From 05b6db09b8a81d84e885136b3ae7dba39f41732d Mon Sep 17 00:00:00 2001
From: Pierre LOTTE <pierre.lotte@irit.fr>
Date: Mon, 3 Feb 2025 07:39:57 +0000
Subject: [PATCH] Add soft/fuzzy clustering splitting

---
 paradise/main.py           |    6 +-
 paradise/split/__init__.py |    1 +
 paradise/split/base.py     |   39 +-
 paradise/split/soft.py     |   89 +++
 poetry.lock                | 1062 +++++++++++++++++++-----------------
 pyproject.toml             |    1 +
 6 files changed, 676 insertions(+), 522 deletions(-)
 create mode 100644 paradise/split/soft.py

diff --git a/paradise/main.py b/paradise/main.py
index cfd8077..c6c08e9 100755
--- a/paradise/main.py
+++ b/paradise/main.py
@@ -13,7 +13,7 @@ import numpy as np
 import pandas as pd
 
 from generator import DatasetGenerator
-from split import BaseSplitter
+from split import SoftSplitter
 from trainers import BaseTrainer
 from results import ResultExtractor, ROCResults
 
@@ -160,8 +160,8 @@ if __name__ == "__main__":
     #                                                 Split data
     # =================================================================================================================
 
-        if args.split and args.task in ["train", "all"]:
-            splitter = BaseSplitter(f"{INPUT_DIR}/{config_name}")
+        if args.split and args.task in ["generate", "train", "all"]:
+            splitter = SoftSplitter(f"{INPUT_DIR}/{config_name}")
             splitter.split_data(method=args.method, merge=args.merge)
 
     # =================================================================================================================
diff --git a/paradise/split/__init__.py b/paradise/split/__init__.py
index 8061bd0..7bd362f 100644
--- a/paradise/split/__init__.py
+++ b/paradise/split/__init__.py
@@ -2,3 +2,4 @@
 This module provides the necessary code to split data.
 """
 from .base import BaseSplitter
+from .soft import SoftSplitter
diff --git a/paradise/split/base.py b/paradise/split/base.py
index f22698a..326485d 100644
--- a/paradise/split/base.py
+++ b/paradise/split/base.py
@@ -31,31 +31,33 @@ class BaseSplitter:
         self.output_path = f"{path}/splitting"
         os.makedirs(f"{path}/splitting", exist_ok=True)
 
+        self.w_df = pd.read_csv(f"{self.data_path}/dataset_train.csv", index_col="Timestamp")
+        self.df = pd.read_csv(f"{self.data_path}/dataset.csv", index_col="Timestamp")
+        self.train_df = pd.read_csv(f"{self.data_path}/dataset_train.csv", index_col="Timestamp")
+        self.labels_df = pd.read_csv(f"{self.data_path}/dataset_variables_labels.csv")
+
+        # Remove the categorial variables from the correlation computation
+        self.w_df.drop(columns=self.df.select_dtypes(include="int64").columns, inplace=True)
+        self.w_df.drop(columns=["Timestamp", "is_anomaly"], errors="ignore", inplace=True)
+        self.w_df.drop(columns=self.w_df.columns[self.w_df.nunique() <= 1], inplace=True)
+
     def split_data(self, method="HDBSCAN", merge=False):
         """
         This method will be in charge of splitting data into subsystems.
         """
-        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
-        w_df.drop(columns=df.select_dtypes(include="int64").columns, inplace=True)
-        w_df.drop(columns=["Timestamp", "is_anomaly"], errors="ignore", inplace=True)
-        w_df.drop(columns=w_df.columns[w_df.nunique() <= 1], inplace=True)
+        x = self._compute_correlations(merge=merge)
+        self._split_data(x, method=method)
 
+    def _split_data(self, x, method="HDBSCAN"):
         # Compute all the correlations for the dataset and find the coefficient that produces the best
         # cluters from its coefficient
         max_silhouette = 0
         best_clusters = None
-        x = self._compute_correlations(w_df, merge=merge)
-
         if "HDBSCAN" == method:
             model = HDBSCAN(min_cluster_size=2, allow_single_cluster=True, n_jobs=-1)
             best_clusters = model.fit_predict(x)
         elif "kmeans" == method:
-            for i in range(1, len(w_df.columns)):
+            for i in range(1, len(self.w_df.columns)):
                 model = KMeans(n_clusters=i, n_init="auto")
 
