From 43a3caa32e72401adea3f9916a0cf551684d5859 Mon Sep 17 00:00:00 2001
From: Pierre LOTTE <pierrelotte.dev@gmail.com>
Date: Wed, 9 Oct 2024 13:14:34 +0200
Subject: [PATCH] Add possibility to merge or not
---
paradise/main.py | 7 ++++++-
paradise/split/base.py | 19 +++++++++++--------
2 files changed, 17 insertions(+), 9 deletions(-)
diff --git a/paradise/main.py b/paradise/main.py
index ee2b7a8..90a007c 100755
--- a/paradise/main.py
+++ b/paradise/main.py
@@ -63,6 +63,11 @@ if __name__ == "__main__":
help="Use Docker containers directly to run the algorithm. Allow to run algorithms without cloning repo",
action="store_true"
)
+ parser.add_argument(
+ "--merge",
+ help="Whether the method should merge the correlation matrices or not.",
+ action="store_true"
+ )
# Load args
args = parser.parse_args()
@@ -157,7 +162,7 @@ if __name__ == "__main__":
if args.split and args.task in ["train", "all"]:
splitter = BaseSplitter(f"{INPUT_DIR}/{config_name}")
- splitter.split_data(method=args.method)
+ splitter.split_data(method=args.method, merge=args.merge)
# =================================================================================================================
# Train algorithm
diff --git a/paradise/split/base.py b/paradise/split/base.py
index 0f32298..9facc06 100644
--- a/paradise/split/base.py
+++ b/paradise/split/base.py
@@ -31,7 +31,7 @@ class BaseSplitter:
self.output_path = f"{path}/splitting"
os.makedirs(f"{path}/splitting", exist_ok=True)
- def split_data(self, method="HDBSCAN"):
+ def split_data(self, method="HDBSCAN", merge=False):
"""
This method will be in charge of splitting data into subsystems.
"""
@@ -49,7 +49,7 @@ class BaseSplitter:
# cluters from its coefficient
max_silhouette = 0
best_clusters = None
- x = self._compute_correlations(w_df)
+ x = self._compute_correlations(w_df, merge=merge)
if "HDBSCAN" == method:
model = HDBSCAN(min_cluster_size=2, allow_single_cluster=True, n_jobs=-1)
@@ -83,7 +83,7 @@ class BaseSplitter:
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):
+ def _compute_correlations(self, data, merge=False):
"""
Compute the vector of correlation coefficients for each of the variable of the dataset.
"""
@@ -106,11 +106,14 @@ class BaseSplitter:
x.append(np.abs(correlation_matrix))
- x = np.array(x)
- x = np.mean(x, axis=0)
+ if merge:
+ x = np.array(x)
+ x = np.mean(x, axis=0)
- sns.heatmap(x, annot=True, cmap="coolwarm")\
- .get_figure()\
- .savefig(f"{self.output_path}/dataset_final_matrix.png")
+ sns.heatmap(x, annot=True, cmap="coolwarm")\
+ .get_figure()\
+ .savefig(f"{self.output_path}/dataset_final_matrix.png")
+ else:
+ x = np.concatenate(x, axis=1)
return x
--
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