalized_variant_entropy\", \"sequence_entropy\":\"epa_sequence_entropy\", \"normalized_sequence_entropy\":\"epa_normalized_sequence_entropy\", \"sequence_entropy_linear_forgetting\":\"epa_sequence_entropy_linear_forgetting\", \"normalized_sequence_entropy_linear_forgetting\":\"epa_normalized_sequence_entropy_linear_forgetting\", \"sequence_entropy_exponential_forgetting\":\"epa_sequence_entropy_exponential_forgetting\", \"normalized_sequence_entropy_exponential_forgetting\":\"epa_normalized_sequence_entropy_exponential_forgetting\"},\n",
-    "#          errors=\"raise\", inplace=True)\n",
-    "\n",
-    "bpic_features.head()\n",
-    "#bpic_features.to_csv(\"../data/34_bpic_features.csv\", index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "ef0df0b9",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['ratio_unique_traces_per_trace', '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']\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>log</th>\n",
-       "      <th>ratio_unique_traces_per_trace</th>\n",
-       "      <th>ratio_most_common_variant</th>\n",
-       "      <th>ratio_top_10_variants</th>\n",
-       "      <th>epa_normalized_variant_entropy</th>\n",
-       "      <th>epa_normalized_sequence_entropy</th>\n",
-       "      <th>epa_normalized_sequence_entropy_linear_forgetting</th>\n",
-       "      <th>epa_normalized_sequence_entropy_exponential_forgetting</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>BPIC15_2</td>\n",
-       "      <td>0.995192</td>\n",
-       "      <td>0.002404</td>\n",
-       "      <td>0.103365</td>\n",
-       "      <td>0.627973</td>\n",
-       "      <td>0.602371</td>\n",
-       "      <td>0.317217</td>\n",
-       "      <td>0.390473</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>BPI_Challenge_2018</td>\n",
-       "      <td>0.649570</td>\n",
-       "      <td>0.026981</td>\n",
-       "      <td>0.415371</td>\n",
-       "      <td>0.712079</td>\n",
-       "      <td>0.570688</td>\n",
-       "      <td>0.381612</td>\n",
-       "      <td>0.420362</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Receipt_WABO_CoSeLoG</td>\n",
-       "      <td>0.080893</td>\n",
-       "      <td>0.497211</td>\n",
-       "      <td>0.887029</td>\n",
-       "      <td>0.689363</td>\n",
-       "      <td>0.235532</td>\n",
-       "      <td>0.100603</td>\n",
-       "      <td>0.138113</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>BPIC15_3</td>\n",
-       "      <td>0.957417</td>\n",
-       "      <td>0.010646</td>\n",
-       "      <td>0.137686</td>\n",
-       "      <td>0.661781</td>\n",
-       "      <td>0.605676</td>\n",
-       "      <td>0.341521</td>\n",
-       "      <td>0.404934</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>BPI_Challenge_2019</td>\n",
-       "      <td>0.047562</td>\n",
-       "      <td>0.199758</td>\n",
-       "      <td>0.946368</td>\n",
-       "      <td>0.645530</td>\n",
-       "      <td>0.328029</td>\n",
-       "      <td>0.320185</td>\n",
-       "      <td>0.320282</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>RequestForPayment</td>\n",
-       "      <td>0.012925</td>\n",
-       "      <td>0.437264</td>\n",
-       "      <td>0.933488</td>\n",
-       "      <td>0.703735</td>\n",
-       "      <td>0.189048</td>\n",
-       "      <td>0.097572</td>\n",
-       "      <td>0.118744</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>PrepaidTravelCost</td>\n",
-       "      <td>0.096236</td>\n",
-       "      <td>0.271081</td>\n",
-       "      <td>0.822773</td>\n",
-       "      <td>0.723785</td>\n",
-       "      <td>0.317044</td>\n",
-       "      <td>0.184879</td>\n",
-       "      <td>0.214387</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>DomesticDeclarations</td>\n",
-       "      <td>0.009429</td>\n",
-       "      <td>0.439810</td>\n",
-       "      <td>0.950095</td>\n",
-       "      <td>0.696474</td>\n",
-       "      <td>0.164758</td>\n",
-       "      <td>0.085439</td>\n",
-       "      <td>0.104389</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>BPIC15_4</td>\n",
-       "      <td>0.996201</td>\n",
-       "      <td>0.002849</td>\n",
-       "      <td>0.102564</td>\n",
-       "      <td>0.652985</td>\n",
-       "      <td>0.603866</td>\n",
-       "      <td>0.355927</td>\n",
-       "      <td>0.412835</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>BPI_Challenge_2012</td>\n",
-       "      <td>0.333614</td>\n",
-       "      <td>0.262016</td>\n",
-       "      <td>0.686254</td>\n",
-       "      <td>0.708280</td>\n",
-       "      <td>0.423074</td>\n",
-       "      <td>0.226133</td>\n",
-       "      <td>0.275551</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>Hospital_log</td>\n",
-       "      <td>0.858268</td>\n",
-       "      <td>0.035871</td>\n",
-       "      <td>0.227472</td>\n",
-       "      <td>0.517443</td>\n",
-       "      <td>0.513032</td>\n",
-       "      <td>0.267825</td>\n",
-       "      <td>0.331672</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>BPIC15_5</td>\n",
-       "      <td>0.997405</td>\n",
-       "      <td>0.001730</td>\n",
-       "      <td>0.102076</td>\n",
-       "      <td>0.648702</td>\n",
-       "      <td>0.603260</td>\n",
-       "      <td>0.342410</td>\n",
-       "      <td>0.404580</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>CoSeLoG_WABO_2</td>\n",
-       "      <td>0.998450</td>\n",
-       "      <td>0.003101</td>\n",
-       "      <td>0.100775</td>\n",
-       "      <td>0.618455</td>\n",
-       "      <td>0.594035</td>\n",
-       "      <td>0.323233</td>\n",
-       "      <td>0.389858</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>Road_Traffic_Fine_Management_Process</td>\n",
-       "      <td>0.001536</td>\n",
-       "      <td>0.375620</td>\n",
-       "      <td>0.993104</td>\n",
-       "      <td>0.769353</td>\n",
-       "      <td>0.111932</td>\n",
-       "      <td>0.052586</td>\n",
-       "      <td>0.068442</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>BPI_Challenge_2017_Offer_log</td>\n",
-       "      <td>0.000372</td>\n",
-       "      <td>0.380626</td>\n",
-       "      <td>0.380626</td>\n",
-       "      <td>0.813479</td>\n",
-       "      <td>0.105130</td>\n",
-       "      <td>0.052672</td>\n",
-       "      <td>0.066000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>Sepsis_Cases_Event_Log</td>\n",
-       "      <td>0.805714</td>\n",
-       "      <td>0.033333</td>\n",
-       "      <td>0.274286</td>\n",
-       "      <td>0.695759</td>\n",
-       "      <td>0.522343</td>\n",
-       "      <td>0.219365</td>\n",
-       "      <td>0.299505</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>CoSeLoG_WABO_3</td>\n",
-       "      <td>0.949402</td>\n",
-       "      <td>0.011960</td>\n",
-       "      <td>0.145354</td>\n",
-       "      <td>0.654296</td>\n",
-       "      <td>0.596367</td>\n",
-       "      <td>0.278121</td>\n",
-       "      <td>0.356439</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>BPI_Challenge_2013_closed_problems</td>\n",
-       "      <td>0.123067</td>\n",
-       "      <td>0.331540</td>\n",
-       "      <td>0.840619</td>\n",
-       "      <td>0.705383</td>\n",
-       "      <td>0.310940</td>\n",
-       "      <td>0.286515</td>\n",
-       "      <td>0.288383</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>BPI_Challenge_2013_incidents</td>\n",
-       "      <td>0.200026</td>\n",
-       "      <td>0.232195</td>\n",
-       "      <td>0.794414</td>\n",
-       "      <td>0.717846</td>\n",
-       "      <td>0.404651</td>\n",
-       "      <td>0.391097</td>\n",
-       "      <td>0.391625</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>PermitLog</td>\n",
-       "      <td>0.209200</td>\n",
-       "      <td>0.135315</td>\n",
-       "      <td>0.757537</td>\n",
-       "      <td>0.733653</td>\n",
-       "      <td>0.420150</td>\n",
-       "      <td>0.137287</td>\n",
-       "      <td>0.215490</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>BPIC15_1</td>\n",
-       "      <td>0.975813</td>\n",
-       "      <td>0.006672</td>\n",
-       "      <td>0.121768</td>\n",
-       "      <td>0.652855</td>\n",
-       "      <td>0.610294</td>\n",
-       "      <td>0.270241</td>\n",
-       "      <td>0.363928</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>InternationalDeclarations</td>\n",
-       "      <td>0.116762</td>\n",
-       "      <td>0.212281</td>\n",
-       "      <td>0.811289</td>\n",
-       "      <td>0.758268</td>\n",
-       "      <td>0.339380</td>\n",
-       "      <td>0.145611</td>\n",
-       "      <td>0.193753</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>BPI_Challenge_2017</td>\n",
-       "      <td>0.505570</td>\n",
-       "      <td>0.033514</td>\n",
-       "      <td>0.531340</td>\n",
-       "      <td>0.741706</td>\n",
-       "      <td>0.461565</td>\n",
-       "      <td>0.231922</td>\n",
-       "      <td>0.290464</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>BPI2016_Complaints</td>\n",
-       "      <td>0.438053</td>\n",
-       "      <td>0.101770</td>\n",
-       "      <td>0.424779</td>\n",
-       "      <td>0.899497</td>\n",
-       "      <td>0.683796</td>\n",
-       "      <td>0.404685</td>\n",
-       "      <td>0.470116</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>BPI2016_Questions</td>\n",
-       "      <td>0.797427</td>\n",
-       "      <td>0.015650</td>\n",
-       "      <td>0.282311</td>\n",
-       "      <td>0.813468</td>\n",
-       "      <td>0.756132</td>\n",
-       "      <td>0.424910</td>\n",
-       "      <td>0.506118</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>BPI2016_Werkmap_Messages</td>\n",
-       "      <td>0.002882</td>\n",
-       "      <td>0.295803</td>\n",
-       "      <td>0.714106</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>BPI_Challenge_2013_open_problems</td>\n",
-       "      <td>0.131868</td>\n",
-       "      <td>0.217338</td>\n",