                 # Compute clusters and their silhouette score
@@ -75,18 +77,19 @@ class BaseSplitter:
 
         # Split the dataset
         for i in range(-1 if -1 in best_clusters else 0, max(best_clusters)+1):
-            drop = [col for idx, col in enumerate(w_df.columns) if best_clusters[idx] != i]
-            df.drop(columns=drop)\
+            drop = [col for idx, col in enumerate(self.w_df.columns) if best_clusters[idx] != i]
+            self.df.drop(columns=drop)\
                 .to_csv(f"{self.data_path}/dataset_{i}_auto_split.csv", index_label="Timestamp")
-            train_df.drop(columns=drop)\
+            self.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)
+            labels = np.bitwise_or.reduce(self.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, merge=False):
+    def _compute_correlations(self, merge=False):
         """
         Compute the vector of correlation coefficients for each of the variable of the dataset.
         """
+        sns.set(font_scale=3.0)
         with open(f"{self.output_path}/split_time.csv", "a", encoding="utf-8") as f:
             f.write("Algorithm,duration\n")
             x = []
@@ -94,7 +97,7 @@ class BaseSplitter:
                 coeff_name = coeff.__name__[:-11]
 
                 start_time = time()
-                correlation_matrix = coeff().compute(data)
+                correlation_matrix = coeff().compute(self.w_df)
                 duration = time() - start_time
                 f.write(f"{coeff_name},{duration}\n")
 
diff --git a/paradise/split/soft.py b/paradise/split/soft.py
new file mode 100644
index 0000000..7ef30db
--- /dev/null
+++ b/paradise/split/soft.py
@@ -0,0 +1,89 @@
+"""
+This module contains the basic splitter algorithm based on the correlation coefficients computing and
+soft clustering.
+"""
+import json
+import os
+
+from time import time
+from warnings import  simplefilter
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+
+from scipy.spatial.distance import euclidean
+from skfuzzy.cluster import cmeans
+from sklearn.exceptions import ConvergenceWarning
+from sklearn.metrics import silhouette_score
+
+from .correlations import CORRELATION_CLASSES
+from .base import BaseSplitter
+
+simplefilter("ignore", category=ConvergenceWarning)
+
+
+class SoftSplitter(BaseSplitter):
+    """
+    Class in charge of doing basic splitting using correlation coefficients
+    """
+    def _fuzzy_silhouette(self, fuzzy_matrix, points, distances_matrix):
+        s = np.zeros(len(points))
+        ranked_scores = []
+
+        for i in range(len(points)):
+            la = [euclidean(points[i], points[x]) for x in range(len(points)) if x != i and np.argmax(fuzzy_matrix[:, i]) == np.argmax(fuzzy_matrix[:, x])]
+            if la:
+                a = np.mean(la)
+                b = np.sort(distances_matrix[:, i])[1]
+                s[i] = (b-a)/max(a,b)
+
+            ranked_scores.append(-np.sort(-fuzzy_matrix[:, i]))
+
+        alpha = 1
+        non_summed = np.array([((ranked_scores[i][0] - ranked_scores[i][1])**alpha) for i in range(len(points))])
+        denom = sum(non_summed)
+        num = sum(non_summed * s)
+
+        return num/denom
+
+
+    def _split_data(self, x, method=""):
+        # Compute all the correlations for the dataset and find the coefficient that produces the best
+        # cluters from its coefficient
+        best_clusters = None
+        best_fs = 0
+        all_fs = []
+        k = 1
+
+        for i in range(2, len(self.w_df.columns)):
+            # Compute fuzzy clusters. The third argument has been taken from the tutorial of skfuzzy
+            # and might not be the best choice. 
+            _, scores, _, d, _, _, _ = cmeans(x.T, i, 2, error=0.005, maxiter=1000)
+            score = self._fuzzy_silhouette(scores, x, d)
+            all_fs.append(score)
+
+            # Check if the proposed clustering is better than already proposed solutions
+            if score > best_fs:
+                best_fs = score
+                best_clusters = scores.T
+                k = i
+
+
+        # Save choosen clustering
+        with open(f"{self.output_path}/dataset_split_clusters.txt", "w", encoding="utf-8") as f:
+            f.write(f"{best_clusters.tolist()}\n\n")
+            f.write(f"All silhouette: {all_fs}\n\n")
+
+            # Split the dataset
+            for i in range(k):
+                drop = [col for idx, col in enumerate(self.w_df.columns) if np.argmax(best_clusters[idx]) != i and best_clusters[idx, i] < best_clusters[idx, np.argmax(best_clusters[idx])] /2]
+                f.write(f"Cluster {i} -> {[col for col in self.w_df.columns if col not in drop]}\n")
+                self.df.drop(columns=drop)\
+                    .to_csv(f"{self.data_path}/dataset_{i}_auto_split.csv", index_label="Timestamp")
+                self.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(self.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)
+        return x
diff --git a/poetry.lock b/poetry.lock
index 550a404..dbc27c2 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -2,119 +2,119 @@
 
