From c59367d1eeb9a1a8306815a1212cf63942c31d5f Mon Sep 17 00:00:00 2001
From: Manish Kumar <manish.kumar@basf.com>
Date: Wed, 26 Feb 2025 15:44:22 +0100
Subject: [PATCH 1/3] version incremented

---
 mambular/__version__.py |   2 +-
 poetry.lock             | 528 +++++++++++++++++++++-------------------
 pyproject.toml          |   2 +-
 3 files changed, 277 insertions(+), 255 deletions(-)

diff --git a/mambular/__version__.py b/mambular/__version__.py
index 629965c4..af5b177b 100644
--- a/mambular/__version__.py
+++ b/mambular/__version__.py
@@ -16,4 +16,4 @@
 #
 
 # The following line *must* be the last in the module, exactly as formatted:
-__version__ = "1.2.0"
+__version__ = "1.2.1"
diff --git a/poetry.lock b/poetry.lock
index a620abea..97dddc7e 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -2,13 +2,13 @@
 
 [[package]]
 name = "accelerate"
-version = "1.3.0"
+version = "1.4.0"
 description = "Accelerate"
 optional = false
 python-versions = ">=3.9.0"
 files = [
-    {file = "accelerate-1.3.0-py3-none-any.whl", hash = "sha256:5788d9e6a7a9f80fed665cf09681c4dddd9dc056bea656db4140ffc285ce423e"},
-    {file = "accelerate-1.3.0.tar.gz", hash = "sha256:518631c0adb80bd3d42fb29e7e2dc2256bcd7c786b0ba9119bbaa08611b36d9c"},
+    {file = "accelerate-1.4.0-py3-none-any.whl", hash = "sha256:f6e1e7dfaf9d799a20a1dc45efbf4b1546163eac133faa5acd0d89177c896e55"},
+    {file = "accelerate-1.4.0.tar.gz", hash = "sha256:37d413e1b64cb8681ccd2908ae211cf73e13e6e636a2f598a96eccaa538773a5"},
 ]
 
 [package.dependencies]
@@ -44,92 +44,92 @@ files = [
 
 [[package]]
 name = "aiohttp"
-version = "3.11.12"
+version = "3.11.13"
 description = "Async http client/server framework (asyncio)"
 optional = false
 python-versions = ">=3.9"
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 [package.dependencies]
@@ -616,13 +616,13 @@ tqdm = ["tqdm"]
 
 [[package]]
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 [package.dependencies]
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 [[package]]
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@@ -1671,53 +1687,53 @@ use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
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+    {file = "scipy-1.15.2-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6d0194c37037707b2afa7a2f2a924cf7bac3dc292d51b6a925e5fcb89bc5c776"},
+    {file = "scipy-1.15.2-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:bae43364d600fdc3ac327db99659dcb79e6e7ecd279a75fe1266669d9a652828"},
+    {file = "scipy-1.15.2-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:f031846580d9acccd0044efd1a90e6f4df3a6e12b4b6bd694a7bc03a89892b28"},
+    {file = "scipy-1.15.2-cp313-cp313t-win_amd64.whl", hash = "sha256:fe8a9eb875d430d81755472c5ba75e84acc980e4a8f6204d402849234d3017db"},
+    {file = "scipy-1.15.2.tar.gz", hash = "sha256:cd58a314d92838f7e6f755c8a2167ead4f27e1fd5c1251fd54289569ef3495ec"},
 ]
 
 [package.dependencies]
@@ -1870,13 +1892,13 @@ test = ["Cython", "array-api-strict (>=2.0,<2.1.1)", "asv", "gmpy2", "hypothesis
 
 [[package]]
 name = "setuptools"
-version = "75.8.0"
+version = "75.8.1"
 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"},
+    {file = "setuptools-75.8.1-py3-none-any.whl", hash = "sha256:3bc32c0b84c643299ca94e77f834730f126efd621de0cc1de64119e0e17dab1f"},
+    {file = "setuptools-75.8.1.tar.gz", hash = "sha256:65fb779a8f28895242923582eadca2337285f0891c2c9e160754df917c3d2530"},
 ]
 
 [package.extras]
diff --git a/pyproject.toml b/pyproject.toml
index b0e4c845..dae76db1 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "mambular"
-version = "1.2.0"
+version = "1.2.1"
 description = "A python package for tabular deep learning with mamba blocks."
 authors = ["Anton Thielmann", "Manish Kumar", "Christoph Weisser"]
 readme = "README.md"