-       "      <td>0.769231</td>\n",
-       "      <td>0.702960</td>\n",
-       "      <td>0.276771</td>\n",
-       "      <td>0.262094</td>\n",
-       "      <td>0.263029</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>CoSeLoG_WABO_1</td>\n",
-       "      <td>0.977588</td>\n",
-       "      <td>0.009605</td>\n",
-       "      <td>0.119530</td>\n",
-       "      <td>0.646697</td>\n",
-       "      <td>0.601566</td>\n",
-       "      <td>0.292824</td>\n",
-       "      <td>0.376276</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>CoSeLoG_WABO_4</td>\n",
-       "      <td>0.992376</td>\n",
-       "      <td>0.002541</td>\n",
-       "      <td>0.106734</td>\n",
-       "      <td>0.644399</td>\n",
-       "      <td>0.597109</td>\n",
-       "      <td>0.373920</td>\n",
-       "      <td>0.422526</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>CoSeLoG_WABO_5</td>\n",
-       "      <td>0.985426</td>\n",
-       "      <td>0.004484</td>\n",
-       "      <td>0.112108</td>\n",
-       "      <td>0.642668</td>\n",
-       "      <td>0.592454</td>\n",
-       "      <td>0.346832</td>\n",
-       "      <td>0.401731</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>Detail_Change</td>\n",
-       "      <td>0.048444</td>\n",
-       "      <td>0.074944</td>\n",
-       "      <td>0.765056</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>Detail_Incident_Activity</td>\n",
-       "      <td>0.496847</td>\n",
-       "      <td>0.037455</td>\n",
-       "      <td>0.552836</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>Detail_Interaction</td>\n",
-       "      <td>0.000041</td>\n",
-       "      <td>0.787081</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33</th>\n",
-       "      <td>finale</td>\n",
-       "      <td>0.049345</td>\n",
-       "      <td>0.516594</td>\n",
-       "      <td>0.906332</td>\n",
-       "      <td>0.799120</td>\n",
-       "      <td>0.254066</td>\n",
-       "      <td>0.118478</td>\n",
-       "      <td>0.154576</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                     log  ratio_unique_traces_per_trace   \n",
-       "0                               BPIC15_2                       0.995192  \\\n",
-       "1                     BPI_Challenge_2018                       0.649570   \n",
-       "2                   Receipt_WABO_CoSeLoG                       0.080893   \n",
-       "3                               BPIC15_3                       0.957417   \n",
-       "4                     BPI_Challenge_2019                       0.047562   \n",
-       "5                      RequestForPayment                       0.012925   \n",
-       "6                      PrepaidTravelCost                       0.096236   \n",
-       "7                   DomesticDeclarations                       0.009429   \n",
-       "8                               BPIC15_4                       0.996201   \n",
-       "9                     BPI_Challenge_2012                       0.333614   \n",
-       "10                          Hospital_log                       0.858268   \n",
-       "11                              BPIC15_5                       0.997405   \n",
-       "12                        CoSeLoG_WABO_2                       0.998450   \n",
-       "13  Road_Traffic_Fine_Management_Process                       0.001536   \n",
-       "14          BPI_Challenge_2017_Offer_log                       0.000372   \n",
-       "15                Sepsis_Cases_Event_Log                       0.805714   \n",
-       "16                        CoSeLoG_WABO_3                       0.949402   \n",
-       "17    BPI_Challenge_2013_closed_problems                       0.123067   \n",
-       "18          BPI_Challenge_2013_incidents                       0.200026   \n",
-       "19                             PermitLog                       0.209200   \n",
-       "20                              BPIC15_1                       0.975813   \n",
-       "21             InternationalDeclarations                       0.116762   \n",
-       "22                    BPI_Challenge_2017                       0.505570   \n",
-       "23                    BPI2016_Complaints                       0.438053   \n",
-       "24                     BPI2016_Questions                       0.797427   \n",
-       "25              BPI2016_Werkmap_Messages                       0.002882   \n",
-       "26      BPI_Challenge_2013_open_problems                       0.131868   \n",
-       "27                        CoSeLoG_WABO_1                       0.977588   \n",
-       "28                        CoSeLoG_WABO_4                       0.992376   \n",
-       "29                        CoSeLoG_WABO_5                       0.985426   \n",
-       "30                         Detail_Change                       0.048444   \n",
-       "31              Detail_Incident_Activity                       0.496847   \n",
-       "32                    Detail_Interaction                       0.000041   \n",
-       "33                                finale                       0.049345   \n",
-       "\n",
-       "    ratio_most_common_variant  ratio_top_10_variants   \n",
-       "0                    0.002404               0.103365  \\\n",
-       "1                    0.026981               0.415371   \n",
-       "2                    0.497211               0.887029   \n",
-       "3                    0.010646               0.137686   \n",
-       "4                    0.199758               0.946368   \n",
-       "5                    0.437264               0.933488   \n",
-       "6                    0.271081               0.822773   \n",
-       "7                    0.439810               0.950095   \n",
-       "8                    0.002849               0.102564   \n",
-       "9                    0.262016               0.686254   \n",
-       "10                   0.035871               0.227472   \n",
-       "11                   0.001730               0.102076   \n",
-       "12                   0.003101               0.100775   \n",
-       "13                   0.375620               0.993104   \n",
-       "14                   0.380626               0.380626   \n",
-       "15                   0.033333               0.274286   \n",
-       "16                   0.011960               0.145354   \n",
-       "17                   0.331540               0.840619   \n",
-       "18                   0.232195               0.794414   \n",
-       "19                   0.135315               0.757537   \n",
-       "20                   0.006672               0.121768   \n",
-       "21                   0.212281               0.811289   \n",
-       "22                   0.033514               0.531340   \n",
-       "23                   0.101770               0.424779   \n",
-       "24                   0.015650               0.282311   \n",
-       "25                   0.295803               0.714106   \n",
-       "26                   0.217338               0.769231   \n",
-       "27                   0.009605               0.119530   \n",
-       "28                   0.002541               0.106734   \n",
-       "29                   0.004484               0.112108   \n",
-       "30                   0.074944               0.765056   \n",
-       "31                   0.037455               0.552836   \n",
-       "32                   0.787081               0.000000   \n",
-       "33                   0.516594               0.906332   \n",
-       "\n",
-       "    epa_normalized_variant_entropy  epa_normalized_sequence_entropy   \n",
-       "0                         0.627973                         0.602371  \\\n",
-       "1                         0.712079                         0.570688   \n",
-       "2                         0.689363                         0.235532   \n",
-       "3                         0.661781                         0.605676   \n",
-       "4                         0.645530                         0.328029   \n",
-       "5                         0.703735                         0.189048   \n",
-       "6                         0.723785                         0.317044   \n",
-       "7                         0.696474                         0.164758   \n",
-       "8                         0.652985                         0.603866   \n",
-       "9                         0.708280                         0.423074   \n",
-       "10                        0.517443                         0.513032   \n",
-       "11                        0.648702                         0.603260   \n",
-       "12                        0.618455                         0.594035   \n",
-       "13                        0.769353                         0.111932   \n",
-       "14                        0.813479                         0.105130   \n",
-       "15                        0.695759                         0.522343   \n",
-       "16                        0.654296                         0.596367   \n",
-       "17                        0.705383                         0.310940   \n",
-       "18                        0.717846                         0.404651   \n",
-       "19                        0.733653                         0.420150   \n",
-       "20                        0.652855                         0.610294   \n",
-       "21                        0.758268                         0.339380   \n",
-       "22                        0.741706                         0.461565   \n",
-       "23                        0.899497                         0.683796   \n",
-       "24                        0.813468                         0.756132   \n",
-       "25                        0.000000                         0.000000   \n",
-       "26                        0.702960                         0.276771   \n",
-       "27                        0.646697                         0.601566   \n",