 [[package]]
 name = "aiohappyeyeballs"
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+version = "2.4.4"
 description = "Happy Eyeballs for asyncio"
 optional = false
 python-versions = ">=3.8"
 files = [
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-    {file = "aiohappyeyeballs-2.4.3.tar.gz", hash = "sha256:75cf88a15106a5002a8eb1dab212525c00d1f4c0fa96e551c9fbe6f09a621586"},
+    {file = "aiohappyeyeballs-2.4.4-py3-none-any.whl", hash = "sha256:a980909d50efcd44795c4afeca523296716d50cd756ddca6af8c65b996e27de8"},
+    {file = "aiohappyeyeballs-2.4.4.tar.gz", hash = "sha256:5fdd7d87889c63183afc18ce9271f9b0a7d32c2303e394468dd45d514a757745"},
 ]
 
 [[package]]
 name = "aiohttp"
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 description = "Async http client/server framework (asyncio)"
 optional = false
 python-versions = ">=3.8"
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 aiosignal = ">=1.1.2"
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 attrs = ">=17.3.0"
 frozenlist = ">=1.1.1"
 multidict = ">=4.5,<7.0"
@@ -139,30 +139,30 @@ frozenlist = ">=1.1.0"
 
 [[package]]
 name = "async-timeout"
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 description = "Timeout context manager for asyncio programs"
 optional = false
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 tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
@@ -186,127 +186,114 @@ scipy = ">=1.0.0"
 
 [[package]]
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+version = "2024.12.14"
 description = "Python package for providing Mozilla's CA Bundle."
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 ]
 
 [[package]]
@@ -506,59 +493,61 @@ typing = ["typing-extensions (>=4.12.2)"]
 
 [[package]]
 name = "fonttools"
-version = "4.54.1"
+version = "4.55.3"
 description = "Tools to manipulate font files"
 optional = false
 python-versions = ">=3.8"
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+version = "24.2"
 description = "Core utilities for Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "packaging-24.1-py3-none-any.whl", hash = "sha256:5b8f2217dbdbd2f7f384c41c628544e6d52f2d0f53c6d0c3ea61aa5d1d7ff124"},
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 ]
 
 [[package]]
@@ -1719,41 +1723,42 @@ files = [
 
 [[package]]
 name = "psutil"
-version = "6.0.0"
+version = "6.1.1"
 description = "Cross-platform lib for process and system monitoring in Python."
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
 files = [
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+    {file = "psutil-6.1.1-cp36-abi3-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:97f7cb9921fbec4904f522d972f0c0e1f4fabbdd4e0287813b21215074a0f160"},
+    {file = "psutil-6.1.1-cp36-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:33431e84fee02bc84ea36d9e2c4a6d395d479c9dd9bba2376c1f6ee8f3a4e0b3"},
+    {file = "psutil-6.1.1-cp36-cp36m-win32.whl", hash = "sha256:384636b1a64b47814437d1173be1427a7c83681b17a450bfc309a1953e329603"},
+    {file = "psutil-6.1.1-cp36-cp36m-win_amd64.whl", hash = "sha256:8be07491f6ebe1a693f17d4f11e69d0dc1811fa082736500f649f79df7735303"},
+    {file = "psutil-6.1.1-cp37-abi3-win32.whl", hash = "sha256:eaa912e0b11848c4d9279a93d7e2783df352b082f40111e078388701fd479e53"},
+    {file = "psutil-6.1.1-cp37-abi3-win_amd64.whl", hash = "sha256:f35cfccb065fff93529d2afb4a2e89e363fe63ca1e4a5da22b603a85833c2649"},
+    {file = "psutil-6.1.1.tar.gz", hash = "sha256:cf8496728c18f2d0b45198f06895be52f36611711746b7f30c464b422b50e2f5"},
 ]
 
