diff --git a/algorithm_params.json b/algorithm_params.json
index d7b8755e00b8f1f23ba7e5f61bf8489c82d95fdb..87704f45a16277b2d83bc700fef7b93ac96889c7 100644
--- a/algorithm_params.json
+++ b/algorithm_params.json
@@ -1,44 +1,73 @@
{
"kmeans": {
"training": false,
- "n_clusters": 50,
- "window_size": 10
+ "optimize": {
+ "n_clusters": [5, 100],
+ "window_size": [5, 100]
+ },
+ "const": {
+ }
},
"iforest": {
"training": false,
- "n_trees": 500,
- "max_features": 1.0
- },
- "normalizing_flows": {
- "training": true,
- "window_size": 100,
- "epochs": 500,
- "trainer_epochs": 100
+ "optimize": {
+ "n_trees": [50, 1000]
+ },
+ "const": {
+ "max_features": 1.0
+ }
},
"deepant": {
"training": true,
- "epochs": 500,
- "window_size": 75,
- "prediction_window_size": 50,
- "learning_rate": 0.001
+ "optimize": {
+ "window_size": [10, 200],
+ "prediction_window_size": [10, 200]
+ },
+ "const": {
+ "epochs": 500,
+ "learning_rate": 0.001
+ }
},
"lstm_ad": {
"training": true,
- "lstm_layers": 1,
- "window_size": 250,
- "prediction_window_size": 50,
- "epochs": 500,
- "learning_rate": 0.001
+ "optimize": {
+ "lstm_layers": [1, 5],
+ "window_size": [50, 500],
+ "prediction_window_size": [10, 100]
+ },
+ "const": {
+ "epochs": 500,
+ "learning_rate": 0.001
+ }
},
"health_esn": {
"training": true,
- "prediction_window_size": 50
+ "optimize": {
+ "prediction_window_size": [10, 100]
+ },
+ "const": {
+ }
},
- "telemanom": {
+ "random_black_forest": {
"training": true,
- "window_size": 100
+ "optimize": {},
+ "const": {}
},
- "random_black_forest": {
- "training": true
+ "if_lof": {
+ "training": false,
+ "optimize": {
+ "n_neighbors": [10, 100],
+ "n_trees": [50, 1000]
+ },
+ "const": {}
+ },
+ "lof": {
+ "training": false,
+ "optimize": {
+ "n_neighbors": [10, 100],
+ "leaf_size": [5, 50]
+ },
+ "const": {}
}
+
}
diff --git a/algorithms/if_lof/Dockerfile b/algorithms/if_lof/Dockerfile
new file mode 100644
index 0000000000000000000000000000000000000000..67dd034eef14c49c09ebc62fff84008f1934e8e4
--- /dev/null
+++ b/algorithms/if_lof/Dockerfile
@@ -0,0 +1,12 @@
+FROM ghcr.io/timeeval/python3-base:0.3.0
+
+LABEL maintainer="rohan.sawahn@student.hpi.de"
+LABEL org.opencontainers.image.licenses=MIT
+
+ENV ALGORITHM_MAIN="/app/algorithm.py"
+
+COPY requirements.txt /app/
+RUN pip install -r requirements.txt
+
+COPY algorithm.py /app/
+COPY manifest.json /app/
diff --git a/algorithms/if_lof/LICENSE b/algorithms/if_lof/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..2db883d8cabaf3f696cc2eb5ed48661877d41795
--- /dev/null
+++ b/algorithms/if_lof/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2020-2022 Rohan Sawahn, Phillip Wenig and Sebastian Schmidl
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/algorithms/if_lof/README.md b/algorithms/if_lof/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..83dd0d7df8da3c830b755ae4b08c6e2d31ff973a
--- /dev/null
+++ b/algorithms/if_lof/README.md
@@ -0,0 +1,21 @@
+# Isolation Forest - Local Outlier Factor (IF-LOF)
+
+|||
+| :--- | :--- |
+| Citekey | ChengEtAl2019Outlier |
+| Source Code | own |
+| Learning type | unsupervised |
+| Input dimensionality | multivariate |
+|||
+
+## Output Format
+
+The output will be an anomaly score for every input data point
+
+## Dependencies
+
+- python 3
+
+## Copyright notice and citation format
+
+> Zhangyu Cheng, Chengming Zou, and Jianwei Dong. 2019. Outlier detection using isolation forest and local outlier factor. In Proceedings of the Conference on Research in Adaptive and Convergent Systems (RACS '19). Association for Computing Machinery, New York, NY, USA, 161–168. DOI:https://doi.org/10.1145/3338840.3355641
diff --git a/algorithms/if_lof/algorithm.py b/algorithms/if_lof/algorithm.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d2374f2c53242c37559aff6ff90dc55aadb7bbc
--- /dev/null
+++ b/algorithms/if_lof/algorithm.py
@@ -0,0 +1,191 @@
+#!/usr/bin/env python3
+
+import sys
+import json
+import math
+import heapq
+
+import numpy as np
+from typing import Optional
+from pathlib import Path
+from sklearn.neighbors import LocalOutlierFactor
+from sklearn.ensemble import IsolationForest
+
+
+class Config:
+ dataInput: Path
+ dataOutput: Path
+ executionType: str
+ n_trees: int
+ max_samples: Optional[float]
+ n_neighbors: int
+ alpha: float
+ m: int
+ random_state: int
+
+ def __init__(self, params):
+ self.dataInput = Path(params.get('dataInput',
+ '/data/dataset.csv'))
+ self.dataOutput = Path(params.get('dataOutput',
+ '/results/anomaly_window_scores.ts'))
+ self.executionType = params.get('executionType',
+ 'execute')
+ try:
+ customParameters = params['customParameters']
+ except KeyError:
+ customParameters = {}
+ self.n_trees = customParameters.get('n_trees', 200)
+ self.max_samples = customParameters.get('max_samples', None)
+ self.n_neighbors = customParameters.get('n_neighbors', 20)
+ self.alpha = customParameters.get('alpha', 1)
+ self.m = customParameters.get('m', None)
+ self.random_state = customParameters.get('random_state', 42)
+
+
+def set_random_state(config) -> None:
+ seed = config.random_state
+ import random
+ random.seed(seed)
+ np.random.seed(seed)
+
+
+def read_data(config: Config):
+ print('Reading Data...')
+ X = np.genfromtxt(config.dataInput, delimiter=',', skip_header=True)
+ # skip first col (index) and last col (label)
+ X = X[:, 1:-1]
+ print('Data')
+ print(' dims:', X.shape[0])
+ print(' samples:', X.shape[1])
+ if config.m is None:
+ config.m = len(X[0])
+ return (X, config)
+
+
+def compute_iforest_scores(X, config: Config):
+ print('Configuring forest...')
+ max_samples = config.max_samples if config.max_samples else "auto"
+ forest = IsolationForest(n_estimators=config.n_trees,
+ max_samples=max_samples,
+ random_state=config.random_state)
+ forest.fit(X)
+ print('Computing Forest scores...')
+ scores = forest.decision_function(X)
+ return -scores
+
+
+def save_results(data, path: str):
+ print(f'Saving Results to {path}')
+ np.savetxt(path, data, delimiter=',', fmt='%f')
+ print('Results saved')
+
+
+def outlier_coefficient_for_attribute(attr_index: int, data):
+ ''' The original paper is incorrect and inaccurate over here.
+ My assumption is that we would want to calculate the following:
+ | emperical_standard_deviation(attr) / mean(attr) | '''
+
+ attr = data[:, attr_index]
+ mean = np.mean(attr)
+ esd = np.std(attr)
+ # We take to absolute value in the case of a negative mean
+ return np.abs(esd / mean)
+
+
+def prune_data(config: Config, data, anomaly_scores):
+ ''' The original paper is very inaccurate over here and it is sometimes hard
+ to grasp the meaning of variables. Please be aware that
+ this method might not be the same as inteded by the authors, but is my
+ assumption on what they were trying to do. The pruning is described
+ in section 3.3 of the paper'''
+
+ print('Pruning data...')
+
+ outlier_coefficients = [outlier_coefficient_for_attribute(attr_index, data)
+ for attr_index in range(len(data[0]))]
+
+ # assumption: We want to get the m outlier coefficients with highest value
+ outlier_coefficients.sort(reverse=True)
+ top_m = outlier_coefficients[0:config.m]
+ proportion_of_outliers = (config.alpha * sum(top_m)) / config.m
+
+ # now that we know the proportion of outliers, we return the according
+ # amount of data points with the highest anomaly scores
+ num_outliers = math.ceil(len(data) * proportion_of_outliers)
+ print(f'Num of outlier_candidates {num_outliers}')
+
+ # prune the dataset by removing all points except the num_outlier points with
+ # highest anomaly score
+ min_anomaly_score = heapq.nlargest(num_outliers, anomaly_scores)[-1]
+ outlier_candidates_indexes = [i for i in range(len(data))
+ if anomaly_scores[i] > min_anomaly_score]
+ outlier_candidates = [data[i] for i in outlier_candidates_indexes]
+
+ return (outlier_candidates, outlier_candidates_indexes)
+
+
+def compute_lof(config: Config, data, outlier_canidates):
+ print('Computing local outlier factors ...')
+ lof = LocalOutlierFactor(n_neighbors=config.n_neighbors, novelty=True)
+ lof.fit(data)
+ return -lof.score_samples(outlier_canidates)
+
+
+def continous_scores(outlier_factors, outlier_indexes, original_ds_len):
+ print("Postprocessing")
+ current_outlier_index = 0
+ res = []
+
+ def is_index_of_outlier_candidate(i):
+ return i in outlier_indexes
+
+ for i in range(0, original_ds_len):
+ if is_index_of_outlier_candidate(i):
+ res.append(outlier_factors[current_outlier_index])
+ current_outlier_index += 1
+ else:
+ res.append(0)
+
+ return res
+
+
+def execute(config: Config):
+ data, config = read_data(config=config)
+ iforest_scores = compute_iforest_scores(X=data, config=config)
+ outlier_candidates, outlier_indexes = prune_data(config=config,
+ data=data,
+ anomaly_scores=iforest_scores)
+ outlier_factors = compute_lof(config=config,
+ data=data,
+ outlier_canidates=outlier_candidates)
+
+ results = continous_scores(outlier_factors=outlier_factors,
+ outlier_indexes=outlier_indexes,
+ original_ds_len=len(data))
+
+ save_results(data=results, path=config.dataOutput)
+
+
+def parse_args():
+ print(sys.argv)
+ if len(sys.argv) < 2:
+ print('No arguments supplied, using default arguments!',
+ file=sys.stderr)
+ params = {}
+ elif len(sys.argv) > 2:
+ print('Wrong number of arguments given! Single JSON-String expected!',
+ file=sys.stderr)
+ exit(1)
+ else:
+ params = json.loads(sys.argv[1])
+ return Config(params)
+
+
+if __name__ == '__main__':
+ config = parse_args()
+ if config.executionType == 'train':
+ print('Nothing to train.')