-       "28                        0.644399                         0.597109   \n",
-       "29                        0.642668                         0.592454   \n",
-       "30                             NaN                              NaN   \n",
-       "31                             NaN                              NaN   \n",
-       "32                             NaN                              NaN   \n",
-       "33                        0.799120                         0.254066   \n",
-       "\n",
-       "    epa_normalized_sequence_entropy_linear_forgetting   \n",
-       "0                                            0.317217  \\\n",
-       "1                                            0.381612   \n",
-       "2                                            0.100603   \n",
-       "3                                            0.341521   \n",
-       "4                                            0.320185   \n",
-       "5                                            0.097572   \n",
-       "6                                            0.184879   \n",
-       "7                                            0.085439   \n",
-       "8                                            0.355927   \n",
-       "9                                            0.226133   \n",
-       "10                                           0.267825   \n",
-       "11                                           0.342410   \n",
-       "12                                           0.323233   \n",
-       "13                                           0.052586   \n",
-       "14                                           0.052672   \n",
-       "15                                           0.219365   \n",
-       "16                                           0.278121   \n",
-       "17                                           0.286515   \n",
-       "18                                           0.391097   \n",
-       "19                                           0.137287   \n",
-       "20                                           0.270241   \n",
-       "21                                           0.145611   \n",
-       "22                                           0.231922   \n",
-       "23                                           0.404685   \n",
-       "24                                           0.424910   \n",
-       "25                                           0.000000   \n",
-       "26                                           0.262094   \n",
-       "27                                           0.292824   \n",
-       "28                                           0.373920   \n",
-       "29                                           0.346832   \n",
-       "30                                                NaN   \n",
-       "31                                                NaN   \n",
-       "32                                                NaN   \n",
-       "33                                           0.118478   \n",
-       "\n",
-       "    epa_normalized_sequence_entropy_exponential_forgetting  \n",
-       "0                                            0.390473       \n",
-       "1                                            0.420362       \n",
-       "2                                            0.138113       \n",
-       "3                                            0.404934       \n",
-       "4                                            0.320282       \n",
-       "5                                            0.118744       \n",
-       "6                                            0.214387       \n",
-       "7                                            0.104389       \n",
-       "8                                            0.412835       \n",
-       "9                                            0.275551       \n",
-       "10                                           0.331672       \n",
-       "11                                           0.404580       \n",
-       "12                                           0.389858       \n",
-       "13                                           0.068442       \n",
-       "14                                           0.066000       \n",
-       "15                                           0.299505       \n",
-       "16                                           0.356439       \n",
-       "17                                           0.288383       \n",
-       "18                                           0.391625       \n",
-       "19                                           0.215490       \n",
-       "20                                           0.363928       \n",
-       "21                                           0.193753       \n",
-       "22                                           0.290464       \n",
-       "23                                           0.470116       \n",
-       "24                                           0.506118       \n",
-       "25                                           0.000000       \n",
-       "26                                           0.263029       \n",
-       "27                                           0.376276       \n",
-       "28                                           0.422526       \n",
-       "29                                           0.401731       \n",
-       "30                                                NaN       \n",
-       "31                                                NaN       \n",
-       "32                                                NaN       \n",
-       "33                                           0.154576       "
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bpic_stats = bpic_features.describe().transpose()\n",
-    "normalized_feature_names = bpic_stats[(bpic_stats['min']>=0)&(bpic_stats['max']<=1)].index.to_list() \n",
-    "normalized_feature_names = ['ratio_unique_traces_per_trace', 'ratio_most_common_variant', \n",
-    "                            'ratio_top_10_variants', 'epa_normalized_variant_entropy', 'epa_normalized_sequence_entropy', \n",
-    "                            'epa_normalized_sequence_entropy_linear_forgetting', 'epa_normalized_sequence_entropy_exponential_forgetting']\n",
-    "print(normalized_feature_names)\n",
-    "bpic_features[['log']+normalized_feature_names]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "44909860",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "21\n",
-      "Saved experiment config in ../config_files/algorithm/34_bpic_features/generator_34bpic_2objectives_enself_rt10v.json\n",
-      "Saved experiment config in ../config_files/algorithm/34_bpic_features/generator_34bpic_2objectives_enseef_rmcv.json\n",
-      "Saved experiment config in ../config_files/algorithm/34_bpic_features/generator_34bpic_2objectives_ense_enself.json\n",
-      "Saved experiment config in ../config_files/algorithm/34_bpic_features/generator_34bpic_2objectives_enve_rt10v.json\n",
-      "Saved experiment config in ../config_files/algorithm/34_bpic_features/generator_34bpic_2objectives_ense_rt10v.json\n",
-      "Saved experiment config in ../config_files/algorithm/34_bpic_features/generator_34bpic_2objectives_ense_enseef.json\n",
-      "Saved experiment config in ../config_files/algorithm/34_bpic_features/generator_34bpic_2objectives_enself_rmcv.json\n",
-      "Saved experiment config in ../config_files/algorithm/34_bpic_features/generator_34bpic_2objectives_rmcv_rutpt.json\n",
-      "Saved experiment config in ../config_files/algorithm/34_bpic_features/generator_34bpic_2objectives_enseef_enve.json\n",
-      "Saved experiment config in ../config_files/algorithm/34_bpic_features/generator_34bpic_2objectives_enve_rmcv.json\n",
-      "Saved experiment config in ../config_files/algorithm/34_bpic_features/generator_34bpic_2objectives_ense_rmcv.json\n",
-      "Saved experiment config in ../config_files/algorithm/34_bpic_features/generator_34bpic_2objectives_enseef_rutpt.json\n",
-      "Saved experiment config in ../config_files/algorithm/34_bpic_features/generator_34bpic_2objectives_enself_enve.json\n",
-      "Saved experiment config in ../config_files/algorithm/34_bpic_features/generator_34bpic_2objectives_rmcv_rt10v.json\n",
-      "Saved experiment config in ../config_files/algorithm/34_bpic_features/generator_34bpic_2objectives_enself_rutpt.json\n",
-      "Saved experiment config in ../config_files/algorithm/34_bpic_features/generator_34bpic_2objectives_enseef_enself.json\n",
-      "Saved experiment config in ../config_files/algorithm/34_bpic_features/generator_34bpic_2objectives_enseef_rt10v.json\n",
-      "Saved experiment config in ../config_files/algorithm/34_bpic_features/generator_34bpic_2objectives_ense_enve.json\n",
-      "Saved experiment config in ../config_files/algorithm/34_bpic_features/generator_34bpic_2objectives_enve_rutpt.json\n",
-      "Saved experiment config in ../config_files/algorithm/34_bpic_features/generator_34bpic_2objectives_rt10v_rutpt.json\n",
-      "Saved experiment config in ../config_files/algorithm/34_bpic_features/generator_34bpic_2objectives_ense_rutpt.json\n",
-      "None\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Features between 0 and 1: \n",
-    "def write_generator_bpic_experiment(objectives, n_para_obj=2):\n",
-    "    parameters_o = \"objectives, \"\n",
-    "    experiments = eval(f\"[exp for exp in list(itertools.product({(parameters_o*n_para_obj)[:-2]})) if exp[0]!=exp[1]]\")\n",
-    "    experiments = list(set([tuple(sorted(exp)) for exp in experiments]))\n",
-    "    for exp in experiments:\n",
-    "        experiment_path = os.path.join('..','data', '34_bpic_features')\n",
-    "        os.makedirs(experiment_path, exist_ok=True)\n",
-    "        experiment_path = os.path.join(experiment_path, f\"34bpic_{len(exp)}objectives_{abbrev_obj_keys(exp)}.csv\") \n",
-    "\n",
-    "\n",
-    "        first_dir = os.path.split(experiment_path[3:])[-1].replace(\".csv\",\"\")\n",
-    "        second_dir = first_dir.replace(\"grid_\",\"\").replace(\"objectives\",\"\")\n",
-    "\n",
-    "        experiment = [\n",
-    "          {\n",