 [package.extras]
-test = ["enum34", "ipaddress", "mock", "pywin32", "wmi"]
+dev = ["abi3audit", "black", "check-manifest", "coverage", "packaging", "pylint", "pyperf", "pypinfo", "pytest-cov", "requests", "rstcheck", "ruff", "sphinx", "sphinx_rtd_theme", "toml-sort", "twine", "virtualenv", "vulture", "wheel"]
+test = ["pytest", "pytest-xdist", "setuptools"]
 
 [[package]]
 name = "pyod"
-version = "2.0.2"
+version = "2.0.3"
 description = "A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)"
 optional = false
 python-versions = "*"
 files = [
-    {file = "pyod-2.0.2.tar.gz", hash = "sha256:e05b7a457ac9bd8b0cc41754fdafe3aefcc1f61270f33d38f43112676d9db2b8"},
+    {file = "pyod-2.0.3.tar.gz", hash = "sha256:68bb7333061ecb04113e39180efc75918a077f86b349a5e927dede0d5bb4473a"},
 ]
 
 [package.dependencies]
@@ -1824,6 +1829,17 @@ urllib3 = ">=1.21.1,<3"
 socks = ["PySocks (>=1.5.6,!=1.5.7)"]
 use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
+[[package]]
+name = "scikit-fuzzy"
+version = "0.5.0"
+description = "Fuzzy logic toolkit for SciPy"
+optional = false
+python-versions = "*"
+files = [
+    {file = "scikit_fuzzy-0.5.0-py2.py3-none-any.whl", hash = "sha256:e456c365146bc5c58d9f593f4127ac783112e2daf89f87b12c6ef3c38e4087dd"},
+    {file = "scikit_fuzzy-0.5.0.tar.gz", hash = "sha256:2ee5340523aa7635ca568b9d8029c7738d05f9ea550fc89d4054b4cb50f34e7f"},
+]
+
 [[package]]
 name = "scikit-learn"
 version = "1.3.2"
@@ -1930,26 +1946,60 @@ dev = ["flake8", "flit", "mypy", "pandas-stubs", "pre-commit", "pytest", "pytest
 docs = ["ipykernel", "nbconvert", "numpydoc", "pydata_sphinx_theme (==0.10.0rc2)", "pyyaml", "sphinx (<6.0.0)", "sphinx-copybutton", "sphinx-design", "sphinx-issues"]
 stats = ["scipy (>=1.7)", "statsmodels (>=0.12)"]
 
+[[package]]
+name = "setuptools"
+version = "75.8.0"
+description = "Easily download, build, install, upgrade, and uninstall Python packages"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "setuptools-75.8.0-py3-none-any.whl", hash = "sha256:e3982f444617239225d675215d51f6ba05f845d4eec313da4418fdbb56fb27e3"},
+    {file = "setuptools-75.8.0.tar.gz", hash = "sha256:c5afc8f407c626b8313a86e10311dd3f661c6cd9c09d4bf8c15c0e11f9f2b0e6"},
+]
+
+[package.extras]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)", "ruff (>=0.8.0)"]
+core = ["importlib_metadata (>=6)", "jaraco.collections", "jaraco.functools (>=4)", "jaraco.text (>=3.7)", "more_itertools", "more_itertools (>=8.8)", "packaging", "packaging (>=24.2)", "platformdirs (>=4.2.2)", "tomli (>=2.0.1)", "wheel (>=0.43.0)"]
+cover = ["pytest-cov"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
+enabler = ["pytest-enabler (>=2.2)"]
+test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.7.2)", "jaraco.test (>=5.5)", "packaging (>=24.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-home (>=0.5)", "pytest-perf", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel (>=0.44.0)"]
+type = ["importlib_metadata (>=7.0.2)", "jaraco.develop (>=7.21)", "mypy (==1.14.*)", "pytest-mypy"]
+
 [[package]]
 name = "six"
-version = "1.16.0"
+version = "1.17.0"
 description = "Python 2 and 3 compatibility utilities"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
 files = [
-    {file = "six-1.16.0-py2.py3-none-any.whl", hash = "sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254"},
-    {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
+    {file = "six-1.17.0-py2.py3-none-any.whl", hash = "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274"},
+    {file = "six-1.17.0.tar.gz", hash = "sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81"},
 ]
 