+ elif config.executionType == 'execute':
+ execute(config)
+ else:
+ raise Exception('Invalid Execution type given')
diff --git a/algorithms/if_lof/manifest.json b/algorithms/if_lof/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..3dbbd0aa8430f089d5f6d3aba7d8a8493889f86b
--- /dev/null
+++ b/algorithms/if_lof/manifest.json
@@ -0,0 +1,58 @@
+{
+ "title": "IF-LOF",
+ "description": "Isolation Forest - Local Outlier Factor: Uses a 3 step process - Building an isolation forest, pruning the forest with a computed treshhold, and applies local outlier factor to the resulting dataset",
+ "inputDimensionality": "multivariate",
+ "version": "0.3.0",
+ "authors": "Cheng, Zhangyu and Zou, Chengming and Dong, Jianwei",
+ "type": "Detector",
+ "language": "Python",
+ "learningType": "Unsupervised",
+ "mainFile": "algorithm.py",
+ "executionStep": {
+ "parameters": [
+ {
+ "name": "n_trees",
+ "type": "int",
+ "description": "Number of trees in isolation forest",
+ "defaultValue": 200,
+ "optional": "false"
+ },
+ {
+ "name": "max_samples",
+ "type": "float",
+ "description": "The number of samples to draw from X to train each tree: `max_samples * X.shape[0]`. If unspecified (`null`), then `max_samples=min(256, X.shape[0])`.",
+ "defaultValue": null,
+ "optional": "false"
+ },
+ {
+ "name": "n_neighbors",
+ "type": "int",
+ "description": "Number neighbors to look at in local outlier factor calculation",
+ "defaultValue": 10,
+ "optional": "false"
+ },
+ {
+ "name": "alpha",
+ "type": "float",
+ "description": "Scalar that depends on consideration of the dataset and controls the amount of data to be pruned",
+ "defaultValue": 0.5,
+ "optional": "false"
+ },
+ {
+ "name": "m",
+ "type": "int",
+ "description": "m features with highest scores will be used for pruning",
+ "defaultValue": null,
+ "optional": "true"
+ },
+ {
+ "name": "random_state",
+ "type": "int",
+ "description": "Seed for random number generation.",
+ "defaultValue": 42,
+ "optional": "true"
+ }
+ ],
+ "modelInput": "none"
+ }
+}
diff --git a/algorithms/if_lof/requirements.txt b/algorithms/if_lof/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5cddbb17cf94d1ab654eb558d756c8e6ad1f345c
--- /dev/null
+++ b/algorithms/if_lof/requirements.txt
@@ -0,0 +1,2 @@
+numpy
+scikit-learn
diff --git a/algorithms/iforest/Dockerfile b/algorithms/iforest/Dockerfile
new file mode 100644
index 0000000000000000000000000000000000000000..d3af8f3720eda8c56f65240b14b2ceac7a9f35eb
--- /dev/null
+++ b/algorithms/iforest/Dockerfile
@@ -0,0 +1,13 @@
+FROM ghcr.io/timeeval/pyod:0.3.1
+
+LABEL maintainer="sebastian.schmidl@hpi.de"
+LABEL org.opencontainers.image.licenses=MIT
+
+ENV ALGORITHM_MAIN="/app/algorithm.py"
+
+# install algorithm dependencies
+COPY requirements.txt /app/
+RUN pip install -r /app/requirements.txt
+
+COPY algorithm.py /app/
+COPY manifest.json /app/
diff --git a/algorithms/iforest/LICENSE b/algorithms/iforest/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..e594ec89208b9bcc64e3b455e283d3ce07f1b817
--- /dev/null
+++ b/algorithms/iforest/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2020-2022 Phillip Wenig and Sebastian Schmidl
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/algorithms/iforest/README.md b/algorithms/iforest/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..15b758943ef52a11caf1a67b2f0057632889a632
--- /dev/null
+++ b/algorithms/iforest/README.md
@@ -0,0 +1,57 @@
+# Isolation Forest (iForest)
+
+|||
+| :--- | :--- |
+| Citekey | LiuEtAl2012IsolationBased |
+| Source Code | https://github.com/yzhao062/pyod/blob/master/pyod/models/iforest.py |
+| Learning type | unsupervised |
+| Input dimensionality | multivariate |
+|||
+
+## Parameters
+
+- `n_estimators`: int, optional (default=100)
+ The number of base estimators in the ensemble.
+
+- `max_samples`: int or float, optional (default="auto")
+ The number of samples to draw from X to train each base estimator.
+ - If int, then draw `max_samples` samples.
+ - If float, then draw `max_samples * X.shape[0]` samples.
+ - If "auto", then `max_samples=min(256, n_samples)`.
+ If max_samples is larger than the number of samples provided, all samples will be used for all trees (no sampling).
+
+- `max_features`: int or float, optional (default=1.0)
+ The number of features to draw from X to train each base estimator.
+ - If int, then draw `max_features` features.
+ - If float, then draw `max_features * X.shape[1]` features.
+
+- `contamination`: float in (0., 0.5), optional (default=0.1)
+ The amount of contamination of the data set, i.e. the proportion of outliers in the data set.
+ Used when fitting to define the threshold on the decision function.
+ **Automatically determined by algorithm script!!**
+
+- `bootstrap`: bool, optional (default=False)
+ If True, individual trees are fit on random subsets of the training data sampled with replacement.
+ If False, sampling without replacement is performed.
+
+- `behaviour`: str, default='old'
+ Behaviour of the `decision_function` which can be either 'old' or 'new'.
+ Passing `behaviour='new'` makes the `decision_function` change to match other anomaly detection algorithm API which will be the default behaviour in the future.
+ As explained in details in the `offset_` attribute documentation, the `decision_function` becomes dependent on the contamination parameter, in such a way that 0 becomes its natural threshold to detect outliers.
+ **REMOVED** (old behavior is used per default!).
+
+- `random_state`: int, RandomState instance or None, optional (default=None)
+ If int, random_state is the seed used by the random number generator;
+ If RandomState instance, random_state is the random number generator;
+ If None, the random number generator is the RandomState instance used by `np.random`.
+
+- `verbose`: int, optional (default=0)
+ Controls the verbosity of the tree building process.
+
+- `n_jobs`: integer, optional (default=1)
+ The number of jobs to run in parallel for both `fit` and `predict`.
+ If -1, then the number of jobs is set to the number of cores.
+
+## Citation format (for source code)
+
+> Zhao, Y., Nasrullah, Z. and Li, Z., 2019. PyOD: A Python Toolbox for Scalable Outlier Detection. Journal of machine learning research (JMLR), 20(96), pp.1-7.
diff --git a/algorithms/iforest/algorithm.py b/algorithms/iforest/algorithm.py
new file mode 100755
index 0000000000000000000000000000000000000000..fa2de08793ef096c1ae0eb601d96c8f719d2feb8
--- /dev/null
+++ b/algorithms/iforest/algorithm.py
@@ -0,0 +1,83 @@
+#!/usr/bin/env python3
+import argparse
+import json
+import sys
+import numpy as np
+import pandas as pd
+
+from typing import Optional
+from dataclasses import dataclass
+from pyod.models.iforest import IForest
+
+
+@dataclass
+class CustomParameters:
+ n_trees: float = 100
+ max_samples: Optional[float] = None
+ max_features: float = 1.
+ bootstrap: bool = False
+ random_state: int = 42
+ verbose: int = 0
+ n_jobs: int = 1
+
+
+class AlgorithmArgs(argparse.Namespace):
+ @staticmethod
+ def from_sys_args() -> 'AlgorithmArgs':
+ args: dict = json.loads(sys.argv[1])
+ custom_parameter_keys = dir(CustomParameters())
+ filtered_parameters = dict(filter(lambda x: x[0] in custom_parameter_keys, args.get("customParameters", {}).items()))
+ args["customParameters"] = CustomParameters(**filtered_parameters)
+ return AlgorithmArgs(**args)
+
+
+def set_random_state(config: AlgorithmArgs) -> None:
+ seed = config.customParameters.random_state
+ import random
+ random.seed(seed)
+ np.random.seed(seed)
+
+
+def load_data(config: AlgorithmArgs) -> np.ndarray:
+ df = pd.read_csv(config.dataInput)
+ data = df.iloc[:, 1:-1].values
+ labels = df.iloc[:, -1].values
+ contamination = labels.sum() / len(labels)
+ # Use smallest positive float as contamination if there are no anomalies in dataset
+ contamination = np.nextafter(0, 1) if contamination == 0. else contamination
+ return data, contamination
+
+
+def main(config: AlgorithmArgs):
+ set_random_state(config)
+ data, contamination = load_data(config)
+
+ clf = IForest(
+ contamination=contamination,
+ n_estimators=config.customParameters.n_trees,
+ max_samples=config.customParameters.max_samples or "auto",
+ max_features=config.customParameters.max_features,
+ bootstrap=config.customParameters.bootstrap,
+ random_state=config.customParameters.random_state,
+ verbose=config.customParameters.verbose,
+ n_jobs=config.customParameters.n_jobs,
+ )
+ clf.fit(data)
+ scores = clf.decision_scores_
+ np.savetxt(config.dataOutput, scores, delimiter=",")
+
+
+if __name__ == "__main__":
+ if len(sys.argv) != 2:
+ print("Wrong number of arguments specified; expected a single json-string!")
+ exit(1)
+
+ config = AlgorithmArgs.from_sys_args()
+ print(f"Config: {config}")
+
+ if config.executionType == "train":
+ print("Nothing to train, finished!")
+ elif config.executionType == "execute":
+ main(config)
+ else:
+ raise ValueError(f"Unknown execution type '{config.executionType}'; expected either 'train' or 'execute'!")
diff --git a/algorithms/iforest/manifest.json b/algorithms/iforest/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..070ca8779f77d4cd46f7288d787b7a08a5b215ef
--- /dev/null
+++ b/algorithms/iforest/manifest.json
@@ -0,0 +1,65 @@
+{
+ "title": "Isolation Forest (iForest)",
+ "description": "Implementation of https://doi.org/10.1145/2133360.2133363.",
+ "inputDimensionality": "multivariate",
+ "version": "0.3.1",
+ "authors": "Fei Tony Liu and Kai Ming Ting and Zhi-Hua Zhou",
+ "language": "Python",
+ "type": "Detector",
+ "mainFile": "algorithm.py",
+ "learningType": "unsupervised",
+ "executionStep": {
+ "parameters": [
+ {
+ "name": "n_trees",
+ "type": "int",
+ "defaultValue": 100,
+ "optional": "true",
+ "description": "The number of decision trees (base estimators) in the forest (ensemble)."
+ },
+ {
+ "name": "max_samples",
+ "type": "float",
+ "defaultValue": null,
+ "optional": "true",
+ "description": "The number of samples to draw from X to train each base estimator: `max_samples * X.shape[0]`. If unspecified (`null`), then `max_samples=min(256, n_samples)`."
+ },
+ {
+ "name": "max_features",
+ "type": "float",
+ "defaultValue": 1.0,
+ "optional": "true",
+ "description": "The number of features to draw from X to train each base estimator: `max_features * X.shape[1]`."
