diff --git a/paradise/main.py b/paradise/main.py
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..b7eaf01d49f118fa4326d033f6dfbc76c736b4ed 100755
--- a/paradise/main.py
+++ b/paradise/main.py
@@ -0,0 +1,186 @@
+#!/usr/bin/env python
+"""
+This module is the entrypoint for the generation of time series.
+"""
+import json
+import os
+
+from argparse import ArgumentParser
+from datetime import datetime
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+from generator import DatasetGenerator
+from split import BaseSplitter
+from trainers import BaseTrainer
+from results import ResultExtractor
+
+plt.rcParams["figure.figsize"] = (20, 10)
+
+
+if __name__ == "__main__":
+ # =================================================================================================================
+ # Fetch arguments from CLI
+ # =================================================================================================================
+ # Create the parser
+ parser = ArgumentParser(prog="Time Series Generator", description="Create time series.")
+
+ # Add arguments
+ parser.add_argument(
+ "task",
+ help="Either generate data, test an algorithm, compute the results of an execution or do everything.",
+ choices=["generate", "train", "results", "all"]
+ )
+ parser.add_argument(
+ "-c", "--config",
+ help="Path to config file(s) to use to generate data.",
+ default=["config/2d-mix.json"],
+ nargs="+"
+ )
+ parser.add_argument("-a", "--algorithms", help="Which algorithm to train.", default=["kmeans"], nargs="+")
+ parser.add_argument(
+ "-m", "--cluster-method",
+ help="Which cluster algorithm to use.",
+ default="HDBSCAN",
+ dest="method"
+ )
+ parser.add_argument("-i", "--input", help="Input directory. Only to be used when no data will be generated")
+ parser.add_argument("-o", "--output", help="Output directory")
+ parser.add_argument(
+ "-s", "--split",
+ help="Automatically split dataset into subsystems and treat them once at a time.",
+ action="store_true"
+ )
+ parser.add_argument(
+ "-optim",
+ help="Optimize the different hyper parameters according to what's wirtten in the algorithm_params.json file",
+ action="store_true"
+ )
+ parser.add_argument(
+ "-d", "--docker",
+ help="Use Docker containers directly to run the algorithm. Allow to run algorithms without cloning repo",
+ action="store_true"
+ )
+
+ # Load args
+ args = parser.parse_args()
+
+ # Prepare output directory
+ if args.output is None:
+ OUTPUT_DIR = f"output/{datetime.now().strftime('%Y-%m-%d.%H-%M-%S')}"
+ else:
+ OUTPUT_DIR = f"output/{args.output}"
+
+ # Prepare input directory if needed
+ if args.task not in ["generate", "all"] and args.input is None:
+ raise ValueError("Impossible to skip data generation and not give an input directory")
+
+ if args.task not in ["generate", "all"]:
+ INPUT_DIR = f"output/{args.input}"
+ else:
+ INPUT_DIR = OUTPUT_DIR
+
+ # =================================================================================================================
+ # Generate Data
+ # =================================================================================================================
+ # Load config file
+ for config_file in args.config:
+ # Compute config name
+ config_name = config_file.split("/")[-1][:-5] if "." in config_file else config_file.split("/")[-1]
+
+ if args.task in ["generate", "all"]:
+ # Create output dir
+ os.makedirs(f"{OUTPUT_DIR}/{config_name}", exist_ok=True)
+
+ # Read config file
+ with open(config_file, "r", encoding="utf-8") as f:
+ config = json.load(f)
+
+ # Data generation
+ generator = DatasetGenerator(config)
+
+ dataset, train_dataset, labels, variables_labels = generator.generate()
+ subsystems, splitted_data, splitted_train, splitted_labels = generator.get_splitted_data()