 [[package]]
 name = "sympy"
-version = "1.13.3"
+version = "1.12.1"
 description = "Computer algebra system (CAS) in Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "sympy-1.13.3-py3-none-any.whl", hash = "sha256:54612cf55a62755ee71824ce692986f23c88ffa77207b30c1368eda4a7060f73"},
-    {file = "sympy-1.13.3.tar.gz", hash = "sha256:b27fd2c6530e0ab39e275fc9b683895367e51d5da91baa8d3d64db2565fec4d9"},
+    {file = "sympy-1.12.1-py3-none-any.whl", hash = "sha256:9b2cbc7f1a640289430e13d2a56f02f867a1da0190f2f99d8968c2f74da0e515"},
+    {file = "sympy-1.12.1.tar.gz", hash = "sha256:2877b03f998cd8c08f07cd0de5b767119cd3ef40d09f41c30d722f6686b0fb88"},
+]
+
+[package.dependencies]
+mpmath = ">=1.1.0,<1.4.0"
+
+[[package]]
+name = "sympy"
+version = "1.13.1"
+description = "Computer algebra system (CAS) in Python"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "sympy-1.13.1-py3-none-any.whl", hash = "sha256:db36cdc64bf61b9b24578b6f7bab1ecdd2452cf008f34faa33776680c26d66f8"},
+    {file = "sympy-1.13.1.tar.gz", hash = "sha256:9cebf7e04ff162015ce31c9c6c9144daa34a93bd082f54fd8f12deca4f47515f"},
 ]
 
 [package.dependencies]
@@ -1971,21 +2021,20 @@ files = [
 
 [[package]]
 name = "torch"
-version = "2.4.1+cu118"
+version = "2.5.1+cu118"
 description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
 optional = false
 python-versions = ">=3.8.0"
 files = [
-    {file = "torch-2.4.1+cu118-cp310-cp310-linux_x86_64.whl", hash = "sha256:740bae6eb10c6b41cb86c4f9e84da0b4533b5595aed4f06694d95d5e32b4076c"},
-    {file = "torch-2.4.1+cu118-cp310-cp310-win_amd64.whl", hash = "sha256:08634e2d32e753ea4a086d42cc145f9251c767b0a7619fd9a6ed5c035dee7b63"},
-    {file = "torch-2.4.1+cu118-cp311-cp311-linux_x86_64.whl", hash = "sha256:c7fbf1e214af65ccc0e54d265140b2d09486f977b966fcde218b25068bd54551"},
-    {file = "torch-2.4.1+cu118-cp311-cp311-win_amd64.whl", hash = "sha256:1db7ac11687fc6ec279c5504302840a057a4f6bbadf81b7c4588fa53743cf493"},
-    {file = "torch-2.4.1+cu118-cp312-cp312-linux_x86_64.whl", hash = "sha256:5c08fa312d259dd19dbd5058d3e82992b27f092347fccc7c0f417f9e0ac16956"},
-    {file = "torch-2.4.1+cu118-cp312-cp312-win_amd64.whl", hash = "sha256:1ea70f54853744dd097992ef88d86d4ab793df0df4c41d1e37313ef4b80ef93f"},
-    {file = "torch-2.4.1+cu118-cp38-cp38-linux_x86_64.whl", hash = "sha256:a8ba825b2c00274db8924b8c3e860a8d42947d2f04fcdf5b220ad7a650a83dea"},
-    {file = "torch-2.4.1+cu118-cp38-cp38-win_amd64.whl", hash = "sha256:1520c0a9aa6d0187c9617b07409c9493d0bf20b28f26cffa3458995f53f58c48"},
-    {file = "torch-2.4.1+cu118-cp39-cp39-linux_x86_64.whl", hash = "sha256:82309da0cce45cf61eb48b0567c7d080992d8ba98264da128ee1d858fac5dd75"},
-    {file = "torch-2.4.1+cu118-cp39-cp39-win_amd64.whl", hash = "sha256:0eb4393f51f110e5d9f20e3e8079b4bd1fc8fa781c4ac521f709065f498be676"},
+    {file = "torch-2.5.1+cu118-cp310-cp310-linux_x86_64.whl", hash = "sha256:eee38239682a01ab02d3790fe194753f7070f07e1741e67fb5c3c3059d3b97b2"},
+    {file = "torch-2.5.1+cu118-cp310-cp310-win_amd64.whl", hash = "sha256:0d8b4ebe8429e8bcaf7ae833531b2949169db788d5ed5accaabb481175c7443c"},
+    {file = "torch-2.5.1+cu118-cp311-cp311-linux_x86_64.whl", hash = "sha256:c3a3fa09578e1acb76236dc3056ac67ac2f991d9214ab54ec440c4a1427cf016"},
+    {file = "torch-2.5.1+cu118-cp311-cp311-win_amd64.whl", hash = "sha256:7e09af36c3d0bf8e6554cacae794f9fcd348d096e21f8be1dd42ed3bc48b4e2f"},
+    {file = "torch-2.5.1+cu118-cp312-cp312-linux_x86_64.whl", hash = "sha256:f4c3ae93c72e8b725b510c3bc5b9dd5437f6740a66c3cc3bb1a19f177c3baef4"},
+    {file = "torch-2.5.1+cu118-cp312-cp312-win_amd64.whl", hash = "sha256:3edf97d9b0a17e3f764a511ed084e866c086af76c9d489cc51d5edb77ca7c936"},
+    {file = "torch-2.5.1+cu118-cp313-cp313-linux_x86_64.whl", hash = "sha256:60c1d1b522ea534a17e1ac60a0dc542ed578c45c84d63dc1528febbd1a0f5953"},
+    {file = "torch-2.5.1+cu118-cp39-cp39-linux_x86_64.whl", hash = "sha256:462ab8592e8a2380d9a1a7dc167c076d4df82cd390656a99c5b3e9fe5a216c63"},
+    {file = "torch-2.5.1+cu118-cp39-cp39-win_amd64.whl", hash = "sha256:1e7e4368e75f6ba68369201228ecbbc141dcc66358af11808138dff0abaa6f6e"},
 ]
 