+ },
+ {
+ "name": "bootstrap",
+ "type": "boolean",
+ "defaultValue": "false",
+ "optional": "true",
+ "description": "If True, individual trees are fit on random subsets of the training data sampled with replacement. If False, sampling without replacement is performed."
+ },
+ {
+ "name": "random_state",
+ "type": "int",
+ "defaultValue": 42,
+ "optional": "true",
+ "description": "Seed for random number generation."
+ },
+ {
+ "name": "verbose",
+ "type": "int",
+ "defaultValue": 0,
+ "optional": "true",
+ "description": "Controls the verbosity of the tree building process logs."
+ },
+ {
+ "name": "n_jobs",
+ "type": "int",
+ "defaultValue": 1,
+ "optional": "true",
+ "description": "The number of jobs to run in parallel. If -1, then the number of jobs is set to the number of cores."
+ }
+ ],
+ "modelInput": "none"
+ }
+}
diff --git a/algorithms/iforest/requirements.txt b/algorithms/iforest/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..bec81306730ae25959144bbb9867f9c0b191a221
--- /dev/null
+++ b/algorithms/iforest/requirements.txt
@@ -0,0 +1,3 @@
+numpy>=1.19.5
+pandas>=1.2.1
+pyod>=0.9.2
diff --git a/algorithms/lof/Dockerfile b/algorithms/lof/Dockerfile
new file mode 100644
index 0000000000000000000000000000000000000000..0388a4239b903d931acfc9bb70f09ed1bb2ca468
--- /dev/null
+++ b/algorithms/lof/Dockerfile
@@ -0,0 +1,13 @@
+FROM ghcr.io/timeeval/pyod:0.3.1
+
+LABEL maintainer="sebastian.schmidl@hpi.de"
+LABEL org.opencontainers.image.licenses=MIT
+
+ENV ALGORITHM_MAIN="/app/algorithm.py"
+
+# install algorithm dependencies
+COPY requirements.txt /app/
+RUN pip install -r /app/requirements.txt
+
+COPY manifest.json /app/
+COPY algorithm.py /app/
diff --git a/algorithms/lof/LICENSE b/algorithms/lof/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..e594ec89208b9bcc64e3b455e283d3ce07f1b817
--- /dev/null
+++ b/algorithms/lof/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2020-2022 Phillip Wenig and Sebastian Schmidl
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/algorithms/lof/README.md b/algorithms/lof/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..5d89f249d65e26d9f43ae82f34f916d51fe651d3
--- /dev/null
+++ b/algorithms/lof/README.md
@@ -0,0 +1,72 @@
+# Local outlier factor (LOF)
+
+|||
+| :--- | :--- |
+| Citekey | BreunigEtAl2000LOF |
+| Source Code | https://github.com/yzhao062/pyod/blob/master/pyod/models/lof.py |
+| Learning type | unsupervised |
+| Input dimensionality | multivariate |
+|||
+
+## Parameters
+
+- `n_neighbors`: `int`, optional (default=20)
+ Number of neighbors to use by default for `kneighbors` queries.
+ If n_neighbors is larger than the number of samples provided, all samples will be used.
+
+- `algorithm`: {'auto', 'ball_tree', 'kd_tree', 'brute'}, optional (default 'auto')
+ Algorithm used to compute the nearest neighbors:
+
+ - 'ball_tree' will use BallTree
+ - 'kd_tree' will use KDTree
+ - 'brute' will use a brute-force search.
+ - 'auto' will attempt to decide the most appropriate algorithm based on the values passed to `fit` method.
+
+ Note: fitting on sparse input will override the setting of this parameter, using brute force.
+ **REMOVED!!**
+
+- `leaf_size`: int, optional (default=30)
+ Leaf size passed to `BallTree` or `KDTree`.
+ This can affect the speed of the construction and query, as well as the memory required to store the tree.
+ The optimal value depends on the nature of the problem.
+
+- `metric`: string or callable, default 'minkowski'
+ Metric used for the distance computation.
+ Any metric from scikit-learn or scipy.spatial.distance can be used.
+ If 'precomputed', the training input X is expected to be a distance matrix.
+ If metric is a callable function, it is called on each pair of instances (rows) and the resulting value recorded.
+ The callable should take two arrays as input and return one value indicating the distance between them.
+ This works for Scipy's metrics, but is less efficient than passing the metric name as a string.
+ Valid values for metric are:
+
+ - from scikit-learn: ['cityblock', 'cosine', 'euclidean', 'l1', 'l2', 'manhattan']
+ - from scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev', 'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski', 'mahalanobis', 'matching', 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'yule']
+
+ See the documentation for scipy.spatial.distance for details on these metrics:
+ http://docs.scipy.org/doc/scipy/reference/spatial.distance.html.
+ **REMOVED!!**
+
+- `p`: integer, optional (default = 2)
+ Parameter for the Minkowski metric from sklearn.metrics.pairwise.pairwise_distances.
+ When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2.
+ For arbitrary p, minkowski_distance (l_p) is used.
+ See http://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.pairwise_distances.
+ **Renamed to `distance_metric_order`!!**
+
+- `metric_params`: dict, optional (default = None)
+ Additional keyword arguments for the metric function.
+ **REMOVED!!**
+
+- `contamination`: float in (0., 0.5), optional (default=0.1)
+ The amount of contamination of the data set, i.e. the proportion of outliers in the data set.
+ When fitting this is used to define the threshold on the decision function.
+ **Automatically determined by algorithm script!!**
+
+- `n_jobs`: int, optional (default = 1)
+ The number of parallel jobs to run for neighbors search.
+ If ``-1``, then the number of jobs is set to the number of CPU cores.
+ Affects only kneighbors and kneighbors_graph methods.
+
+## Citation format (for source code)
+
+> Zhao, Y., Nasrullah, Z. and Li, Z., 2019. PyOD: A Python Toolbox for Scalable Outlier Detection. Journal of machine learning research (JMLR), 20(96), pp.1-7.
diff --git a/algorithms/lof/algorithm.py b/algorithms/lof/algorithm.py
new file mode 100755
index 0000000000000000000000000000000000000000..b89298ce7476b045fb6fa5d2475f428775073da7
--- /dev/null
+++ b/algorithms/lof/algorithm.py
@@ -0,0 +1,82 @@
+#!/usr/bin/env python3
+import argparse
+import json
+import sys
+import numpy as np
+import pandas as pd
+
+from dataclasses import dataclass
+from pyod.models.lof import LOF
+
+
+@dataclass
+class CustomParameters:
+ n_neighbors: int = 20
+ leaf_size: int = 30
+ distance_metric_order: int = 2
+ n_jobs: int = 1
+ algorithm: str = "auto" # using default is fine
+ distance_metric: str = "minkowski" # using default is fine
+ random_state: int = 42
+
+
+class AlgorithmArgs(argparse.Namespace):
+ @staticmethod
+ def from_sys_args() -> 'AlgorithmArgs':
+ args: dict = json.loads(sys.argv[1])
+ custom_parameter_keys = dir(CustomParameters())
+ filtered_parameters = dict(filter(lambda x: x[0] in custom_parameter_keys, args.get("customParameters", {}).items()))
+ args["customParameters"] = CustomParameters(**filtered_parameters)
+ return AlgorithmArgs(**args)
+
+
+def set_random_state(config: AlgorithmArgs) -> None:
+ seed = config.customParameters.random_state
+ import random
+ random.seed(seed)
+ np.random.seed(seed)
+
+
+def load_data(config: AlgorithmArgs) -> np.ndarray:
+ df = pd.read_csv(config.dataInput)
+ data = df.iloc[:, 1:-1].values
+ labels = df.iloc[:, -1].values
+ contamination = labels.sum() / len(labels)
+ # Use smallest positive float as contamination if there are no anomalies in dataset
+ contamination = np.nextafter(0, 1) if contamination == 0. else contamination
+ return data, contamination
+
+
+def main(config: AlgorithmArgs):
+ set_random_state(config)
+ data, contamination = load_data(config)
+
+ clf = LOF(
+ contamination=contamination,
+ n_neighbors=config.customParameters.n_neighbors,
+ leaf_size=config.customParameters.leaf_size,
+ n_jobs=config.customParameters.n_jobs,
+ algorithm=config.customParameters.algorithm,
+ metric=config.customParameters.distance_metric,
+ metric_params=None,
+ p=config.customParameters.distance_metric_order,
+ )
+ clf.fit(data)
+ scores = clf.decision_scores_
+ np.savetxt(config.dataOutput, scores, delimiter=",")
+
+
+if __name__ == "__main__":
+ if len(sys.argv) != 2:
+ print("Wrong number of arguments specified; expected a single json-string!")
+ exit(1)
+
+ config = AlgorithmArgs.from_sys_args()
+ print(f"Config: {config}")
+
+ if config.executionType == "train":
+ print("Nothing to train, finished!")
+ elif config.executionType == "execute":
+ main(config)
+ else:
+ raise ValueError(f"Unknown execution type '{config.executionType}'; expected either 'train' or 'execute'!")
diff --git a/algorithms/lof/manifest.json b/algorithms/lof/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..dbb14eb69f603157038e5c328f2eb780d4ffca0a
--- /dev/null
+++ b/algorithms/lof/manifest.json
@@ -0,0 +1,48 @@
+{
+ "title": "LOF",
+ "description": "Implementation of https://doi.org/10.1145/342009.335388.",
+ "inputDimensionality": "multivariate",
+ "version": "0.3.1",
+ "authors": "Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng, Jörg Sander",
+ "language": "Python",
+ "type": "Detector",
+ "mainFile": "algorithm.py",
+ "learningType": "unsupervised",
+ "executionStep": {
+ "parameters": [
+ {
+ "name": "n_neighbors",
+ "type": "int",
+ "defaultValue": 20,
+ "optional": "true",
+ "description": "Number of neighbors to use by default for `kneighbors` queries. If n_neighbors is larger than the number of samples provided, all samples will be used."
+ },{
+ "name": "leaf_size",
+ "type": "int",
+ "defaultValue": 30,
+ "optional": "true",
+ "description": "Leaf size passed to `BallTree` or `KDTree`. This can affect the speed of the construction and query, as well as the memory required to store the tree. The optimal value depends on the nature of the problem."
+ },{
+ "name": "distance_metric_order",
+ "type": "int",
+ "defaultValue": 2,
+ "optional": "true",
+ "description": "Parameter for the Minkowski metric from sklearn.metrics.pairwise.pairwise_distances. When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used. See http://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.pairwise_distances."
+ },{
+ "name": "n_jobs",
+ "type": "int",
+ "defaultValue": 1,
+ "optional": "true",
+ "description": "The number of parallel jobs to run for neighbors search. If ``-1``, then the number of jobs is set to the number of CPU cores. Affects only kneighbors and kneighbors_graph methods."
+ },
+ {
+ "name": "random_state",
+ "type": "int",
+ "defaultValue": 42,
+ "optional": "true",
+ "description": "Seed for random number generation."
