[SC-8300] Fix: Store embeddings in dictionary instead of DataFrame

pyproject.toml

@@ -10,7 +10,7 @@ description = "ValidMind Library"
 license = "Commercial License"
 name = "validmind"
 readme = "README.pypi.md"
-version = "2.8.2"
+version = "2.8.3"
 
 [tool.poetry.dependencies]
 aiohttp = {extras = ["speedups"], version = "*"}

validmind/__version__.py

@@ -1 +1 @@
-__version__ = "2.8.2"
+__version__ = "2.8.3"

validmind/tests/model_validation/embeddings/StabilityAnalysisRandomNoise.py

@@ -151,4 +151,4 @@ def perturb_data(data):
         mean_similarity_threshold,
     )
 
-    return result, RawData(original_perturbed_similarity=raw_data)
+    return *result, RawData(original_perturbed_similarity=raw_data)

validmind/tests/model_validation/embeddings/StabilityAnalysisSynonyms.py

@@ -107,4 +107,4 @@ def perturb_data(data):
         mean_similarity_threshold,
     )
 
-    return result, RawData(original_perturbed_similarity=raw_data)
+    return *result, RawData(original_perturbed_similarity=raw_data)

validmind/tests/model_validation/embeddings/StabilityAnalysisTranslation.py

@@ -134,4 +134,4 @@ def perturb_data(data):
         mean_similarity_threshold,
     )
 
-    return result, RawData(original_perturbed_similarity=raw_data)
+    return *result, RawData(original_perturbed_similarity=raw_data)

validmind/tests/model_validation/embeddings/utils.py

@@ -3,7 +3,6 @@
 # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial
 
 import numpy as np
-import pandas as pd
 import plotly.express as px
 from sklearn.metrics.pairwise import cosine_similarity
 
@@ -18,14 +17,12 @@ def create_stability_analysis_result(
         original_embeddings, perturbed_embeddings
     ).diagonal()
 
-    # create a raw dataframe of the original, perturbed and similarity
-    raw_data = pd.DataFrame(
-        {
-            "original": original_embeddings,
-            "perturbed": perturbed_embeddings,
-            "similarity": similarities,
-        }
-    )
+    # Store raw data in a dictionary
+    raw_data = {
+        "original_embeddings": original_embeddings,
+        "perturbed_embeddings": perturbed_embeddings,
+        "similarities": similarities,
+    }
 
     mean = np.mean(similarities)
     passed = mean > mean_similarity_threshold