-    "            'pipeline_step': 'event_logs_generation',\n",
-    "            'output_path':'output/generated',\n",
-    "            'generator_params': {\n",
-    "              \"experiment\": {\"input_path\": \"data/34_bpic_features.csv\",\n",
-    "                \"objectives\": exp},\n",
-    "              'config_space': {\n",
-    "                'mode': [5, 20],\n",
-    "                'sequence': [0.01, 1],\n",
-    "                'choice': [0.01, 1],\n",
-    "                'parallel': [0.01, 1],\n",
-    "                'loop': [0.01, 1],\n",
-    "                'silent': [0.01, 1],\n",
-    "                'lt_dependency': [0.01, 1],\n",
-    "                'num_traces': [10, 10001],\n",
-    "                'duplicate': [0],\n",
-    "                'or': [0]\n",
-    "              },\n",
-    "              'n_trials': 200\n",
-    "            }\n",
-    "          },\n",
-    "          {\n",
-    "            'pipeline_step': 'feature_extraction',\n",
-    "            'input_path': os.path.join('output', 'features', 'generated', '34_bpic_features', second_dir),\n",
-    "            'feature_params': {'feature_set':['simple_stats', 'trace_length', 'trace_variant', 'activities', 'start_activities', 'end_activities', 'eventropies', 'epa_based']},\n",
-    "            'output_path': 'output/plots',\n",
-    "            'real_eventlog_path': 'data/34_bpic_features.csv',\n",
-    "            'plot_type': 'boxplot'\n",
-    "      }\n",
-    "        ]\n",
-    "\n",
-    "        output_path = os.path.join('..', 'config_files','algorithm','34_bpic_features')\n",
-    "        os.makedirs(output_path, exist_ok=True)\n",
-    "        output_path = os.path.join(output_path, f'generator_{os.path.split(experiment_path)[-1].split(\".\")[0]}.json') \n",
-    "\n",
-    "        with open(output_path, 'w') as f:\n",
-    "            json.dump(experiment, f, ensure_ascii=False)\n",
-    "        print(f\"Saved experiment config in {output_path}\")\n",
-    "        return experiment\n",
-    "\n",
-    "\n",
-    "def create_objectives_grid(objectives, n_para_obj=2):\n",
-    "    parameters_o = \"objectives, \"\n",
-    "    experiments = eval(f\"[exp for exp in list(itertools.product({(parameters_o*n_para_obj)[:-2]})) if exp[0]!=exp[1]]\")\n",
-    "    experiments = list(set([tuple(sorted(exp)) for exp in experiments]))\n",
-    "    print(len(experiments))\n",
-    "    \n",
-    "    for exp in experiments:\n",
-    "        write_generator_bpic_experiment(objectives=exp)\n",
-    "        \n",
-    "exp_test = create_objectives_grid(normalized_feature_names, n_para_obj=2)        \n",
-    "print(exp_test)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b07e9753",
-   "metadata": {},
-   "source": [
-    "## Single objective from real logs\n",
-    "(Feature selection)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "d759a677",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "7 experiments:  [('epa_normalized_sequence_entropy_exponential_forgetting',), ('epa_normalized_variant_entropy',), ('ratio_top_10_variants',), ('epa_normalized_sequence_entropy',), ('epa_normalized_sequence_entropy_linear_forgetting',), ('ratio_most_common_variant',), ('ratio_unique_traces_per_trace',)]\n",
-      "11\n",
-      "Saved experiment in ../data/grid_experiments/grid_1objectives_enseef.csv\n",
-      "Saved experiment config in ../config_files/algorithm/grid_experiments/generator_grid_1objectives_enseef.json\n",
-      "Saved experiment in ../data/grid_experiments/grid_1objectives_enve.csv\n",
-      "Saved experiment config in ../config_files/algorithm/grid_experiments/generator_grid_1objectives_enve.json\n",
-      "Saved experiment in ../data/grid_experiments/grid_1objectives_rt10v.csv\n",
-      "Saved experiment config in ../config_files/algorithm/grid_experiments/generator_grid_1objectives_rt10v.json\n",
-      "Saved experiment in ../data/grid_experiments/grid_1objectives_ense.csv\n",
-      "Saved experiment config in ../config_files/algorithm/grid_experiments/generator_grid_1objectives_ense.json\n",
-      "Saved experiment in ../data/grid_experiments/grid_1objectives_enself.csv\n",
-      "Saved experiment config in ../config_files/algorithm/grid_experiments/generator_grid_1objectives_enself.json\n",
-      "Saved experiment in ../data/grid_experiments/grid_1objectives_rmcv.csv\n",
-      "Saved experiment config in ../config_files/algorithm/grid_experiments/generator_grid_1objectives_rmcv.json\n",
-      "Saved experiment in ../data/grid_experiments/grid_1objectives_rutpt.csv\n",
-      "Saved experiment config in ../config_files/algorithm/grid_experiments/generator_grid_1objectives_rutpt.json\n",
-      "None\n"
-     ]
-    }
-   ],
-   "source": [
-    "def write_single_objective_experiment(experiment_path, objectives=[\"ratio_top_20_variants\", \"epa_normalized_sequence_entropy_linear_forgetting\"]):\n",
-    "    first_dir = os.path.split(experiment_path[3:])[-1].replace(\".csv\",\"\")\n",
-    "    second_dir = first_dir.replace(\"grid_\",\"\").replace(\"objectives\",\"\")\n",
-    "\n",
-    "    experiment = [\n",
-    "      {\n",
-    "        'pipeline_step': 'event_logs_generation',\n",
-    "        'output_path':os.path.join('output','generated', 'grid_1obj'),\n",
-    "        'generator_params': {\n",
-    "          \"experiment\": {\"input_path\": experiment_path[3:],\n",
-    "            \"objectives\": objectives},\n",
-    "          'config_space': {\n",
-    "            'mode': [5, 20],\n",
-    "            'sequence': [0.01, 1],\n",
-    "            'choice': [0.01, 1],\n",
-    "            'parallel': [0.01, 1],\n",
-    "            'loop': [0.01, 1],\n",
-    "            'silent': [0.01, 1],\n",
-    "            'lt_dependency': [0.01, 1],\n",
-    "            'num_traces': [10, 10001],\n",
-    "            'duplicate': [0],\n",
-    "            'or': [0]\n",
-    "          },\n",
-    "          'n_trials': 200\n",
-    "        }\n",
-    "      },\n",
-    "      {\n",
-    "        'pipeline_step': 'feature_extraction',\n",
-    "        'input_path': os.path.join('output','features', 'generated', 'grid_1obj', first_dir, second_dir),\n",
-    "        'feature_params': {'feature_set':['simple_stats', 'trace_length', 'trace_variant', 'activities', 'start_activities', 'end_activities', 'eventropies', 'epa_based']},\n",
-    "        'output_path': 'output/plots',\n",
-    "        'real_eventlog_path': 'data/34_bpic_features.csv',\n",
-    "        'plot_type': 'boxplot'\n",
-    "  }\n",
-    "    ]\n",
-    "\n",
-    "    #print(\"EXPERIMENT:\", experiment)\n",
-    "    output_path = os.path.join('..', 'config_files','algorithm','grid_experiments')\n",
-    "    os.makedirs(output_path, exist_ok=True)\n",
-    "    output_path = os.path.join(output_path, f'generator_{os.path.split(experiment_path)[-1].split(\".\")[0]}.json') \n",
-    "    with open(output_path, 'w') as f:\n",
-    "        json.dump(experiment, f, ensure_ascii=False)\n",
-    "    print(f\"Saved experiment config in {output_path}\")\n",
-    "    \n",
-    "    return experiment\n",
-    "\n",
-    "def create_objectives_grid(objectives, n_para_obj=2):\n",
-    "    parameters_o = \"objectives, \"\n",
-    "    if n_para_obj==1:\n",
-    "        experiments = [[exp] for exp in objectives]\n",
-    "    else:\n",
-    "        experiments = eval(f\"[exp for exp in list(itertools.product({(parameters_o*n_para_obj)[:-2]})) if exp[0]!=exp[1]]\")\n",
-    "    experiments = list(set([tuple(sorted(exp)) for exp in experiments]))\n",
-    "    print(len(experiments), \"experiments: \", experiments)\n",
-    "    \n",
-    "    parameters = \"np.around(np.arange(0, 1.1,0.1),2), \"\n",
-    "    tasks = eval(f\"list(itertools.product({(parameters*n_para_obj)[:-2]}))\")\n",
-    "    tasks = [(f'task_{i+1}',)+task for i, task in enumerate(tasks)]\n",
-    "    print(len(tasks))\n",
-    "    for exp in experiments:\n",
-    "        df = pd.DataFrame(data=tasks, columns=[\"task\", *exp])\n",
-    "        experiment_path = os.path.join('..','data', 'grid_experiments')\n",
-    "        os.makedirs(experiment_path, exist_ok=True)\n",
-    "        experiment_path = os.path.join(experiment_path, f\"grid_{len(df.columns)-1}objectives_{abbrev_obj_keys(exp)}.csv\") \n",
-    "        df.to_csv(experiment_path, index=False)\n",
-    "        print(f\"Saved experiment in {experiment_path}\")\n",
-    "        write_single_objective_experiment(experiment_path, objectives=exp)\n",
-    "    #df.to_csv(f\"../data/grid_{}objectives_{abbrev_obj_keys(objectives.tolist())}.csv\" ,index=False)\n",
-    "        \n",
-    "exp_test = create_objectives_grid(normalized_feature_names, n_para_obj=1)        \n",
-    "print(exp_test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f9886f44",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.19"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/notebooks/gedi_figs9and10_consistency.ipynb b/notebooks/gedi_figs9and10_consistency.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..58391df6a0b42d8ed1fe5fe8fa0a0aeddbb76248
--- /dev/null
+++ b/notebooks/gedi_figs9and10_consistency.ipynb
@@ -0,0 +1,505 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "1768477d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from scipy import spatial\n",
+    "from sklearn.metrics.pairwise import cosine_similarity\n",
+    "TEST='pearsonr'#'kendalltau', 'pearsonr'\n",
+    "DATA_SOURCE = 'BaselineED' #'BaselineED', 'GenBaselineED', 'GenED'\n",
+    "IMPUTE = False #If False Nan lines are dropped\n",
+    "\n",
+    "paper_feat_columns = [\"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'] \n",
+    "paper_metrics_columns = ['log', 'fitness_heu', 'precision_heu',\n",
+    "           'fscore_heu', 'size_heu', 'cfc_heu', 'fitness_ilp', 'precision_ilp', 'fscore_ilp',\n",
+    "           'size_ilp','cfc_ilp', 'fitness_imf', 'precision_imf', 'fscore_imf', 'size_imf', 'cfc_imf']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d3b7f2d1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pearsonr_BaselineED_nanDropped\n"