+ }
+ ],
+ "modelInput": "none"
+ }
+}
diff --git a/algorithms/lof/requirements.txt b/algorithms/lof/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..bec81306730ae25959144bbb9867f9c0b191a221
--- /dev/null
+++ b/algorithms/lof/requirements.txt
@@ -0,0 +1,3 @@
+numpy>=1.19.5
+pandas>=1.2.1
+pyod>=0.9.2
diff --git a/config/generated_config_1.json b/config/generated_config_1.json
index 48e3a82fe3af7b251ba25112cbe488ce4969e275..8b53d0d30adcb97a74e36476b26e84c4eb9eae7f 100644
--- a/config/generated_config_1.json
+++ b/config/generated_config_1.json
@@ -1 +1 @@
-{"dimensions": [{"kind": "VFOSC", "equation": {"function": "haversine", "amplitude": 0.659, "frequency_min": 1.4852, "frequency_max": 1.5831, "period": 113}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "NOISE", "mean": -0.07126446458486624, "std": 0.054465935660757935, "length": 53, "position": "start"}]}, {"kind": "EXP_CORRELATION", "dimension": 0, "equation": {"sign": -1, "start": 0.6063, "end": 1.6292, "lag": 58}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "VFOSC", "equation": {"function": "coversine", "amplitude": 1.0824, "frequency_min": 1.0565, "frequency_max": 3.3021, "period": 60}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 0, "equation": {"sign": 1, "step": 0.0135}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "NOISE", "mean": 0.06364979812667934, "std": 0.05330268160230887, "length": 49, "position": "start"}]}, {"kind": "CORRELATION", "dimension": 3, "equation": {"sign": 1, "step": 0.0958}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "VFOSC", "equation": {"function": "vercosine", "amplitude": 4.9105, "frequency_min": 2.345, "frequency_max": 4.7107, "period": 65}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "VF", "length": 276, "position": "end"}]}, {"kind": "CORRELATION", "dimension": 3, "equation": {"sign": 1, "step": 0.0191}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 1, "equation": {"sign": 1, "step": 0.0281}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 2, "equation": {"sign": -1, "start": -4.0881, "end": -2.2027, "lag": 91}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "CORRELATION", "length": 230, "position": "start"}]}, {"kind": "CORRELATION", "dimension": 2, "equation": {"sign": -1, "step": 0.0758}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "VFOSC", "equation": {"function": "havercosine", "amplitude": 4.0596, "frequency_min": 1.1381, "frequency_max": 4.1699, "period": 87}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "VF", "length": 225, "position": "end"}]}, {"kind": "EXP_CORRELATION", "dimension": 2, "equation": {"sign": -1, "start": -4.7305, "end": -1.1057, "lag": 24}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 1, "equation": {"sign": -1, "step": 0.0851}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "NOISE", "mean": -0.015744876527757773, "std": 0.06403746101782093, "length": 42, "position": "middle"}, {"kind": "CORRELATION", "length": 59, "position": "end"}]}, {"kind": "CORRELATION", "dimension": 7, "equation": {"sign": 1, "step": 0.0422}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 0, "equation": {"sign": 1, "step": 0.0405}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 0, "equation": {"sign": 1, "start": -4.6465, "end": 0.1567, "lag": 50}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "CORRELATION", "length": 60, "position": "end"}]}, {"kind": "EXP_CORRELATION", "dimension": 3, "equation": {"sign": 1, "start": -2.5605, "end": 0.4057, "lag": 66}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 13, "equation": {"sign": -1, "step": 0.0817}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 12, "equation": {"sign": -1, "start": -4.5564, "end": -2.7777, "lag": 50}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 18, "equation": {"sign": 1, "start": -2.891, "end": -0.5861, "lag": 88}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}], "length": 20000, "subsystems": [[0, 1, 3, 4, 6, 7, 12, 13, 14, 15, 16, 17, 18, 19], [2, 8, 9, 11], [5], [10]]}
\ No newline at end of file
+{"dimensions": [{"kind": "VFOSC", "equation": {"function": "havercosine", "amplitude": 3.5058, "frequency_min": 0.7498, "frequency_max": 2.8021, "period": 208}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 0, "equation": {"sign": 1, "start": -3.7879, "end": 0.6812, "lag": 80}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "OSC", "equation": {"function": "hacoversine", "amplitude": 1.6154, "frequency": 2.4505}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "NOISE", "mean": -0.06085414386487478, "std": 0.04725996530552058, "length": 268, "position": "middle"}]}, {"kind": "CORRELATION", "dimension": 1, "equation": {"sign": 1, "step": 0.026}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 0, "equation": {"sign": 1, "start": -1.5673, "end": -0.3333, "lag": 20}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 3, "equation": {"sign": 1, "start": 0.121, "end": 0.6966, "lag": 60}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "NOISE", "mean": -0.08045537619793947, "std": 0.08783724015059098, "length": 84, "position": "middle"}]}, {"kind": "EXP_CORRELATION", "dimension": 1, "equation": {"sign": -1, "start": -4.6186, "end": -2.8956, "lag": 58}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 3, "equation": {"sign": 1, "start": -2.1773, "end": -0.0934, "lag": 22}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "CORRELATION", "length": 121, "position": "end"}]}, {"kind": "EXP_CORRELATION", "dimension": 4, "equation": {"sign": 1, "start": -4.2116, "end": 2.2012, "lag": 77}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 5, "equation": {"sign": 1, "step": 0.0118}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "CORRELATION", "length": 119, "position": "start"}]}, {"kind": "OSC", "equation": {"function": "hacoversine", "amplitude": 1.4045, "frequency": 3.1716}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 7, "equation": {"sign": 1, "step": 0.0404}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 10, "equation": {"sign": -1, "start": -2.8646, "end": -2.3917, "lag": 11}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "NOISE", "mean": 0.0055221180245495355, "std": 0.012346785231497875, "length": 412, "position": "end"}]}, {"kind": "VFOSC", "equation": {"function": "coversine", "amplitude": 1.9191, "frequency_min": 1.7938, "frequency_max": 3.0133, "period": 291}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "VF", "length": 321, "position": "end"}]}, {"kind": "OSC", "equation": {"function": "hacoversine", "amplitude": 4.4204, "frequency": 0.438}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 14, "equation": {"sign": -1, "start": 1.3707, "end": 2.4565, "lag": 66}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "NOISE", "mean": -0.06977781377044004, "std": 0.0411342176663674, "length": 307, "position": "middle"}]}, {"kind": "OSC", "equation": {"function": "cos", "amplitude": 1.6806, "frequency": 3.5378}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "NOISE", "mean": -0.011395843937854158, "std": 0.0746382003892833, "length": 362, "position": "start"}]}, {"kind": "EXP_CORRELATION", "dimension": 9, "equation": {"sign": 1, "start": -2.5764, "end": -1.3961, "lag": 69}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 12, "equation": {"sign": -1, "step": 0.0764}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 8, "equation": {"sign": -1, "step": 0.021}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}], "length": 20000, "subsystems": [[0, 1, 3, 4, 5, 6, 7, 8, 9, 11, 17, 19], [2], [10, 12, 18], [13], [14, 15], [16]]}
\ No newline at end of file
diff --git a/config/generated_config_10.json b/config/generated_config_10.json
index 332f38967b33b1af4aa6d3b7214469b3d616651c..8f42b4c583ab6b4f4ee6e4e018ae88fb6b39d534 100644
--- a/config/generated_config_10.json
+++ b/config/generated_config_10.json
@@ -1 +1 @@
-{"dimensions": [{"kind": "VFOSC", "equation": {"function": "vercosine", "amplitude": 4.8013, "frequency_min": 1.8598, "frequency_max": 3.7604, "period": 230}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 0, "equation": {"sign": -1, "start": -4.2783, "end": 1.6133, "lag": 42}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "VFOSC", "equation": {"function": "coversine", "amplitude": 0.4991, "frequency_min": 2.8643, "frequency_max": 4.7158, "period": 158}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 2, "equation": {"sign": -1, "start": -4.1175, "end": 2.2307, "lag": 62}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 0, "equation": {"sign": -1, "start": -4.7731, "end": 2.4128, "lag": 65}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "VFOSC", "equation": {"function": "sin", "amplitude": 4.5517, 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\ No newline at end of file
diff --git a/config/generated_config_11.json b/config/generated_config_11.json
index 924623e58efd7f84be773e0fdfcb275a6c34eebf..4e3d56ea71992c2acca0a80c5db161689ad730c5 100644
--- a/config/generated_config_11.json
+++ b/config/generated_config_11.json
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/config/generated_config_12.json b/config/generated_config_12.json
index 18bc2c2a019a6ad329abff7aac99d2f76c5724f3..76cfea22af475fedae187258b08ca014f5616cbb 100644
--- a/config/generated_config_12.json
+++ b/config/generated_config_12.json
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/config/generated_config_13.json b/config/generated_config_13.json
index 42dd2c8f463640b65e4cd0269382bff46d2f8c5b..c41de338056760c2dba8698b924e0a8d314d78e8 100644
--- a/config/generated_config_13.json
+++ b/config/generated_config_13.json
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/config/generated_config_14.json b/config/generated_config_14.json
index 79a4e68ff770df815cd1d031661ce36a3ea8ce51..e1f1177b40dd5be93ecb200ff8e399b8d7b86bad 100644
--- a/config/generated_config_14.json
+++ b/config/generated_config_14.json
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/config/generated_config_15.json b/config/generated_config_15.json
index 9f79be8943b2cd506bb48f41df09c13e92736cc3..8cf6766807ecc380babcc3ac3994a31bacc6821f 100644
--- a/config/generated_config_15.json
+++ b/config/generated_config_15.json
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/config/generated_config_16.json b/config/generated_config_16.json
index 6b761800e58bbd9374d71c7d0218712acb9a3d0e..dab92cd7382b27e05cf857af5c769ebc933f578a 100644
--- a/config/generated_config_16.json
+++ b/config/generated_config_16.json
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/config/generated_config_17.json b/config/generated_config_17.json
index fc83c84b9bd9425facaab9ce01e9d5b012e92ef1..d528f5f0c0f06fafb6c4dbf4a25ad11b6395573c 100644
--- a/config/generated_config_17.json
+++ b/config/generated_config_17.json
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/config/generated_config_18.json b/config/generated_config_18.json
index 0d6c1e74dd3c6b4a34750265ef831ba6d53685ba..f2633e37aca035b6176522dfa29ad0421b74c73a 100644
--- a/config/generated_config_18.json
+++ b/config/generated_config_18.json
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/config/generated_config_19.json b/config/generated_config_19.json
