Adds app.py

app.py

@@ -0,0 +1,291 @@
+from copy import deepcopy
+from importlib import reload
+from itertools import product as cproduct
+from itertools import combinations
+from pylab import *
+import itertools
+import json
+import math
+import os
+import pandas as pd
+import pm4py
+import random
+import streamlit as st
+import subprocess
+
+st.set_page_config(layout='wide')
+INPUT_XES="output/inputlog_temp.xes"
+
+"""
+# Configuration File fabric for
+## GEDI: **G**enerating **E**vent **D**ata with **I**ntentional Features for Benchmarking Process Mining
+"""
+def double_switch(label_left, label_right, third_label=None, fourth_label=None):
+    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=False,
+            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)
+            sel_features = st.multiselect("Selected features", list(df.columns))
+            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()))
+
+    def handle_manual_option(grid_option):
+        if grid_option:
+            combinatorial = double_switch("Range", "Combinatorial")
+            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()
+
+                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(generator_params['experiment'].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
+
+if __name__ == '__main__':
+    config_layout = json.load(open("config_files/config_layout.json"))
+    type(config_layout)
+    step_candidates = ["instance_augmentation","event_logs_generation","feature_extraction","benchmark_test"]
+    pipeline_steps = st.multiselect(
+        "Choose pipeline step",
+        step_candidates,
+        ["event_logs_generation"]
+    )
+    step_configs = []
+    set_col, view_col = st.columns([3, 2])
+    for pipeline_step in pipeline_steps:
+        step_config = [d for d in config_layout if d['pipeline_step'] == pipeline_step][0]
+        with set_col:
+            st.header(pipeline_step)
+            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)
+    config_file = json.dumps(step_configs, indent=4)
+    output_path = st.text_input("Output file path", "config_files/experiment_config.json")
+    os.makedirs(os.path.dirname(output_path), exist_ok=True)
+    save_labels = ["Save config file", "Save and run config_file"]
+    save_labels = ["Save configuration file"]
+    #create_button, create_run_button = multi_button(save_labels)
+    create_button = multi_button(save_labels)
+    # FIXME: Bug: automatically updates the experiment_config.json file even without pressing the save button
+    if create_button: # or create_run_button:
+        with open(output_path, "w") as f:
+            f.write(config_file)
+        st.write("Saved configuration in ", output_path, ". Run command:")
+        #if create_run_button:
+        if True:
+            var = f"python -W ignore main.py -a {output_path}"
+            st.code(var, language='bash')
+        if False: #FIXME: Command fails when using multiprocessing 
+            command = var.split()
+
+            # Run the command
+            result = subprocess.run(command, capture_output=True, text=True)
+
+            if len(result.stderr)==0:
+                st.write(result.stdout)
+            else:
+                st.write("ERROR: ", result.stderr)