+     ]
+    }
+   ],
+   "source": [
+    "def get_output_file_name(test, data_source, impute):    \n",
+    "    #print(data_source)\n",
+    "    impute = 'imputed' if impute else 'nanDropped'\n",
+    "    return (\"_\".join([test, data_source, impute]))\n",
+    "print(get_output_file_name(TEST, DATA_SOURCE, IMPUTE))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6594d6b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## LOAD FEATURE AND METRICS FILES\n",
+    "def load_data(data_source, content):\n",
+    "    path = f\"../data/{data_source}.csv\" \n",
+    "    #print(\"Path: \", path)\n",
+    "    data = pd.read_csv(path).sort_values('log')\n",
+    "    if data_source == 'GenBaselineED_feat':\n",
+    "        data['log']=data.apply(lambda x: \"Gen\"+x['log'], axis=1)\n",
+    "    elif data_source == 'GenBaselineED_bench':\n",
+    "            data['log']=data.apply(lambda x: \"Gen\"+x['log'].replace(\"genEL\",\"\").rsplit(\"_\",7)[0], axis=1)\n",
+    "    return data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7428d805",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### INSTANCE SELECTION: NULLS OR IMPUTATION?\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "\n",
+    "def clean_data(fd_pdm, impute=False, feat_columns=paper_feat_columns, metric_columns=paper_metrics_columns):\n",
+    "    num_cols = fd_pdm.convert_dtypes().select_dtypes(exclude=['string']).columns\n",
+    "    str_cols = fd_pdm.convert_dtypes().select_dtypes(include=['string']).columns\n",
+    "\n",
+    "    imputer = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
+    "    imputer.fit(fd_pdm.drop(str_cols, axis=1))\n",
+    "    imp_df = imputer.transform(fd_pdm.drop(str_cols, axis=1))\n",
+    "    imp_df = pd.DataFrame(imp_df, columns=num_cols)\n",
+    "    imp_df['log'] = fd_pdm['log']\n",
+    "    #print(\"Imputed dataset:\" ,imp_df.shape)\n",
+    "\n",
+    "    ft_pdm_no_nans = fd_pdm.copy()\n",
+    "    ft_pdm_no_nans = ft_pdm_no_nans.dropna()\n",
+    "    ft_pdm_no_nans['log'] = fd_pdm['log']\n",
+    "    #print(\"No nan's dataset:\" ,ft_pdm_no_nans.shape)\n",
+    "    #print(len(ft_pdm_no_nans[ft_pdm_no_nans['source']==DATA_SOURCE]['log']))\n",
+    "    #print(\"FT_COL: \", feat_columns)\n",
+    "    #print(\"M_COL: \", metric_columns)\n",
+    "    \n",
+    "    if IMPUTE:\n",
+    "        benchmarked_ft = imp_df[feat_columns]\n",
+    "        benchmarked_pd = imp_df[metric_columns]\n",
+    "    else:\n",
+    "        benchmarked_ft = ft_pdm_no_nans[feat_columns]\n",
+    "        benchmarked_pd = ft_pdm_no_nans[metric_columns]\n",
+    "    return benchmarked_ft, benchmarked_pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "07370d54",
+   "metadata": {},
+   "source": [
+    "## Statistical test: Is there a statistical significant relation between feature similarity and performance metrics?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "14e72f71",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.stats import spearmanr\n",
+    "from scipy.stats import kendalltau\n",
+    "from scipy.stats import pearsonr\n",
+    "from numpy import isnan\n",
+    "\n",
+    "import sys\n",
+    "import os\n",
+    "sys.path.append(os.path.dirname(\"../gedi/utils/io_helpers.py\"))\n",
+    "from io_helpers import get_keys_abbreviation\n",
+    "\n",
+    "def statistical_test(feature_source, bench_source, test, impute=False, p_thresh=0.05, focus='stat'):\n",
+    "    ft = load_data(feature_source, 'feat')\n",
+    "    #ft['log']=ft.apply(lambda x: x['log'].replace(\"Gen\",\"\"), axis=1)\n",
+    "    #paper_feat_columns = [\"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'] \n",
+    "    #ft= ft[paper_feat_columns]\n",
+    "    #print(ft.shape)\n",
+    "    #print(\"Feature: \", ft['log'].tolist())\n",
+    "\n",
+    "\n",
+    "    ben = load_data(bench_source, 'bench')\n",
+    "    ben['log']=ben.apply(lambda x: x['log'].replace(\"Gen\",\"\"), axis=1)\n",
+    "\n",
+    "    paper_metrics_columns = ['log', 'fitness_heu', 'precision_heu',\n",
+    "           'fscore_heu', 'size_heu', 'cfc_heu', 'fitness_ilp', 'precision_ilp', 'fscore_ilp',\n",
+    "           'size_ilp','cfc_ilp', 'fitness_imf', 'precision_imf', 'fscore_imf', 'size_imf', 'cfc_imf']\n",
+    "\n",
+    "    #ben = ben[paper_metrics_columns]\n",
+    "    #print(ben.shape)\n",
+    "    #print(\"Bench: \", ben['log'].tolist())\n",
+    "    fd_pdm = pd.merge(ft, ben, on=['log'], how='inner').reset_index(drop=True)#.reindex(both_df.index)\n",
+    "\n",
+    "    ## DROP DUPLICATES\n",
+    "    fd_pdm = fd_pdm.reset_index(drop=True)\n",
+    "    fd_pdm = fd_pdm.T.drop_duplicates().T\n",
+    "    #print(fd_pdm.shape)\n",
+    "    fd_pdm['log'].unique()\n",
+    "    \n",
+    "    #print(fd_pdm.columns)\n",
+    "    benchmark_ft, benchmark_pd = clean_data(fd_pdm, impute, paper_feat_columns, paper_metrics_columns)\n",
+    "    \n",
+    "    #print(benchmark_ft.shape, benchmark_pd.shape)\n",
+    "\n",
+    "    benchmarked_ft_plot = benchmark_ft.copy()\n",
+    "    benchmarked_pdm_plot = benchmark_pd.copy()\n",
+    "\n",
+    "    #benchmarked_ft = benchmarked_ft.head(10)\n",
+    "    #benchmarked_pdm = benchmarked_pdm.head(10)\n",
+    "    print(DATA_SOURCE, benchmarked_ft_plot.shape, benchmarked_pdm_plot.shape)\n",
+    "\n",
+    "    tmp = list(benchmarked_ft_plot.columns[1:-1])\n",
+    "    df_tmp = pd.DataFrame(index=benchmarked_pdm_plot.columns[1:-1], columns=tmp)\n",
+    "    #print(\"Benchmark_pdm:\", benchmarked_pdm.columns[1:-1])\n",
+    "    #print (\"Benchmark_ft:\", tmp)\n",
+    "\n",
+    "    for feature in benchmarked_ft_plot.columns:\n",
+    "        if feature != 'log' and feature != 'source':\n",
+    "            for metric in benchmarked_pdm_plot.columns:\n",
+    "                if metric != 'log' and metric != 'source':\n",
+    "                    #print(feature, benchmarked_pdm.columns[1])\n",
+    "                    stat, p = eval(f\"{test}(benchmarked_ft_plot[feature], benchmarked_pdm_plot[metric])\")   \n",
+    "                    #print(feature, metric, p, p <= 0.05)\n",
+    "                    df_tmp.loc[metric, feature] = eval(focus)*(1.0 if (p <= p_thresh) else 0.0)\n",
+    "\n",
+    "    feature_keys = get_keys_abbreviation(df_tmp.columns).split(\"_\")\n",
+    "    #print(feature_keys)\n",
+    "    df_tmp.columns=feature_keys\n",
+    "    print(\"Direct\", test, feature_source)\n",
+    "    # df_tmp[pd.isnan()]\n",
+    "    return df_tmp\n",
+    "#df_tmp = statistical_test(DATA_SOURCE+\"_feat\", \"Gen\"+DATA_SOURCE+\"_bench\", TEST, IMPUTE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "5f188aca",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "BaselineED (19, 8) (19, 16)\n",
+      "Direct pearsonr BaselineED_feat\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/andreamaldonado/miniconda3/envs/tag/lib/python3.9/site-packages/seaborn/matrix.py:202: RuntimeWarning: All-NaN slice encountered\n",
+      "  vmin = np.nanmin(calc_data)\n",
+      "/Users/andreamaldonado/miniconda3/envs/tag/lib/python3.9/site-packages/seaborn/matrix.py:207: RuntimeWarning: All-NaN slice encountered\n",
+      "  vmax = np.nanmax(calc_data)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "../output/plots/pdm_pearsonr_BaselineEDFeat_GenBaselineEDBench_corr_nanDropped\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 820x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_stat_test(test_results, feature_source, bench_source, test, impute, mask=True, cbar=True, ylabels=True, name_suffix=\"\"):\n",
+    "    feature_source = feature_source.split(\"_\")[0]\n",
+    "    bench_source = bench_source.split(\"_\")[0]\n",
+    "    cbar_w = 1.2 if cbar else 0\n",
+    "    ylabels_w = 1.5 if ylabels else 0\n",
+    "    plt.subplots(figsize=(6+cbar_w+ylabels,5))\n",
+    "    if feature_source==bench_source:\n",
+    "        data_source = feature_source\n",
+    "    else:\n",
+    "        data_source = f\"{feature_source}Feat_{bench_source}Bench\"\n",
+    "    sns.heatmap(test_results.fillna(0), annot=True, cmap=\"viridis\", annot_kws={\"size\": 14}, vmin=-1, vmax=1, cbar=cbar )\n",
+    "    ax = plt.gca()\n",
+    "    if mask:\n",
+    "        sns.heatmap(test_results.fillna(0), mask=test_results.fillna(0)!=0, cmap=\"Greys\", annot=False, cbar=False, ax=ax)\n",
+    "\n",
+    "    #ax.set_title(\"P-values of features leading to process discovery metrics\", fontsize=15)\n",
+    "    cax = ax.figure.axes[-1]\n",
+    "    cax.tick_params(labelsize=14)\n",
+    "\n",
+    "    if not ylabels:\n",
+    "        ax.axes.get_yaxis().set_visible(False)\n",
+    "        \n",
+    "    plt.yticks(fontsize=16)\n",
+    "    plt.xticks(fontsize=16)\n",
+    "    plt.xticks(rotation=-30)\n",
+    "\n",
+    "    plt.tight_layout()\n",
+    "    output_path = f\"../output/plots/pdm_{get_output_file_name(test, data_source+name_suffix, impute)}\"\n",
+    "    print(output_path)\n",
+    "    plt.savefig(output_path, dpi=300)\n",
+    "\n",
+    "masked_results = statistical_test(DATA_SOURCE+\"_feat\", \"Gen\"+DATA_SOURCE+\"_bench\", TEST, IMPUTE, p_thresh=1)\n",
+    "plot_stat_test(masked_results, DATA_SOURCE+\"_feat\", \"Gen\"+DATA_SOURCE+\"_bench\", TEST, IMPUTE, name_suffix=\"_corr\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1d6423db",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "BaselineED (14, 8) (14, 16)\n",
+      "Direct pearsonr BaselineED_feat\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/andreamaldonado/miniconda3/envs/tag/lib/python3.9/site-packages/seaborn/matrix.py:202: RuntimeWarning: All-NaN slice encountered\n",
+      "  vmin = np.nanmin(calc_data)\n",
+      "/Users/andreamaldonado/miniconda3/envs/tag/lib/python3.9/site-packages/seaborn/matrix.py:207: RuntimeWarning: All-NaN slice encountered\n",