index 5ee9c8b37e4a8e1e4103cd49d70853a2d704cd18..68a941b17675c1810c17dbe56009efa6d343b33d 100644
--- a/config/generated_config_19.json
+++ b/config/generated_config_19.json
@@ -1 +1 @@
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0.0651078140772057, "std": 0.08562557889288173, "length": 101, "position": "middle"}]}, {"kind": "EXP_CORRELATION", "dimension": 11, "equation": {"sign": -1, "start": -3.0385, "end": 0.2878, "lag": 97}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "NOISE", "mean": -0.030620515221057937, "std": 0.09384815915634465, "length": 62, "position": "start"}]}, {"kind": "EXP_CORRELATION", "dimension": 62, "equation": {"sign": -1, "start": -0.5859, "end": 1.1443, "lag": 54}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 36, "equation": {"sign": 1, "start": -1.966, "end": 1.6735, "lag": 22}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 50, "equation": {"sign": 1, "start": -2.2539, "end": -1.1853, "lag": 64}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 79, "equation": {"sign": 1, "step": 0.0999}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 24, "equation": {"sign": -1, "start": -3.3317, "end": 0.3871, "lag": 23}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "NOISE", "mean": 0.0874579233564162, "std": 0.048724846862960164, "length": 74, "position": "start"}]}, {"kind": "VFOSC", "equation": {"function": "sin", "amplitude": 4.6089, "frequency_min": 0.9568, "frequency_max": 4.1821, "period": 269}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "VF", "length": 100, "position": "middle"}]}, {"kind": "EXP_CORRELATION", "dimension": 9, "equation": {"sign": -1, "start": -3.4036, "end": 1.4563, "lag": 59}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 67, "equation": {"sign": -1, "step": 0.0769}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 62, "equation": {"sign": -1, "step": 0.0146}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "OSC", "equation": {"function": "haversine", "amplitude": 0.1666, "frequency": 1.8902}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "NOISE", "mean": 0.012379627777679536, "std": 0.04826266661227804, "length": 92, "position": "start"}]}, {"kind": "EXP_CORRELATION", "dimension": 93, "equation": {"sign": 1, "start": -0.2139, "end": 0.1381, "lag": 28}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "CORRELATION", "length": 8, "position": "end"}]}, {"kind": "CORRELATION", "dimension": 65, "equation": {"sign": 1, "step": 0.0801}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 75, "equation": {"sign": 1, "start": 1.0346, "end": 2.2383, "lag": 27}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 40, "equation": {"sign": 1, "start": -1.9755, "end": -1.6946, "lag": 36}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 43, "equation": {"sign": 1, "step": 0.0315}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "NOISE", "mean": 0.031585459409328165, "std": 0.07104619484307044, "length": 62, "position": "middle"}]}, {"kind": "CORRELATION", "dimension": 27, "equation": {"sign": 1, "step": 0.0977}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 59, "equation": {"sign": 1, "start": -4.0751, "end": 1.4521, "lag": 29}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "VFOSC", "equation": {"function": "versine", "amplitude": 0.3437, "frequency_min": 0.1911, "frequency_max": 1.5659, "period": 197}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "VF", "length": 75, "position": "middle"}]}, {"kind": "CORRELATION", "dimension": 81, "equation": {"sign": -1, "step": 0.0759}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "CORRELATION", "length": 96, "position": "start"}]}, {"kind": "EXP_CORRELATION", "dimension": 62, "equation": {"sign": -1, "start": 0.1179, "end": 2.0019, "lag": 5}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "CORRELATION", "length": 66, "position": "end"}]}], "length": 20000, "subsystems": [[0, 1, 2, 3, 4, 7, 8, 15, 19, 23, 25, 28, 34, 37, 38, 54, 61, 63, 69, 70, 75, 79, 80, 93, 100, 102], [5, 9, 13, 18, 29, 32, 33, 36, 39, 60, 66, 67, 72, 73, 74, 76, 83, 86, 91, 96, 97], [6, 12, 14, 20, 27, 31, 41, 52, 57, 59, 65, 68, 101, 105, 106], [10, 24, 94], [11, 89], [16, 21, 49, 50, 92], [17, 30, 42, 47, 48, 84, 87], [22, 64, 88], [26], [35], [40, 56, 103], [43, 104], [44], [45, 46, 82], [51], [53], [55, 77], [58], [62, 90, 98, 109], [71], [78, 85], [81, 108], [95], [99], [107]]}
\ No newline at end of file
diff --git a/config/generated_config_2.json b/config/generated_config_2.json
index eda3ad7ffc43ec69c7f116dfa974a70aef7d584c..957c1e62a49af1e1a39b5abbfd0422b0a9ed5937 100644
--- a/config/generated_config_2.json
+++ b/config/generated_config_2.json
@@ -1 +1 @@
-{"dimensions": [{"kind": "OSC", "equation": {"function": "haversine", "amplitude": 3.4321, "frequency": 3.3002}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 0, "equation": {"sign": 1, "step": 0.011}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 0, "equation": {"sign": 1, "step": 0.0936}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "VFOSC", "equation": {"function": "cos", "amplitude": 2.4439, "frequency_min": 0.4249, "frequency_max": 3.6146, "period": 255}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "VF", "length": 159, "position": "middle"}]}, {"kind": "VFOSC", "equation": {"function": "coversine", "amplitude": 3.8568, "frequency_min": 1.7674, "frequency_max": 2.1862, "period": 57}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 2, "equation": {"sign": -1, "step": 0.0551}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 3, "equation": {"sign": -1, "start": -1.0692, "end": -0.9417, "lag": 30}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 2, "equation": {"sign": -1, "step": 0.0732}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 2, "equation": {"sign": -1, "start": 0.1578, "end": 1.4064, "lag": 27}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "NOISE", "mean": -0.08247267442128084, "std": 0.08498542273567589, "length": 71, "position": "middle"}]}, {"kind": "OSC", "equation": {"function": "versine", "amplitude": 0.5073, "frequency": 3.9361}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 6, "equation": {"sign": -1, "step": 0.0831}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 2, "equation": {"sign": 1, "step": 0.0911}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "OSC", "equation": {"function": "covercosine", "amplitude": 4.4181, "frequency": 1.7907}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 2, "equation": {"sign": -1, "step": 0.0458}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "CORRELATION", "length": 77, "position": "middle"}]}, {"kind": "CORRELATION", "dimension": 9, "equation": {"sign": -1, "step": 0.0914}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "NOISE", "mean": -0.09226340669863405, "std": 0.047956256203088306, "length": 155, "position": "middle"}]}, {"kind": "CORRELATION", "dimension": 8, "equation": {"sign": 1, "step": 0.0551}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 4, "equation": {"sign": -1, "step": 0.0684}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "NOISE", "mean": 0.046051116023375444, "std": 0.09664518268077563, "length": 120, "position": "middle"}]}, {"kind": "CORRELATION", "dimension": 1, "equation": {"sign": 1, "step": 0.0551}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "VFOSC", "equation": {"function": "hacoversine", "amplitude": 2.3326, "frequency_min": 0.7756, "frequency_max": 3.7642, "period": 141}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "VF", "length": 155, "position": "middle"}]}, {"kind": "VFOSC", "equation": {"function": "haversine", "amplitude": 1.2571, "frequency_min": 1.1823, "frequency_max": 2.3211, "period": 146}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "VF", "length": 113, "position": "middle"}]}, {"kind": "EXP_CORRELATION", "dimension": 0, "equation": {"sign": -1, "start": -3.8049, "end": 0.3825, "lag": 99}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 14, "equation": {"sign": 1, "start": -1.8739, "end": 0.9716, "lag": 88}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 12, "equation": {"sign": 1, "step": 0.0869}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "NOISE", "mean": 0.060929478136377974, "std": 0.025500405564605196, "length": 146, "position": "start"}]}, {"kind": "CORRELATION", "dimension": 21, "equation": {"sign": 1, "step": 0.0968}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 21, "equation": {"sign": -1, "step": 0.0989}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}], "length": 20000, "subsystems": [[0, 1, 2, 5, 7, 8, 11, 13, 15, 17, 20], [3, 6, 10], [4, 16], [9, 14, 21, 23, 24], [12, 22], [18], [19]]}
+{"dimensions": [{"kind": "VFOSC", "equation": {"function": "versine", "amplitude": 0.9679, "frequency_min": 3.9187, "frequency_max": 4.5153, "period": 117}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 0, "equation": {"sign": -1, "start": -4.2642, "end": -2.4821, "lag": 87}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "NOISE", "mean": -0.0899577497047375, "std": 0.07688526445317151, "length": 167, "position": "end"}]}, {"kind": "VFOSC", "equation": {"function": "coversine", "amplitude": 2.0197, "frequency_min": 0.1863, "frequency_max": 1.4363, "period": 154}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "VF", "length": 211, "position": "end"}]}, {"kind": "OSC", "equation": {"function": "versine", "amplitude": 3.5487, "frequency": 3.0297}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 2, "equation": {"sign": -1, "start": 0.0703, "end": 1.1064, "lag": 82}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "OSC", "equation": {"function": "sin", "amplitude": 0.5191, "frequency": 4.1127}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 4, "equation": {"sign": 1, "step": 0.0668}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 5, "equation": {"sign": -1, "step": 0.0287}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 5, "equation": {"sign": -1, "step": 0.0827}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "CORRELATION", "length": 121, "position": "start"}]}, {"kind": "EXP_CORRELATION", "dimension": 1, "equation": {"sign": 1, "start": -4.4941, "end": -1.422, "lag": 91}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 3, "equation": {"sign": 1, "step": 0.0927}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "CORRELATION", "length": 187, "position": "start"}]}, {"kind": "EXP_CORRELATION", "dimension": 5, "equation": {"sign": -1, "start": -0.9747, "end": -0.1077, "lag": 85}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 11, "equation": {"sign": -1, "step": 0.0777}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 0, "equation": {"sign": -1, "start": -3.281, "end": -1.7014, "lag": 82}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "OSC", "equation": {"function": "haversine", "amplitude": 3.5048, "frequency": 4.297}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "NOISE", "mean": -0.000503348572280668, "std": 0.033433478138856755, "length": 214, "position": "middle"}]}, {"kind": "CORRELATION", "dimension": 