 [package.dependencies]
@@ -2002,15 +2051,19 @@ nvidia-cufft-cu11 = {version = "10.9.0.58", markers = "platform_system == \"Linu
 nvidia-curand-cu11 = {version = "10.3.0.86", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 nvidia-cusolver-cu11 = {version = "11.4.1.48", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 nvidia-cusparse-cu11 = {version = "11.7.5.86", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-nccl-cu11 = {version = "2.20.5", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-nccl-cu11 = {version = "2.21.5", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 nvidia-nvtx-cu11 = {version = "11.8.86", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-sympy = "*"
-triton = {version = "3.0.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.13\""}
+setuptools = {version = "*", markers = "python_version >= \"3.12\""}
+sympy = [
+    {version = "1.12.1", markers = "python_version == \"3.8\""},
+    {version = "1.13.1", markers = "python_version >= \"3.9\""},
+]
+triton = {version = "3.1.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.13\""}
 typing-extensions = ">=4.8.0"
 
 [package.extras]
 opt-einsum = ["opt-einsum (>=3.3)"]
-optree = ["optree (>=0.11.0)"]
+optree = ["optree (>=0.12.0)"]
 
 [package.source]
 type = "legacy"
@@ -2162,36 +2215,37 @@ reference = "pyg"
 
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 name = "tqdm"
-version = "4.66.5"
+version = "4.67.1"
 description = "Fast, Extensible Progress Meter"
 optional = false
 python-versions = ">=3.7"
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 [package.dependencies]
 colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
 [package.extras]
-dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"]
+dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"]
+discord = ["requests"]
 notebook = ["ipywidgets (>=6)"]
 slack = ["slack-sdk"]
 telegram = ["requests"]
 
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 name = "triton"
-version = "3.0.0"
+version = "3.1.0"
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 python-versions = "*"
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 [package.dependencies]
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 ]
 
 [package.dependencies]
@@ -2384,4 +2444,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.8"
-content-hash = "e370fa51a52bcacf1ca43e47fab2a9f9899c27db826070f7e26063662cb92b84"
+content-hash = "76e302bd6a7d9675d4c6f02b01de62bb71812ef0fee935070084583730cfde24"
diff --git a/pyproject.toml b/pyproject.toml
index 0bd19b7..a6f4a01 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -24,6 +24,7 @@ torch-scatter = {version="^2.1.2", source="pyg"}
 torch-spline-conv = {version="^1.2.2", source="pyg"}
 torch-cluster = {version="^1.6.3", source="pyg"}
 torch-sparse = {version="^0.6.18", source="pyg"}
+scikit-fuzzy = "^0.5.0"
 
 
 [[tool.poetry.source]]
-- 
GitLab