+      "  vmax = np.nanmax(calc_data)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "../output/plots/pdm_pearsonr_BaselineED_corr_nanDropped\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 820x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "masked_results = statistical_test(DATA_SOURCE+\"_feat\", DATA_SOURCE+\"_bench\", TEST, IMPUTE, p_thresh=1)\n",
+    "plot_stat_test(masked_results, DATA_SOURCE+\"_feat\", DATA_SOURCE+\"_bench\", TEST, IMPUTE, name_suffix=\"_corr\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d0a9ddb2",
+   "metadata": {},
+   "source": [
+    "## Figure 9: Correlation differences"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "3d381199",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "BaselineED (19, 8) (19, 16)\n",
+      "Direct pearsonr BaselineED_feat\n",
+      "BaselineED (14, 8) (14, 16)\n",
+      "Direct pearsonr BaselineED_feat\n",
+      "pearsonr\n",
+      "../output/plots/pdm_pearsonr_BaselineEDFeat_GenBaselineEDBench_corrDiff_nanDropped\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "test='pearsonr'\n",
+    "remark6 = statistical_test(DATA_SOURCE+\"_feat\", \"Gen\"+DATA_SOURCE+\"_bench\", test, IMPUTE, p_thresh=1)\n",
+    "baselineED = statistical_test(DATA_SOURCE+\"_feat\", DATA_SOURCE+\"_bench\", test, IMPUTE, p_thresh=1)\n",
+    "reality_check = remark6.abs().subtract(baselineED.abs(), fill_value=0) \n",
+    "reality_check = reality_check.astype(float).round(2)\n",
+    "print(test)\n",
+    "plot_stat_test(reality_check, DATA_SOURCE+\"_feat\", \"Gen\"+DATA_SOURCE+\"_bench\", test, IMPUTE, mask=False, cbar=False, name_suffix=\"_corrDiff\") "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "0d82487f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "BaselineED (19, 8) (19, 16)\n",
+      "Direct kendalltau BaselineED_feat\n",
+      "BaselineED (14, 8) (14, 16)\n",
+      "Direct kendalltau BaselineED_feat\n",
+      "kendalltau\n",
+      "../output/plots/pdm_kendalltau_BaselineEDFeat_GenBaselineEDBench_corrDiff_nanDropped\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "test = 'kendalltau'\n",
+    "remark6_k = statistical_test(DATA_SOURCE+\"_feat\", \"Gen\"+DATA_SOURCE+\"_bench\", test, IMPUTE, p_thresh=1)\n",
+    "baselineED_k = statistical_test(DATA_SOURCE+\"_feat\", DATA_SOURCE+\"_bench\", test, IMPUTE, p_thresh=1)\n",
+    "reality_check_k = remark6_k.abs().subtract(baselineED_k.abs(), fill_value=0)\n",
+    "reality_check_k = reality_check_k.astype(float).round(2) \n",
+    "print(test)\n",
+    "plot_stat_test(reality_check_k, DATA_SOURCE+\"_feat\", \"Gen\"+DATA_SOURCE+\"_bench\", test, IMPUTE, mask=False, cbar=True, ylabels=False, name_suffix=\"_corrDiff\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "962658c0",
+   "metadata": {},
+   "source": [
+    "## Figure 10: Limitations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "0ca393cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "BaselineED (19, 8) (19, 16)\n",
+      "Direct pearsonr BaselineED_feat\n",
+      "../output/plots/pdm_pearsonr_BaselineEDFeat_GenBaselineEDBench_filtered_nanDropped\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "test='pearsonr'\n",
+    "remark6_masked = statistical_test(DATA_SOURCE+\"_feat\", \"Gen\"+DATA_SOURCE+\"_bench\", test, IMPUTE, p_thresh=0.05)\n",
+    "plot_stat_test(remark6_masked, DATA_SOURCE+\"_feat\", \"Gen\"+DATA_SOURCE+\"_bench\", test, IMPUTE, mask=True, cbar=False, name_suffix=\"_filtered\") "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "7b82ae33",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "BaselineED (19, 8) (19, 16)\n",
+      "Direct kendalltau BaselineED_feat\n",
+      "../output/plots/pdm_kendalltau_BaselineEDFeat_GenBaselineEDBench_filtered_nanDropped\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "test='kendalltau'\n",
+    "remark6_masked = statistical_test(DATA_SOURCE+\"_feat\", \"Gen\"+DATA_SOURCE+\"_bench\", test, IMPUTE, p_thresh=0.05)\n",
+    "plot_stat_test(remark6_masked, DATA_SOURCE+\"_feat\", \"Gen\"+DATA_SOURCE+\"_bench\", test, IMPUTE, mask=True, cbar=True, ylabels=False, name_suffix=\"_filtered\") "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a2080811",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "tag",
+   "language": "python",
+   "name": "tag"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1b7b9f90bfaab404d4816731a291aff7948e0750
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,105 @@
+altair==5.3.0
+asttokens==2.4.1
+attrs==23.2.0
+backcall==0.2.0
+blinker==1.8.2
+brotli==1.1.0
+cachetools==5.4.0
+certifi==2024.2.2
+charset-normalizer==3.3.2
+click==8.1.7
+cloudpickle==3.0.0
+comm==0.2.2
+configspace==0.7.1
+contourpy==1.2.1
+cycler==0.12.1
+cvxopt==1.3.2
+dask==2024.4.1
+dask-jobqueue==0.8.5
+debugpy==1.8.1
+decorator==5.1.1
+deprecation==2.1.0
+distributed==2024.4.1
+emcee==3.1.4
+executing==2.0.1
+feeed==1.2.0
+fsspec==2024.3.1
+fonttools==4.51.0
+gitdb==4.0.11
+gitpython==3.1.43
+graphviz==0.20.3
+idna==3.7
+importlib-metadata==7.1.0
+importlib-resources==6.4.0
+imbalanced-learn==0.12.2
+imblearn==0.0
+intervaltree==3.1.0
+ipykernel==6.29.3
+ipython==8.12.0
+jedi==0.19.1
+jinja2==3.1.3
+joblib==1.4.0
+jsonschema==4.23.0
+jsonschema-specifications==2023.12.1
+jupyter_client==8.6.1
+jupyter_core==5.7.2
+kiwisolver==1.4.5
+levenshtein==0.23.0
+llvmlite==0.42.0
+locket==1.0.0
+lxml==5.2.1
+markdown-it-py==3.0.0
+markupsafe==2.1.5
+matplotlib==3.8.4
+matplotlib-inline==0.1.7
+mdurl==0.1.2
+more-itertools==10.2.0
+msgpack==1.0.8
+munkres==1.1.4
+networkx==3.2.1
+numba==0.59.1
+numpy==1.26.4
+opyenxes==0.3.0
+partd==1.4.1
+pandas==2.2.2
+pm4py==2.7.2
+protobuf==5.27.2
+pyarrow==17.0.0
+pydeck==0.9.1
+pydotplus==2.0.2
+pynisher==1.0.10
+pyrfr==0.9.0
+python-dateutil==2.9.0
+pyyaml==6.0.1
+rapidfuzz==3.8.1
+referencing==0.35.1
+regex==2023.12.25
+requests==2.32.3
+rich==13.7.1
+rpds-py==0.19.0
+seaborn==0.13.2
+scikit-learn==1.2.2
+scipy==1.13.0
+slicer==0.0.8
+smac==2.0.2
+smmap==5.0.1
+sortedcontainers==2.4.0
+stack_data==0.6.2
+streamlit==1.36.0
+stringdist==1.0.9
+tabulate==0.9.0
+tblib==3.0.0
+tenacity==8.5.0
+threadpoolctl==3.4.0
+toml==0.10.2
+tomli==2.0.1
+tornado==6.4
+tqdm==4.65.0
+toolz==0.12.1
+tzdata==2024.1
+urllib3==2.2.1
+watchdog==4.0.1
+xgboost==2.1.0
+zict==3.0.0
+zipp==3.17.0
+zstd==1.5.5.1
diff --git a/setup.py b/setup.py
index c3762c14bdd757b1ea797cfb3be4ac556c900fa1..4a5957056ae541febb56851189c6c4409c06fde4 100644
--- a/setup.py
+++ b/setup.py
@@ -1,4 +1,4 @@
-from setuptools import setup, find_packages
+from setuptools import setup
 import os
 
 with open("README.md", "r") as fh:
@@ -32,6 +32,7 @@ setup(
             'seaborn==0.13.2',
             'smac==2.0.2',
             'tqdm==4.65.0',
+            'streamlit-toggle-switch>=1.0.2'
             ],
         packages = ['gedi'],
         classifiers=[
diff --git a/utils/config_fabric.py b/utils/config_fabric.py
new file mode 100644
index 0000000000000000000000000000000000000000..a896bec7d94bd0e042571ceeb6ff70adbfce50e4
--- /dev/null
+++ b/utils/config_fabric.py
@@ -0,0 +1,428 @@
+from itertools import product as cproduct
+from itertools import combinations
+from pathlib import Path
+from pylab import *
+import base64
+import json
+import math
+import os
+import pandas as pd
+import streamlit as st
+import subprocess
+import time
+import shutil
+import zipfile
+import io
+
+st.set_page_config(layout='wide')
+INPUT_XES="output/inputlog_temp.xes"
+LOGO_PATH="gedi/utils/logo.png"
+
+def get_base64_image(image_path):
+    with open(image_path, "rb") as image_file:
+        return base64.b64encode(image_file.read()).decode()
+
+def play_header():
+    # Convert local image to base64
+    logo_base64 = get_base64_image(LOGO_PATH)
+
+    # HTML and CSS for placing the logo at the top left corner
+    head1, head2 = st.columns([1,8])
+    head1.markdown(
+        f"""
+        <style>
+        .header-logo {{
+            display: flex;
+            align-items: center;
+            justify-content: flex-start;
+        }}
+        .header-logo img {{
+            max-width: 100%; /* Adjust the size as needed */
+            overflow: hidden;
+            object-fit: contain;
+            padding-top: 12px;
+       }}
+        </style>
+        <div class="header-logo">
+            <img src="data:image/png;base64,{logo_base64}" alt="Logo">
+        </div>
+        """,
+        unsafe_allow_html=True
+    )
+    with head2:
+        """
+        # interactive GEDI
+        """
+    """
+    ## **G**enerating **E**vent **D**ata with **I**ntentional Features for Benchmarking Process Mining
+    """
+    return
+
+def double_switch(label_left, label_right, third_label=None, fourth_label=None, value=False):
+    if third_label==None and fourth_label==None:
+        # Create two columns for the labels and toggle switch
+        col0, col1, col2, col3, col4 = st.columns([2,1,1,1,2])
+    else:
+        # Create two columns for the labels and toggle switch
+        col0, col1, col2, col3, col4, col5, col6, col7, col8 = st.columns([1,1,1,1,1,1,1,1,1])
+
+    # Add labels to the columns
+    with col1:
+        st.write(label_left)
+
+    with col2:
+        # Create the toggle switch
+        toggle_option = st.toggle(" ",value=value,
+            key="toggle_switch_"+label_left,
+        )
+
+    with col3:
+        st.write(label_right)
+    if third_label is None and fourth_label is None:return toggle_option
+    else:
+        with col5:
+            st.write(third_label)
+
+        with col6:
+            # Create the toggle switch
+            toggle_option_2 = st.toggle(" ",value=False,
+                key="toggle_switch_"+third_label,
+            )
+
+        with col7:
+            st.write(fourth_label)
+        return toggle_option, toggle_option_2
+
+def multi_button(labels):
+    cols = st.columns(len(labels))
+    activations = []
+    for col, label in zip(cols, labels):
+        activations.append(col.button(label))
+    return activations
+
+def input_multicolumn(labels, default_values, n_cols=5):
+    result = {}
+    cols = st.columns(n_cols)
+    factor = math.ceil(len(labels)/n_cols)