12, "equation": {"sign": 1, "step": 0.0471}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 4, "equation": {"sign": 1, "step": 0.0755}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 12, "equation": {"sign": 1, "start": -4.6832, "end": -2.6226, "lag": 38}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "OSC", "equation": {"function": "hacoversine", "amplitude": 3.1541, "frequency": 3.9247}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "NOISE", "mean": -0.0721001388580239, "std": 0.049614854923479466, "length": 221, "position": "start"}]}, {"kind": "VFOSC", "equation": {"function": "cos", "amplitude": 1.0475, "frequency_min": 2.9129, "frequency_max": 4.9011, "period": 202}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "NOISE", "mean": -0.07321324568777517, "std": 0.07473325346119904, "length": 151, "position": "start"}]}, {"kind": "VFOSC", "equation": {"function": "versine", "amplitude": 0.2908, "frequency_min": 3.5838, "frequency_max": 4.8633, "period": 215}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "VF", "length": 187, "position": "start"}]}, {"kind": "EXP_CORRELATION", "dimension": 12, "equation": {"sign": -1, "start": 1.6421, "end": 2.0987, "lag": 58}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "CORRELATION", "length": 118, "position": "middle"}]}, {"kind": "CORRELATION", "dimension": 3, "equation": {"sign": -1, "step": 0.0919}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "VFOSC", "equation": {"function": "vercosine", "amplitude": 1.9617, "frequency_min": 1.3437, "frequency_max": 4.6243, "period": 162}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "NOISE", "mean": 0.06391503948490115, "std": 0.026181890657697923, "length": 184, "position": "end"}]}, {"kind": "VFOSC", "equation": {"function": "hacoversine", "amplitude": 0.1802, "frequency_min": 1.2679, "frequency_max": 4.2369, "period": 274}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "NOISE", "mean": -0.07888696402496378, "std": 0.09748548356627576, "length": 233, "position": "end"}]}], "length": 20000, "subsystems": [[0, 1, 9, 13], [2, 4, 6, 16], [3, 10, 22], [5, 7, 8, 11, 12, 15, 17, 21], [14], [18], [19], [20], [23], [24]]}
\ No newline at end of file
diff --git a/config/generated_config_20.json b/config/generated_config_20.json
index 58171ee9b29aefbea369ad8c02fc33304a3755ff..e062aaeda47bfe73226878cd4d167223d2aab08f 100644
--- a/config/generated_config_20.json
+++ b/config/generated_config_20.json
@@ -1 +1 @@
-{"dimensions": [{"kind": "VFOSC", "equation": {"function": "coversine", "amplitude": 3.6263, "frequency_min": 3.4077, "frequency_max": 4.536, "period": 179}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 0, "equation": {"sign": -1, "start": -4.8602, "end": -0.2596, "lag": 96}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "VFOSC", "equation": {"function": "versine", "amplitude": 3.9176, "frequency_min": 0.1184, "frequency_max": 0.2402, "period": 275}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 1, "equation": {"sign": -1, "step": 0.0968}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 3, "equation": {"sign": -1, "start": -3.4551, "end": -2.9685, "lag": 74}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 4, "equation": {"sign": -1, "step": 0.0377}, "noise": {"mean": 0.0, "std": 0.1}, 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0.0, "std": 0.1}, "anomalies": []}], "length": 20000, "subsystems": [[0, 2, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 16, 18, 19, 20, 23, 24, 25, 28, 30, 32, 33, 34, 35, 36, 41, 43, 46, 47, 53, 54, 55, 57, 58, 62, 64, 65, 66, 67, 71, 76, 78, 79, 82, 88, 90, 92, 93, 94, 96, 97, 98, 105, 108, 111, 112, 113], [1, 3, 9, 15, 17, 38, 77], [21, 42], [22, 40], [26, 56, 80, 107], [27], [29, 106], [31, 39, 87], [37, 44, 68, 69], [45, 84, 95, 99, 114], [48, 49, 51, 73], [50, 52], [59, 60], [61, 91], [63], [70, 81, 83, 89, 104], [72], [74, 102], [75, 85], [86, 109], [100], [101], [103], [110]]}
\ No newline at end of file
diff --git a/config/generated_config_3.json b/config/generated_config_3.json
index b7c930020d9773033d034cd12a062c7b82d0dbbd..c19d5d4f4760c68107c31a62aed08c237e7557fd 100644
--- a/config/generated_config_3.json
+++ b/config/generated_config_3.json
@@ -1 +1 @@
-{"dimensions": [{"kind": "OSC", "equation": {"function": "hacovercosine", "amplitude": 3.138, "frequency": 0.8616}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 0, "equation": {"sign": 1, "start": -3.6617, "end": -3.3785, "lag": 74}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 1, "equation": {"sign": 1, "start": -1.9676, "end": -1.474, "lag": 81}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "OSC", "equation": {"function": "vercosine", "amplitude": 1.4992, "frequency": 1.0558}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "NOISE", "mean": 0.026023815011095075, "std": 0.08221041745381306, "length": 122, "position": "middle"}]}, {"kind": "CORRELATION", "dimension": 2, "equation": {"sign": -1, "step": 0.0478}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 2, "equation": {"sign": -1, "start": -3.9206, "end": 2.0833, "lag": 8}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "NOISE", "mean": 0.001491435504330882, "std": 0.07454219566004593, "length": 32, "position": "start"}]}, {"kind": "VFOSC", "equation": {"function": "cos", "amplitude": 2.4265, "frequency_min": 1.3759, "frequency_max": 4.7925, "period": 109}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 1, "equation": {"sign": 1, "step": 0.0956}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 5, "equation": {"sign": 1, "start": -4.5601, "end": -1.2569, "lag": 63}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 7, "equation": {"sign": -1, "step": 0.0132}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "CORRELATION", "length": 24, "position": "middle"}]}, {"kind": "EXP_CORRELATION", "dimension": 1, "equation": {"sign": 1, "start": -1.5252, "end": 1.7293, "lag": 50}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "CORRELATION", "length": 31, "position": "start"}]}, {"kind": "OSC", "equation": {"function": "havercosine", "amplitude": 1.5311, "frequency": 4.2683}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "NOISE", "mean": 0.0317131588969638, "std": 0.01664359665784205, "length": 117, "position": "middle"}]}, {"kind": "CORRELATION", "dimension": 10, "equation": {"sign": -1, "step": 0.0979}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "VFOSC", "equation": {"function": "sin", "amplitude": 0.6684, "frequency_min": 1.4195, "frequency_max": 4.8839, "period": 81}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "VF", "length": 126, "position": "end"}]}, {"kind": "CORRELATION", "dimension": 1, "equation": {"sign": -1, "step": 0.0906}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "OSC", "equation": {"function": "sin", "amplitude": 4.9084, "frequency": 0.1755}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "NOISE", "mean": 0.056484684447407535, "std": 0.08131604637239374, "length": 122, "position": "middle"}]}, {"kind": "EXP_CORRELATION", "dimension": 5, "equation": {"sign": -1, "start": -0.6608, "end": -0.3313, "lag": 17}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 9, "equation": {"sign": 1, "step": 0.0862}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "OSC", "equation": {"function": "hacovercosine", "amplitude": 2.7603, "frequency": 2.465}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "VFOSC", "equation": {"function": "coversine", "amplitude": 4.4802, "frequency_min": 1.7705, "frequency_max": 4.2024, "period": 50}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "VF", "length": 105, "position": "middle"}]}, {"kind": "EXP_CORRELATION", "dimension": 18, "equation": {"sign": -1, "start": -1.5283, "end": 1.8902, "lag": 41}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 0, "equation": {"sign": -1, "start": -2.6733, "end": -1.944, "lag": 78}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 19, "equation": {"sign": 1, "step": 0.0221}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 10, "equation": {"sign": 1, "start": -2.5343, "end": -2.1579, "lag": 53}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 6, "equation": {"sign": 1, "start": -2.4442, "end": -0.7244, "lag": 68}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "CORRELATION", "length": 124, "position": "end"}]}, {"kind": "CORRELATION", "dimension": 1, "equation": {"sign": -1, "step": 0.0497}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "NOISE", "mean": 0.03041775655433393, "std": 0.06075438910476462, "length": 31, "position": "start"}]}, {"kind": "CORRELATION", "dimension": 18, "equation": {"sign": 1, "step": 0.0712}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "NOISE", "mean": -0.03500020016181778, "std": 0.046323699926851584, "length": 133, "position": "end"}]}, {"kind": "EXP_CORRELATION", "dimension": 16, "equation": {"sign": 1, "start": -4.7206, "end": -3.9291, "lag": 39}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 4, "equation": {"sign": -1, "start": -1.0291, "end": 2.458, "lag": 4}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "CORRELATION", "length": 25, "position": "middle"}]}, {"kind": "CORRELATION", "dimension": 18, "equation": {"sign": -1, "step": 0.0579}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}], "length": 20000, "subsystems": [[0, 1, 2, 4, 5, 7, 8, 9, 10, 12, 14, 16, 17, 21, 23, 25, 27, 28], [3], [6, 24], [11], [13], [15], [18, 20, 26, 29], [19, 22]]}
\ No newline at end of file
+{"dimensions": [{"kind": "VFOSC", "equation": {"function": "covercosine", "amplitude": 0.8803, "frequency_min": 0.1739, "frequency_max": 3.9554, "period": 190}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 0, "equation": {"sign": -1, "step": 0.0817}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 0, "equation": {"sign": -1, "start": -0.3016, "end": 1.6685, "lag": 2}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "CORRELATION", "length": 119, "position": "start"}]}, {"kind": "OSC", "equation": {"function": "versine", "amplitude": 4.1007, "frequency": 3.2731}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "OSC", "equation": {"function": "hacoversine", "amplitude": 2.1593, "frequency": 2.8923}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 0, "equation": {"sign": 1, "start": -0.4769, "end": -0.0816, "lag": 54}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "VFOSC", "equation": {"function": "haversine", "amplitude": 3.6366, "frequency_min": 1.3304, "frequency_max": 4.3381, "period": 153}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "NOISE", "mean": -0.027977376937038598, "std": 0.03656277635823338, "length": 295, "position": "start"}]}, {"kind": "EXP_CORRELATION", "dimension": 4, "equation": {"sign": 1, "start": -3.7203, "end": 1.1782, "lag": 31}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "VFOSC", "equation": {"function": "cos", "amplitude": 4.6091, "frequency_min": 0.4326, "frequency_max": 4.4908, "period": 202}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 3, "equation": {"sign": -1, "start": -3.6505, "end": -1.9159, "lag": 32}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "CORRELATION", "length": 