Fixed message for missing embeddings

deepchecks/nlp/text_data.py

@@ -293,6 +293,12 @@ def n_samples(self) -> int:
     @property
     def embeddings(self) -> pd.DataFrame:
         """Return the metadata of for the dataset."""
+        if self._embeddings is None:
+            raise DeepchecksValueError(
+                'Functionality requires embeddings, but the the TextData object had none. To use this functionality, '
+                'use the set_embeddings method to set your own embeddings with a numpy.array or use '
+                'TextData.calculate_builtin_embeddings to add the default deepchecks embeddings.'
+            )
         return self._embeddings
 
     def calculate_builtin_embeddings(self, model: str = 'miniLM', file_path: str = 'embeddings.npy'):

deepchecks/nlp/utils/multivariate_embeddings_drift_utils.py

@@ -9,19 +9,24 @@
 # ----------------------------------------------------------------------------
 #
 """Module containing common EmbeddingsDrift Check (domain classifier drift) utils."""
+import warnings
 
 import numpy as np
 import pandas as pd
+from numba import NumbaDeprecationWarning
 from sklearn.decomposition import PCA
 from sklearn.ensemble import GradientBoostingClassifier
 from sklearn.metrics import roc_auc_score
 from sklearn.model_selection import train_test_split
-from umap import UMAP
 
 from deepchecks.core.check_utils.multivariate_drift_utils import auc_to_drift_score, build_drift_plot
 from deepchecks.nlp import TextData
 from deepchecks.nlp.utils.nlp_plot import two_datasets_scatter_plot
 
+with warnings.catch_warnings():
+    warnings.simplefilter(action='ignore', category=NumbaDeprecationWarning)
+    from umap import UMAP
+
 # Max number of samples to use for dimensionality reduction fit (to make calculation faster):
 SAMPLES_FOR_REDUCTION_FIT = 1000