+    extended = cols.copy()
+    for _ in range(factor):
+        extended.extend(cols)
+    for label, default_value, col in zip(labels, default_values, extended):
+        with col:
+            result[label] = col.text_input(label, default_value, key=f"input_"+label+'_'+str(default_value))
+    return result.values()
+
+def split_list(input_list, n):
+    # Calculate the size of each chunk
+    k, m = divmod(len(input_list), n)
+    # Use list comprehension to create n sublists
+    return [input_list[i * k + min(i, m):(i + 1) * k + min(i + 1, m)] for i in range(n)]
+
+def get_ranges_from_stats(stats, tuple_values):
+    col_for_row = ", ".join([f"x[\'{i}\'].astype(float)" for i in tuple_values])
+    stats['range'] = stats.apply(lambda x: tuple([eval(col_for_row)]), axis=1)
+    #tasks = eval(f"list(itertools.product({(parameters*n_para_obj)[:-2]}))")
+    result = [f"np.around({x}, 2)" for x in stats['range']]
+    result = ", ".join(result)
+    return result
+
+def create_objectives_grid(df, objectives, n_para_obj=2, method="combinatorial"):
+        if "combinatorial" in method:
+            sel_features = df.index.to_list()
+            parameters_o = "objectives, "
+            parameters = get_ranges_from_stats(df, sorted(objectives))
+            objectives = sorted(sel_features)
+            tasks = f"list(cproduct({parameters}))[0]"
+
+        elif method=="range-from-csv":
+            tasks = ""
+            for objective in objectives:
+                min_col, max_col, step_col = st.columns(3)
+                with min_col:
+                    selcted_min = st.slider(objective+': min', min_value=float(df[objective].min()), max_value=float(df[objective].max()), value=df[objective].quantile(0.1), step=0.1, key=objective+"min")
+                with max_col:
+                    selcted_max = st.slider('max', min_value=selcted_min, max_value=float(df[objective].max()), value=df[objective].quantile(0.9), step=0.1, key=objective+"max")
+                with step_col:
+                    step_value = st.slider('step', min_value=float(df[objective].min()), max_value=float(df[objective].quantile(0.9)), value=df[objective].median()/(df[objective].min()+0.0001), step=0.01, key=objective+"step")
+                tasks += f"np.around(np.arange({selcted_min}, {selcted_max}+{step_value}, {step_value}),2), "
+        else :#method=="range-manual":
+            experitments = []
+            tasks=""
+            if objectives != None:
+                cross_labels =  [feature[0]+': '+feature[1] for feature in list(cproduct(objectives,['min', 'max', 'step']))]
+                cross_values = [round(eval(str(combination[0])+combination[1]), 2) for combination in list(cproduct(list(df.values()), ['*1', '*2', '/3']))]
+                ranges = zip(objectives, split_list(list(input_multicolumn(cross_labels, cross_values, n_cols=3)), n_para_obj))
+                for objective, range_value in ranges:
+                    selcted_min, selcted_max, step_value = range_value
+                    tasks += f"np.around(np.arange({selcted_min}, {selcted_max}+{step_value}, {step_value}),2), "
+
+        try:
+            cartesian_product = list(cproduct(*eval(tasks)))
+            experiments = [{key: value[idx] for idx, key in enumerate(objectives)} for value in cartesian_product]
+            return experiments
+        except SyntaxError as e:
+            st.write("Please select valid features above.")
+            sys.exit(1)
+        except TypeError as e:
+            st.write("Please select at least 2 values to define.")
+            sys.exit(1)
+
+def set_generator_experiments(generator_params):
+    def handle_csv_file(grid_option):
+        uploaded_file = st.file_uploader("Pick a csv-file containing feature values for features:", type="csv")
+        if uploaded_file is not None:
+            df = pd.read_csv(uploaded_file)
+            if len(df.columns) <= 1:
+                raise pd.errors.ParserError("Please select a file withat least two columns (e.g. log, feature) and use ',' as a delimiter.")
+            sel_features = st.multiselect("Selected features", list(df.columns), list(df.columns)[-1])
+            if sel_features:
+                df = df[sel_features]
+                return df, sel_features
+        return None, None
+
+    def handle_combinatorial(sel_features, stats, tuple_values):
+        triangular_option = double_switch("Square", "Triangular")
+        if triangular_option:
+            experiments = []
+            elements = sel_features
+            # List to store all combinations
+            all_combinations = [combinations(sel_features, r) for r in range(1, len(sel_features) + 1)]
+            all_combinations = [comb for sublist in all_combinations for comb in sublist]
+
+            # Print or use the result as needed
+            for comb in all_combinations:
+                sel_stats = stats.loc[sorted(list(comb))]
+                experiments += create_objectives_grid(sel_stats, tuple_values, n_para_obj=len(tuple_values), method="combinatorial")
+        else: # Square
+            experiments = create_objectives_grid(stats, tuple_values, n_para_obj=len(tuple_values), method="combinatorial")
+        return experiments
+
+    def handle_csv_option(grid_option, df, sel_features):
+        if grid_option:
+            combinatorial = double_switch("Range", "Combinatorial")
+            if combinatorial:
+                add_quantile = st.slider('Add %-quantile', min_value=0.0, max_value=100.0, value=50.0, step=5.0)
+                stats = df.describe().transpose().sort_index()
+                stats[f"{int(add_quantile)}%"] = df.quantile(q=add_quantile / 100)
+                st.write(stats)
+                tuple_values = st.multiselect("Tuples including", list(stats.columns)[3:], default=['min', 'max'])
+                return handle_combinatorial(sel_features, stats, tuple_values)
+            else:  # Range
+                return create_objectives_grid(df, sel_features, n_para_obj=len(sel_features), method="range-from-csv")
+        else:  # Point
+            st.write(df)
+            return df.to_dict(orient='records')
+
+    def feature_select():
+        return st.multiselect("Selected features", list(generator_params['experiment'].keys()),
+                                                        list(generator_params['experiment'].keys())[-1])
+
+    def handle_manual_option(grid_option):
+        if grid_option:
+            combinatorial = double_switch("Range", "Combinatorial", value=True)
+            if combinatorial:
+                col1, col2 = st.columns([1,4])
+                with col1:
+                    num_values = st.number_input('How many values to define?', min_value=2, step=1)
+                with col2:
+                    sel_features = feature_select()
+
+                filtered_dict = {key: generator_params['experiment'][key] for key in sel_features if key in generator_params['experiment']}
+                values_indexes = ["value "+str(i+1) for i in range(num_values)]
+                values_defaults = ['*(1+2*0.'+str(i)+')' for i in range(num_values)]
+                cross_labels =  [feature[0]+': '+feature[1] for feature in list(cproduct(sel_features,values_indexes))]
+                cross_values = [round(eval(str(combination[0])+combination[1]), 2) for combination in list(cproduct(list(filtered_dict.values()), values_defaults))]
+                parameters = split_list(list(input_multicolumn(cross_labels, cross_values, n_cols=num_values)), len(sel_features))
+                tasks = f"list({parameters})"
+
+                tasks_df = pd.DataFrame(eval(tasks), index=sel_features, columns=values_indexes)
+                tasks_df = tasks_df.astype(float)
+                return handle_combinatorial(sel_features, tasks_df, values_indexes)
+
+            else: # Range
+                sel_features = feature_select()
+                return create_objectives_grid(generator_params['experiment'], sel_features, n_para_obj=len(sel_features), method="range-manual")
+
+        else: # Point
+            sel_features = feature_select()
+            #sel_features = st.multiselect("Selected features", list(generator_params['experiment'].keys()))
+
+            experiment = {sel_feature: float(st.text_input(sel_feature, generator_params['experiment'][sel_feature])) for sel_feature in sel_features}
+            return [experiment]
+        return[]
+
+
+    grid_option, csv_option = double_switch("Point-", "Grid-based", third_label="Manual", fourth_label="From CSV")
+
+    if csv_option:
+        df, sel_features = handle_csv_file(grid_option)
+        if df is not None and sel_features is not None:
+            experiments = handle_csv_option(grid_option, df, sel_features)
+        else:
+            experiments = []
+    else:  # Manual
+        experiments = handle_manual_option(grid_option)
+
+    generator_params['experiment'] = experiments
+    st.write(f"...result in {len(generator_params['experiment'])} experiment(s)")
+
+    """
+    #### Configuration space
+    """
+    updated_values = input_multicolumn(generator_params['config_space'].keys(), generator_params['config_space'].values())
+    for key, new_value in zip(generator_params['config_space'].keys(), updated_values):
+        generator_params['config_space'][key] = eval(new_value)
+    generator_params['n_trials'] = int(st.text_input('n_trials', generator_params['n_trials']))
+
+    return generator_params
+
+def sort_key(val):
+    parts = val.split('_')
+    # Extract and convert the numeric parts
+    part1 = int(parts[0][5:])  # e.g., from 'genEL1', extract '1'
+    return (part1)
+
+if __name__ == '__main__':
+    play_header()
+
+    # Load the configuration layout from a JSON file
+    config_layout = json.load(open("config_files/config_layout.json"))
+
+    # Define available pipeline steps
+    step_candidates = ["event_logs_generation", "feature_extraction"]
+
+    # Streamlit multi-select for pipeline steps
+    pipeline_steps = st.multiselect(