222, "position": "middle"}]}, {"kind": "OSC", "equation": {"function": "vercosine", "amplitude": 0.8804, "frequency": 0.5613}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 10, "equation": {"sign": 1, "start": -1.9524, "end": 1.5036, "lag": 31}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "VFOSC", "equation": {"function": "havercosine", "amplitude": 1.1917, "frequency_min": 0.2172, "frequency_max": 2.9026, "period": 287}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "NOISE", "mean": 0.06022432624318369, "std": 0.010952679418388341, "length": 304, "position": "start"}]}, {"kind": "EXP_CORRELATION", "dimension": 8, "equation": {"sign": -1, "start": 1.9281, "end": 2.2386, "lag": 73}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 11, "equation": {"sign": 1, "start": -4.972, "end": 1.5442, "lag": 97}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 12, "equation": {"sign": 1, "step": 0.0118}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 7, "equation": {"sign": 1, "step": 0.0384}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 11, "equation": {"sign": 1, "start": -3.3882, "end": -0.6081, "lag": 50}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "NOISE", "mean": 0.012551009071831792, "std": 0.07176810983058549, "length": 260, "position": "start"}]}, {"kind": "CORRELATION", "dimension": 4, "equation": {"sign": 1, "step": 0.059}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "CORRELATION", "length": 195, "position": "start"}]}, {"kind": "CORRELATION", "dimension": 18, "equation": {"sign": 1, "step": 0.0285}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 13, "equation": {"sign": -1, "step": 0.0759}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "CORRELATION", "length": 257, "position": "middle"}]}, {"kind": "CORRELATION", "dimension": 0, "equation": {"sign": 1, "step": 0.0496}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "CORRELATION", "length": 118, "position": "end"}]}, {"kind": "EXP_CORRELATION", "dimension": 13, "equation": {"sign": -1, "start": -3.8655, "end": -2.3797, "lag": 31}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 18, "equation": {"sign": -1, "start": -3.8437, "end": -3.0296, "lag": 87}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 21, "equation": {"sign": -1, "start": -4.6587, "end": -3.9894, "lag": 37}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 24, "equation": {"sign": 1, "step": 0.053}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 0, "equation": {"sign": -1, "start": -1.995, "end": -0.43, "lag": 99}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 2, "equation": {"sign": 1, "start": -0.813, "end": -0.0287, "lag": 35}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 18, "equation": {"sign": 1, "step": 0.0825}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "OSC", "equation": {"function": "versine", "amplitude": 0.4374, "frequency": 2.3534}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "NOISE", "mean": -0.09579042656470504, "std": 0.06199795664180461, "length": 224, "position": "end"}]}], "length": 20000, "subsystems": [[0, 1, 2, 5, 21, 24, 25, 26, 27], [3, 9], [4, 7, 16, 18, 19, 23, 28], [6], [8, 13, 20, 22], [10, 11, 14, 17], [12, 15], [29]]}
\ No newline at end of file
diff --git a/config/generated_config_4.json b/config/generated_config_4.json
index f7526ae1d3bc06cdf192f1a95aa4483879f642b8..18e9a8032543637235cfb3dab1ea36148bf1e7b2 100644
--- a/config/generated_config_4.json
+++ b/config/generated_config_4.json
@@ -1 +1 @@
-{"dimensions": [{"kind": "VFOSC", "equation": {"function": "covercosine", "amplitude": 0.3095, "frequency_min": 0.6524, "frequency_max": 1.7714, "period": 67}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 0, "equation": {"sign": 1, "start": -1.5565, "end": 1.3285, "lag": 80}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 0, "equation": {"sign": 1, "start": -4.7964, "end": 2.0057, "lag": 90}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "OSC", "equation": {"function": "vercosine", "amplitude": 1.9898, "frequency": 3.9599}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "NOISE", "mean": 0.07791982388521732, "std": 0.03421488724949453, "length": 164, "position": "start"}]}, {"kind": "VFOSC", "equation": {"function": "hacoversine", "amplitude": 2.2076, "frequency_min": 2.1834, "frequency_max": 4.5343, "period": 211}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "VF", "length": 126, "position": "middle"}]}, {"kind": "CORRELATION", "dimension": 0, "equation": {"sign": -1, "step": 0.0657}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 5, "equation": {"sign": -1, "start": -2.3335, "end": 1.949, "lag": 21}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 0, "equation": {"sign": -1, "step": 0.0672}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 0, "equation": {"sign": 1, "start": -2.326, "end": -1.5297, "lag": 53}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 6, "equation": {"sign": -1, "start": -3.3104, "end": -2.8452, "lag": 90}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 7, "equation": {"sign": 1, "step": 0.0244}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 10, "equation": {"sign": 1, "step": 0.0727}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 2, "equation": {"sign": -1, "start": -3.9175, "end": -1.7328, "lag": 48}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "OSC", "equation": {"function": "vercosine", "amplitude": 0.1953, "frequency": 4.0927}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "NOISE", "mean": -0.06396230560584404, "std": 0.08960338554915678, "length": 128, "position": "middle"}]}, {"kind": "EXP_CORRELATION", "dimension": 2, "equation": {"sign": -1, "start": -4.041, "end": -2.5243, "lag": 99}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 2, "equation": {"sign": 1, "start": -3.9144, "end": 0.0518, "lag": 26}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "VFOSC", "equation": {"function": "hacovercosine", "amplitude": 3.4986, "frequency_min": 2.0669, 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\ No newline at end of file
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\ No newline at end of file
diff --git a/config/generated_config_5.json b/config/generated_config_5.json
index 713929711d2faa893f64abd7fe33212fe31ccadf..9f5b0ae1b92e593f2576f6bf63daa86661218653 100644
--- a/config/generated_config_5.json
+++ b/config/generated_config_5.json
@@ -1 +1 @@
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\ No newline at end of file
+{"dimensions": [{"kind": "VFOSC", "equation": {"function": "haversine", "amplitude": 0.8406, "frequency_min": 0.9034, "frequency_max": 2.6886, "period": 294}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 0, "equation": {"sign": 1, "start": -0.9226, "end": 1.0775, "lag": 6}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "CORRELATION", "dimension": 0, "equation": {"sign": 1, "step": 0.0744}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 1, "equation": {"sign": 1, "start": -4.0092, "end": 0.2303, "lag": 53}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": []}, {"kind": "EXP_CORRELATION", "dimension": 1, "equation": {"sign": 1, "start": 0.6712, "end": 2.1261, "lag": 84}, "noise": {"mean": 0.0, "std": 0.1}, "anomalies": [{"kind": "CORRELATION", "length": 43, "position": "middle"}]}, {"kind": "EXP_CORRELATION", "dimension": 4, "equation": {"sign": 1, "start": -3.9991, "end": 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\ No newline at end of file
diff --git a/config/generated_config_6.json b/config/generated_config_6.json
index 456b40b146ebd2b5a9d9c5a78f14ed34399b95da..257a8c5d974d95640dd66afb0ade231135cf243a 100644
--- a/config/generated_config_6.json
+++ b/config/generated_config_6.json
@@ -1 +1 @@
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\ No newline at end of file
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"subsystems": [[0, 1, 2, 3, 4, 5, 6, 7, 12, 13, 15, 17, 18, 19, 20, 21, 22, 24, 25, 26, 27, 28, 29, 33, 34, 36, 37, 39, 40, 42, 43], [8, 14], [9, 10, 16, 31], [11], [23, 38], [30], [32], [35], [41], [44]]}
\ No newline at end of file
diff --git a/config/generated_config_7.json b/config/generated_config_7.json
index a60fed3931113066b7269ff0dbe34651356d4dc3..7f3e9a8a53afdc9de77549f0a52be989bef95159 100644
--- a/config/generated_config_7.json
+++ b/config/generated_config_7.json
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/config/generated_config_8.json b/config/generated_config_8.json
index d932822ee46480edb2b838b85129c1652058f4fc..f3b8e3f631929030bd0cf4da9719182cab842b32 100644
--- a/config/generated_config_8.json
+++ b/config/generated_config_8.json
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/config/generated_config_9.json b/config/generated_config_9.json
index 5b29d8704db80a4818f0a25b666733ba1888736c..c0430ede7081fa6a763267727d44d796b32c6143 100644
--- a/config/generated_config_9.json
+++ b/config/generated_config_9.json
@@ -1 +1 @@
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"anomalies": [{"kind": "NOISE", "mean": 0.09386806978695575, "std": 0.07803176935553123, "length": 108, "position": "start"}]}], "length": 20000, "subsystems": [[0, 1, 2, 3, 8, 10, 17, 20, 21, 23, 31, 38, 40, 43, 44, 50, 52], [4], [5, 14, 37], [6, 7, 9, 11, 18, 27, 51, 58], [12], [13], [15, 25, 49], [16], [19, 26, 33, 35, 41, 46, 47, 53, 56], [22, 32], [24], [28], [29, 42], [30], [34], [36], [39], [45], [48], [54], [55], [57], [59]]}
\ No newline at end of file
diff --git a/generator/dataset.py b/generator/dataset.py
index bf8b54a3666d7f3a2b694c590f48984ef370964a..06e6a93ed35cc530767de409797b4ffdbd06ab5b 100644
--- a/generator/dataset.py
+++ b/generator/dataset.py
@@ -50,7 +50,7 @@ class DatasetGenerator():
self.dimensions_labels[idx] = dimension.get_labels()
self.labels = np.bitwise_or.reduce(np.stack((dimension.get_labels(), self.labels)), axis=0)
- return self.dataset, self.training_dataset, self.labels
+ return self.dataset, self.training_dataset, self.labels, self.dimensions_labels
def get_splitted_data(self) -> List:
"""
diff --git a/main.py b/main.py
index 624cf2fc6eaf6d676130e6aa7d644bfab6709d2e..8e3543d92ad5f879fc91343909d0567ad05c2b21 100755
--- a/main.py
+++ b/main.py
@@ -90,7 +90,7 @@ if __name__ == "__main__":
# Data generation
generator = DatasetGenerator(config)
- dataset, train_dataset, labels = generator.generate()
+ dataset, train_dataset, labels, variables_labels = generator.generate()
splitted_data, splitted_train, splitted_labels = generator.get_splitted_data()
# Save data to disk
@@ -106,6 +106,8 @@ if __name__ == "__main__":
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:
@@ -155,7 +157,7 @@ if __name__ == "__main__":
params = algo_params[algo]
train = params.pop("training")
trainer = BaseTrainer(f"{INPUT_DIR}/{config_name}", algo, train, **params)
- trainer.run()
+ trainer.start()
trainers.append(trainer)
# =================================================================================================================
diff --git a/scripts/gen_config.sh b/scripts/gen_config.sh
index e398d6eb2c75e9db78375d6a5d76d6f9247538a2..0e56537663165014a61de5f4f3238f4251fc2cdb 100755