+
+ # Save data to disk
+ # Prepare the data
+ columns = list(range(0, dataset.shape[0]))
+ indices = list(range(0, dataset.shape[1]))
+ t_indices = list(range(0, train_dataset.shape[1]))
+ df_test = pd.DataFrame(data=dataset.T, index=indices, columns=columns)
+ df_test["is_anomaly"] = labels
+ df_train = pd.DataFrame(data=train_dataset.T, index=t_indices, columns=columns)
+ df_train["is_anomaly"] = np.zeros(train_dataset.shape[1])
+
+ df_test.to_csv(f"{OUTPUT_DIR}/{config_name}/dataset.csv", index_label="Timestamp")
+ df_train.to_csv(f"{OUTPUT_DIR}/{config_name}/dataset_train.csv", index_label="Timestamp")
+ pd.DataFrame(data=labels).to_csv(f"{OUTPUT_DIR}/{config_name}/dataset_labels.csv", index=False)
+ pd.DataFrame(data=variables_labels.T)\
+ .to_csv(f"{OUTPUT_DIR}/{config_name}/dataset_variables_labels.csv", index=False)
+
+ # Plot data and save it to disk
+ for dimension in dataset:
+ plt.plot(dimension)
+ plt.savefig(f"{OUTPUT_DIR}/{config_name}/dataset.png")
+ plt.clf()
+
+ for dimension in train_dataset:
+ plt.plot(dimension)
+ plt.savefig(f"{OUTPUT_DIR}/{config_name}/train_dataset.png")
+ plt.clf()
+
+ # Handle splitted data
+ for idx, (data, train, lab) in enumerate(zip(splitted_data, splitted_train, splitted_labels)):
+ columns = list(range(0, data.shape[0]))
+ df_test_s = pd.DataFrame(data=data.T, index=indices, columns=columns)
+ df_test_s["is_anomaly"] = lab
+ df_train_s = pd.DataFrame(data=train.T, index=t_indices, columns=columns)
+ df_train_s["is_anomaly"] = np.zeros(train.shape[1])
+
+ df_test_s.to_csv(f"{OUTPUT_DIR}/{config_name}/dataset_{idx}.csv", index_label="Timestamp")
+ df_train_s.to_csv(f"{OUTPUT_DIR}/{config_name}/dataset_{idx}_train.csv", index_label="Timestamp")
+ pd.DataFrame(data=lab).to_csv(f"{OUTPUT_DIR}/{config_name}/dataset_{idx}_labels.csv", index=False)
+
+ with open(f"{OUTPUT_DIR}/{config_name}/subsystems.txt", "w", encoding="utf-8") as f:
+ clusters = np.zeros(dataset.shape[0])
+ for idx, cluster in enumerate(subsystems):
+ for member in cluster:
+ clusters[member] = idx
+ f.write(json.dumps(clusters.tolist()))
+
+
+ # =================================================================================================================
+ # Split data
+ # =================================================================================================================
+
+ if args.split and args.task in ["train", "all"]:
+ splitter = BaseSplitter(f"{INPUT_DIR}/{config_name}")
+ splitter.split_data(method=args.method)
+
+ # =================================================================================================================
+ # Train algorithm
+ # =================================================================================================================
+ if args.task in ["train", "all"]:
+ trainers = []
+
+ with open("algorithm_params.json", "r", encoding="utf-8") as f:
+ algo_params = json.load(f)
+
+ with open(f"{INPUT_DIR}/{config_name}/time.csv", "a", encoding="utf-8") as f:
+ f.write("Algorithm,Dataset,Step,Duration\n")
+
+ for algo in args.algorithms:
+ params = algo_params[algo]
+ train = params.pop("training")
+ trainer = BaseTrainer(f"{INPUT_DIR}/{config_name}", algo, train, **params)
+ trainer.start(optim=args.optim)
+ trainers.append(trainer)
+
+ # =================================================================================================================
+ # Compute and plot results
+ # =================================================================================================================
+ if args.task in ["results", "all"]:
+ results = ResultExtractor(INPUT_DIR).fetch_results().compute_results()
+ results.to_csv(f"{INPUT_DIR}/results.csv")