+        "Choose pipeline step",
+        step_candidates,
+        ["event_logs_generation"]
+    )
+
+    step_configs = []
+    set_col, view_col = st.columns([3, 2])
+
+    # Iterate through selected pipeline steps
+    for pipeline_step in pipeline_steps:
+        step_config = next(d for d in config_layout if d['pipeline_step'] == pipeline_step)
+
+        with set_col:
+            st.header(pipeline_step)
+
+            # Iterate through step configuration keys
+            for step_key in step_config.keys():
+                if step_key == "generator_params":
+                    st.subheader("Set-up experiments")
+                    step_config[step_key] = set_generator_experiments(step_config[step_key])
+                elif step_key == "feature_params":
+                    layout_features = list(step_config[step_key]['feature_set'])
+                    step_config[step_key]["feature_set"] = st.multiselect(
+                        "features to extract",
+                        layout_features
+                    )
+                elif step_key != "pipeline_step":
+                    step_config[step_key] = st.text_input(step_key, step_config[step_key])
+
+        with view_col:
+            st.write(step_config)
+
+        step_configs.append(step_config)
+
+    # Convert step configurations to JSON
+    config_file = json.dumps(step_configs, indent=4)
+
+    # Streamlit input for output file path
+    output_path = st.text_input("Output file path", "config_files/experiment_config.json")
+
+    # Ensure output directory exists
+    os.makedirs(os.path.dirname(output_path), exist_ok=True)
+
+    # Streamlit multi-button for saving options
+
+    button_col1, button_col2 = st.columns([1, 1])
+    with button_col1:
+        create_button = st.download_button(label="Download config file", data=config_file, file_name=os.path.basename(output_path), mime='application/json')
+        if pipeline_steps != ["event_logs_generation"]:
+            st.write("Run command:")
+            st.code(f"python -W ignore main.py -a {output_path}", language='bash')
+    if pipeline_steps == ["event_logs_generation"]:
+        with button_col2:
+            create_run_button = st.button("Run Generation")
+
+        if create_run_button:
+            # Save configuration to the specified output path
+            with open(output_path, "w") as f:
+                f.write(config_file)
+
+            command = f"python -W ignore main.py -a {output_path}".split()
+
+            # Prepare output path for feature extraction
+            directory = Path(step_config['output_path']).parts
+            path = os.path.join(directory[0], 'features', *directory[1:]) # for feature storage
+            path_to_logs = os.path.join(*directory[:]) # for log storage
+
+            # Clean existing output path if it exists
+            if os.path.exists(path):
+                shutil.rmtree(path)
+
+            if os.path.exists(path_to_logs): 
+                shutil.rmtree(path_to_logs)
+
+            if os.path.exists(path_to_logs):
+                shutil.rmtree(path_to_logs)
+
+            if os.path.exists(path_to_logs):
+                shutil.rmtree(path_to_logs)
+
+            # Simulate running the command with a loop and update progress
+            with st.spinner("Generating logs.."):
+            # Run the actual command
+                result = subprocess.run(command, capture_output=True, text=True)
+            st.success("Logs generated!")
+            st.write("## Results")
+
+            # Collect all file paths from the output directory
+            file_paths = [os.path.join(root, file)
+                            for root, _, files in os.walk(path)
+                            for file in files]
+
+            # Download the generated logs as a ZIP file
+            download_file_paths = [os.path.join(root, file)
+                            for root, _, files in os.walk(path_to_logs)
+                            for file in files]
+
+            zip_buffer = io.BytesIO()
+            with zipfile.ZipFile(zip_buffer, 'w') as zip_file:
+                for file in download_file_paths:
+                    zip_file.write(file, os.path.basename(file))
+            zip_buffer.seek(0)
+            st.download_button(label="Download generated logs", data=zip_buffer, file_name='generated_logs.zip', mime='application/zip')
+
+            # Read and concatenate all JSON files into a DataFrame
+            dataframes = pd.concat([pd.read_json(file, lines=True) for file in file_paths], ignore_index=True)
+
+            # Reorder columns with 'target_similarity' as the last column
+            columns = [col for col in dataframes.columns if col != 'target_similarity'] + ['target_similarity']
+            dataframes = dataframes[columns]
+            dataframes = dataframes.sort_values(by='log', key=lambda col: col.map(sort_key))
+
+            # Set 'log' as the index
+            dataframes['log'] = dataframes['log'].astype(str)
+            xticks_labels=dataframes['log'].apply(lambda x: x.split('_')[0])#+'_'+x.split('_')[1][:4]+'_'+x.split('_')[2][:4])
+            dataframes.set_index('log', inplace=True)
+
+            col1, col2 = st.columns([2, 3])
+
+            with col1:
+                st.dataframe(dataframes)
+
+            with col2:
+                plt.figure(figsize=(6, 3))
+                plt.plot(xticks_labels, dataframes['target_similarity'], 'o-')
+                plt.xlabel('Log')
+                plt.ylabel('Target Similarity')
+                if len(dataframes) > 10:
+                    plt.xticks(rotation=30, ha='right')
+                else:
+                    plt.xticks(rotation=0, ha='center')
+                plt.tight_layout()
+                st.pyplot(plt, dpi=400)
\ No newline at end of file
diff --git a/utils/default_argparse.py b/utils/default_argparse.py
index 72d960bc9e4ac3f58764f1f018465eb3c074a52d..2bcc74014bc6d5711b6bfbb89a00abd6722dd66e 100644
--- a/utils/default_argparse.py
+++ b/utils/default_argparse.py
@@ -6,23 +6,10 @@ class ArgParser(object):
     def parse(description):
         parser = argparse.ArgumentParser(description=description)
 
-        parser.add_argument(
-            '-o',
-            '--options',
-            dest='run_params_json',
-            help='a path to the parameter configuration of the program',
-        )
         parser.add_argument(
             '-a',
             '--algorithm-configs',
             dest='alg_params_json',
             help='a path to the configurations of the algorithms'
         )
-        parser.add_argument(
-            '-l',
-            '--load',
-            dest='result_load_files',
-            nargs='+',
-            help='the list of paths to already saved results'
-        )
         return parser.parse_args()
\ No newline at end of file
diff --git a/utils/merge_csvs.py b/utils/merge_csvs.py
new file mode 100644
index 0000000000000000000000000000000000000000..10761f128524ee6d8bf5f0ec3df41cb54ae40242
--- /dev/null
+++ b/utils/merge_csvs.py
@@ -0,0 +1,19 @@
+import os
+import pandas as pd
+import sys
+
+
+FILE_START = sys.argv[1]
+ROOT_PATH, FILE_START = os.path.split(FILE_START)
+filename_list = os.listdir(str(ROOT_PATH))
+filename_list = [filename for filename in filename_list if filename.startswith(FILE_START)]
+
+OUTPUT_PATH = os.path.join(ROOT_PATH, FILE_START+".csv")
+
+result = pd.DataFrame(columns=['log'])
+for filename in filename_list:
+    df = pd.read_csv(os.path.join(ROOT_PATH, filename))
+    result = result.merge(df, on='log', how='outer') 
+    print(df.shape)
+result.to_csv(OUTPUT_PATH, index=False)
+print(f"Saved dataframe with {result.shape} in {OUTPUT_PATH}")
diff --git a/utils/merge_jsons.py b/utils/merge_jsons.py
new file mode 100644
index 0000000000000000000000000000000000000000..f0d3b2bd53e4816bdc8317b83f15a9e823b0d952
--- /dev/null
+++ b/utils/merge_jsons.py
@@ -0,0 +1,42 @@
+import argparse
+import json
+import csv
+import os
+
+"""
+Run using:
+python merge_jsons.py path_to_your_json_directory output.csv
+
+"""
+def json_to_csv(json_dir, output_csv):
+
+    json_files = [os.path.join(json_dir, file) for file in os.listdir(json_dir) if file.endswith('.json')]
+
+    # Collect data from all JSON files
+    all_data = []
+    for json_file in json_files:
+        with open(json_file, 'r') as f:
+            data = json.load(f)
+            all_data.append(data)
+
+    # Extract the headers from the first JSON object
+    if all_data:
+        headers = {elem for s in [set(i) for i in [d.keys() for d in all_data]] for elem in s}
+    else:
+        raise ValueError("No data found in JSON files")
+
+    # Write data to CSV
+    with open(output_csv, 'w', newline='') as f:
+        writer = csv.DictWriter(f, fieldnames=headers)
+        writer.writeheader()
+        writer.writerows(all_data)
+
+# Example usage
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Convert JSON files in a directory to a CSV file.')
+    parser.add_argument('json_dir', type=str, help='The directory containing JSON files')
+    parser.add_argument('output_csv', type=str, help='The output CSV file path')
+    args = parser.parse_args()
+
+    json_to_csv(args.json_dir, args.output_csv)
+
diff --git a/utils/param_keys/__init__.py b/utils/param_keys/__init__.py
index 18c4b2adc0ac063447c1a0015f97d3ffba0b4256..773502cdb45ae21569d9144051d06f15fd816e50 100644
--- a/utils/param_keys/__init__.py
+++ b/utils/param_keys/__init__.py
@@ -1,9 +1,3 @@
-# Run Params #
-RUN_OPTION = 'run_option'
-INPUT_NAME = 'input_name'
-SAVE_RESULTS = 'save_results'
-LOAD_RESULTS = 'load_results'
-
 # Model params
 ALGORITHM_NAME = 'algorithm_name'
 PIPELINE_STEP = 'pipeline_step'
diff --git a/utils/param_keys/run_options.py b/utils/param_keys/run_options.py
deleted file mode 100644
index ce995e2abe75efb6eed74607641a6fb19d07fc7c..0000000000000000000000000000000000000000
--- a/utils/param_keys/run_options.py
+++ /dev/null
@@ -1,2 +0,0 @@
-BASELINE = 'baseline'
-COMPARE = 'compare'
\ No newline at end of file