--- a/scripts/gen_config.sh
+++ b/scripts/gen_config.sh
@@ -1,22 +1,22 @@
#!/bin/sh
-# ./config_maker/config_maker.py -d 20 -l 20000 -c 5 -o ./config/generated_config_1.json
-./config_maker/config_maker.py -d 25 -l 20000 -c 5 -o ./config/generated_config_2.json
-./config_maker/config_maker.py -d 30 -l 20000 -c 5 -o ./config/generated_config_3.json
-# ./config_maker/config_maker.py -d 35 -l 20000 -c 5 -o ./config/generated_config_4.json
-# ./config_maker/config_maker.py -d 40 -l 20000 -c 5 -o ./config/generated_config_5.json
-# ./config_maker/config_maker.py -d 45 -l 20000 -c 5 -o ./config/generated_config_6.json
-./config_maker/config_maker.py -d 50 -l 20000 -c 5 -o ./config/generated_config_7.json
-./config_maker/config_maker.py -d 55 -l 20000 -c 5 -o ./config/generated_config_8.json
-# ./config_maker/config_maker.py -d 60 -l 20000 -c 5 -o ./config/generated_config_9.json
-# ./config_maker/config_maker.py -d 65 -l 20000 -c 5 -o ./config/generated_config_10.json
-# ./config_maker/config_maker.py -d 70 -l 20000 -c 5 -o ./config/generated_config_11.json
-./config_maker/config_maker.py -d 75 -l 20000 -c 5 -o ./config/generated_config_12.json
-./config_maker/config_maker.py -d 80 -l 20000 -c 5 -o ./config/generated_config_13.json
-./config_maker/config_maker.py -d 85 -l 20000 -c 5 -o ./config/generated_config_14.json
-# ./config_maker/config_maker.py -d 90 -l 20000 -c 5 -o ./config/generated_config_15.json
-./config_maker/config_maker.py -d 95 -l 20000 -c 5 -o ./config/generated_config_16.json
-./config_maker/config_maker.py -d 100 -l 20000 -c 5 -o ./config/generated_config_17.json
-# ./config_maker/config_maker.py -d 105 -l 20000 -c 5 -o ./config/generated_config_18.json
-./config_maker/config_maker.py -d 110 -l 20000 -c 5 -o ./config/generated_config_19.json
-./config_maker/config_maker.py -d 115 -l 20000 -c 5 -o ./config/generated_config_20.json
+./config_maker/config_maker.py -d 20 -l 20000 -c 10 -o ./config/generated_config_1.json
+./config_maker/config_maker.py -d 25 -l 20000 -c 10 -o ./config/generated_config_2.json
+./config_maker/config_maker.py -d 30 -l 20000 -c 10 -o ./config/generated_config_3.json
+./config_maker/config_maker.py -d 35 -l 20000 -c 10 -o ./config/generated_config_4.json
+./config_maker/config_maker.py -d 40 -l 20000 -c 10 -o ./config/generated_config_5.json
+./config_maker/config_maker.py -d 45 -l 20000 -c 10 -o ./config/generated_config_6.json
+./config_maker/config_maker.py -d 50 -l 20000 -c 10 -o ./config/generated_config_7.json
+./config_maker/config_maker.py -d 55 -l 20000 -c 10 -o ./config/generated_config_8.json
+./config_maker/config_maker.py -d 60 -l 20000 -c 10 -o ./config/generated_config_9.json
+./config_maker/config_maker.py -d 65 -l 20000 -c 10 -o ./config/generated_config_10.json
+./config_maker/config_maker.py -d 70 -l 20000 -c 10 -o ./config/generated_config_11.json
+./config_maker/config_maker.py -d 75 -l 20000 -c 10 -o ./config/generated_config_12.json
+./config_maker/config_maker.py -d 80 -l 20000 -c 10 -o ./config/generated_config_13.json
+./config_maker/config_maker.py -d 85 -l 20000 -c 10 -o ./config/generated_config_14.json
+./config_maker/config_maker.py -d 90 -l 20000 -c 10 -o ./config/generated_config_15.json
+./config_maker/config_maker.py -d 95 -l 20000 -c 10 -o ./config/generated_config_16.json
+./config_maker/config_maker.py -d 100 -l 20000 -c 10 -o ./config/generated_config_17.json
+./config_maker/config_maker.py -d 105 -l 20000 -c 10 -o ./config/generated_config_18.json
+./config_maker/config_maker.py -d 110 -l 20000 -c 10 -o ./config/generated_config_19.json
+./config_maker/config_maker.py -d 115 -l 20000 -c 10 -o ./config/generated_config_20.json
diff --git a/split/base.py b/split/base.py
index 6a78f4fefdacbd11753a44497d4c161452a9bdbf..b39d797c584aa2b04777cdf2c6d5e5cc1bbab092 100644
--- a/split/base.py
+++ b/split/base.py
@@ -35,6 +35,7 @@ class BaseSplitter:
w_df = pd.read_csv(f"{self.data_path}/dataset_train.csv", index_col="Timestamp")
df = pd.read_csv(f"{self.data_path}/dataset.csv", index_col="Timestamp")
train_df = pd.read_csv(f"{self.data_path}/dataset_train.csv", index_col="Timestamp")
+ labels_df = pd.read_csv(f"{self.data_path}/dataset_variables_labels.csv")
# Remove the categorial variables from the correlation computation
cat_columns = df.select_dtypes(include="int64").columns
@@ -76,6 +77,8 @@ class BaseSplitter:
.to_csv(f"{self.data_path}/dataset_{i}_auto_split.csv", index_label="Timestamp")
train_df.drop(columns=drop)\
.to_csv(f"{self.data_path}/dataset_{i}_auto_split_train.csv", index_label="Timestamp")
+ labels = np.bitwise_or.reduce(labels_df.drop(columns=drop).to_numpy(), axis=1, dtype=np.int32)
+ pd.DataFrame(labels).to_csv(f"{self.data_path}/dataset_{i}_auto_split_labels.csv", index=False)
def _compute_correlations(self, data):
"""
diff --git a/trainers/base.py b/trainers/base.py
index 4b01217be775b1bfc498d4abdfd7e825403e2d18..cdaec80df43d9cdb53717f060e1f74018b2788dc 100644
--- a/trainers/base.py
+++ b/trainers/base.py
@@ -3,14 +3,34 @@ This module defines necessary symbols to train algorithms to given data
"""
import json
import os
-import pandas as pd
-import seaborn as sns
import subprocess
-
from time import time
+import numpy as np
+import pandas as pd
+from bayes_opt import BayesianOptimization
+from sklearn.metrics import roc_auc_score#, f1_score
+
from .correlations import CORRELATION_CLASSES
+
+def __vote_for_score(scores, length):
+ """
+ Compute the score for each point of the dataset instead of a per window basis.
+ """
+ scores = np.nan_to_num(scores)
+ results = np.zeros(length)
+ w_size = length - len(scores) + 1
+
+ for idx in range(length):
+ start = idx - w_size if idx - w_size >= 0 else 0
+ end = idx + w_size if idx + w_size < length else length
+
+ results[idx] = np.mean(scores[start:end])
+
+ return results
+
+
class BaseTrainer():
"""
This class defines the basic behavior of a trainer. Each trainer will be in charge of the manupulation
@@ -19,23 +39,42 @@ class BaseTrainer():
def __init__(self, data_path: str, algorithm: str, train: bool, **kwargs) -> "BaseTrainer":
self.data_path = data_path
- self.params = kwargs
+ self.params = kwargs["const"]
+ self.optim_params = kwargs["optimize"]
self.log_path = f"{'/'.join(data_path.split('/')[:-1])}/{self.__class__.__name__}_logs"
self.algorithm = algorithm
self.train = train
+ self.pwd = ""
- def run(self):
+ def start(self):
"""
- This method should be used to run an algorithm.
+ This method orchestrates the optimization, training and computing of the results for the
+ current algorithm
"""
- if self.train:
- self._run(task="train")
+ def __exec(cmd) -> str:
+ """
+ Execute a shell command and process its output as expected.
+ """
+ return subprocess.check_output(cmd, shell=True).decode("utf-8").split()
+
+ self.pwd = __exec('pwd')[0]
+ path = f"{self.pwd}/{self.data_path}"
+
+ os.makedirs(f"{path}/results_{self.algorithm}", exist_ok=True)
+ optimize_time = self.optimize("dataset", path)
- self._run()
+ files = __exec(f"find -L {self.data_path} -regex '^.*dataset[-_0-9]*\(_auto_split\)*.csv'")
- def _run(self, task="execute"):
+ for file in files:
+ file_name = file.split("/")[-1][:-4]
+ train_time, run_time = self.run(file_name, path)
+
+ def optimize(self, file, path):
"""
- This function will construct the proper command to run the algorithm
+ This function optimizes the hyperparameters of the algorithm we want to use.
+
+ It uses Bayesian Optimization to search within the space defined in the file
+ `algorithm_params.json` present at the root of the project.
"""
def __exec(cmd) -> str:
"""
@@ -43,28 +82,84 @@ class BaseTrainer():
"""
return subprocess.check_output(cmd, shell=True).decode("utf-8").split()
- pwd = __exec('pwd')[0]
- path = f"{pwd}/{self.data_path}"
+ def _compute_score():
+ """
+ This function computes the score used in the objective function.
+ """
+ results_path = f"{path}/results_{self.algorithm}"
+
+ labels = pd.read_csv(f"{path}/{file}_labels.csv")
+ y_pred = np.loadtxt(f"{results_path}/anomaly_scores_{file}_{self.algorithm}.ts")
+ y_pred = np.nan_to_num(y_pred)
- for file in __exec(f"find -L {self.data_path} -regex '^.*dataset[-_0-9]*\(_auto_split\)*.csv'"):
- file_name = file.split("/")[-1][:-4]
- args = {
- "executionType": task,
- "dataInput": f"{path}/{file_name}.csv" if task == "execute" else f"{path}/{file_name}_train.csv",
- "dataOutput": f"{path}/results_{self.algorithm}/anomaly_scores_{file_name}_{self.algorithm}.ts",
- "modelInput": f"{path}/results_{self.algorithm}/model_{file_name}.pkl",
- "modelOutput": f"{path}/results_{self.algorithm}/model_{file_name}.pkl",
- "customParameters": self.params
- }
-
- os.makedirs(f"{path}/results_{self.algorithm}", exist_ok=True)
-
- start_time = time()
- os.system(
- f"python3 {pwd}/algorithms/{self.algorithm}/algorithm.py '{json.dumps(args)}' 1>/dev/null 2>/dev/null"
- )
- duration = time() - start_time
-
- if task == "execute":
- with open(f"{path}/time.csv", "a", encoding="utf-8") as f:
- f.write(f"{self.algorithm},{file_name},{duration}\n")
+ if len(labels) != len(y_pred):
+ y_pred = __vote_for_score(y_pred, len(labels))
+
+ return roc_auc_score(labels, y_pred)
+
+ def _obj_function(**params):
+ """
+ This is the objective function used by the Bayesian optimization process.
+ """
+ # Fix parameters to use for this run
+ self.params.update({k: int(v) for k, v in params.items()})
+
+ # Run algorithm
+ self.run(file, path)
+
+ # Remove save of model
+ if self.train:
+ os.remove(f"{path}/results_{self.algorithm}/model_{file}.pkl")
+
+ # Compute and return ROC score
+ return _compute_score()
+
+ start_time = time()
+ optimizer = BayesianOptimization(
+ f=_obj_function,
+ pbounds=self.optim_params
+ )
+ optimizer.maximize(init_points=7, n_iter=3)
+ self.params.update({k: int(v) for k, v in optimizer.max["params"].items()})
+
+ return time() - start_time
+
+ def run(self, file, path):
+ """
+ This method trains the algorithm if needed then runs it.
+ """
+ args_train = self._get_args(file, path, task="train")
+ args = self._get_args(file, path, task="execute")
+
+ training_time = 0.0
+ if self.train:
+ training_time = self._run(args_train)
+ run_time = self._run(args)
+
+ return training_time, run_time
+
+ def _get_args(self, file_name, path, task="execute"):
+ """
+ This method creates the dict containing all the arguments needed to run the algorithm
+ """
+ return {
+ "executionType": task,
+ "dataInput": f"{path}/{file_name}.csv" if task == "execute" else f"{path}/{file_name}_train.csv",
+ "dataOutput": f"{path}/results_{self.algorithm}/anomaly_scores_{file_name}_{self.algorithm}.ts",
+ "modelInput": f"{path}/results_{self.algorithm}/model_{file_name}.pkl",
+ "modelOutput": f"{path}/results_{self.algorithm}/model_{file_name}.pkl",
+ "customParameters": self.params
+ }
+
+ def _run(self, model_args):
+ """
+ This function will construct the proper command to run the algorithm
+ """
+ start_time = time()
+ cmd = f"python3 {self.pwd}/algorithms/{self.algorithm}/algorithm.py '{json.dumps(model_args)}'"
+ cmd += f" 1>{self.log_path} 2>{self.log_path}"
+
+ os.system(cmd)
+ duration = time() - start_time
+
+ return duration