From 4e54bfcbc88195e556be58e5b9d4dda6611a62f8 Mon Sep 17 00:00:00 2001
From: Manish Kumar <manish.kumar@basf.com>
Date: Wed, 26 Feb 2025 15:45:12 +0100
Subject: [PATCH 2/3] fix input data for transform

---
 mambular/preprocessing/preprocessor.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/mambular/preprocessing/preprocessor.py b/mambular/preprocessing/preprocessor.py
index 44c21193..b8df8bc2 100644
--- a/mambular/preprocessing/preprocessor.py
+++ b/mambular/preprocessing/preprocessor.py
@@ -582,6 +582,10 @@ def transform(self, X, embeddings=None):
             raise NotFittedError(
                 "The preprocessor must be fitted before transforming new data. Use .fit or .fit_transform"
             )
+        if isinstance(X, np.ndarray):
+            X = pd.DataFrame(X)
+        else:
+            X = X.copy()
         transformed_X = self.column_transformer.transform(X)  # type: ignore
 
         # Now let's convert this into a dictionary of arrays, one per column

From 5cf1b790c0b296d4bb122445d1a5eb74969dbf23 Mon Sep 17 00:00:00 2001
From: Manish Kumar <manish.kumar@basf.com>
Date: Wed, 26 Feb 2025 15:45:28 +0100
Subject: [PATCH 3/3] formatting

---
 mambular/data_utils/datamodule.py | 128 ++++++++----------------------
 1 file changed, 33 insertions(+), 95 deletions(-)

diff --git a/mambular/data_utils/datamodule.py b/mambular/data_utils/datamodule.py
index 40e80f7b..453a7edf 100644
--- a/mambular/data_utils/datamodule.py
+++ b/mambular/data_utils/datamodule.py
@@ -130,9 +130,7 @@ def preprocess_data(
                     embeddings_val = [embeddings_val]
 
                 split_data += embeddings_train
-                split_result = train_test_split(
-                    *split_data, test_size=val_size, random_state=random_state
-                )
+                split_result = train_test_split(*split_data, test_size=val_size, random_state=random_state)
 
                 self.X_train, self.X_val, self.y_train, self.y_val = split_result[:4]
                 self.embeddings_train = split_result[4::2]
@@ -161,17 +159,13 @@ def preprocess_data(
                 self.embeddings_val = None
 
         # Fit the preprocessor on the combined training and validation data
-        combined_X = pd.concat([self.X_train, self.X_val], axis=0).reset_index(
-            drop=True
-        )
+        combined_X = pd.concat([self.X_train, self.X_val], axis=0).reset_index(drop=True)
         combined_y = np.concatenate((self.y_train, self.y_val), axis=0)
 
         if self.embeddings_train is not None and self.embeddings_val is not None:
             combined_embeddings = [
                 np.concatenate((emb_train, emb_val), axis=0)
-                for emb_train, emb_val in zip(
-                    self.embeddings_train, self.embeddings_val
-                )
+                for emb_train, emb_val in zip(self.embeddings_train, self.embeddings_val, strict=False)
             ]
         else:
             combined_embeddings = None
@@ -179,19 +173,17 @@ def preprocess_data(
         self.preprocessor.fit(combined_X, combined_y, combined_embeddings)
 
         # Update feature info based on the actual processed data
-        (self.num_feature_info, self.cat_feature_info, self.embedding_feature_info) = (
-            self.preprocessor.get_feature_info()
-        )
+        (
+            self.num_feature_info,
+            self.cat_feature_info,
+            self.embedding_feature_info,
+        ) = self.preprocessor.get_feature_info()
 
     def setup(self, stage: str):
         """Transform the data and create DataLoaders."""
         if stage == "fit":
-            train_preprocessed_data = self.preprocessor.transform(
-                self.X_train, self.embeddings_train
-            )
-            val_preprocessed_data = self.preprocessor.transform(
-                self.X_val, self.embeddings_val
-            )
+            train_preprocessed_data = self.preprocessor.transform(self.X_train, self.embeddings_train)
+            val_preprocessed_data = self.preprocessor.transform(self.X_val, self.embeddings_val)
 
             # Initialize lists for tensors
             train_cat_tensors = []
@@ -205,75 +197,40 @@ def setup(self, stage: str):
             for key in self.cat_feature_info:  # type: ignore
                 dtype = (
                     torch.float32
-                    if any(
-                        x in self.cat_feature_info[key]["preprocessing"]
-                        for x in ["onehot", "pretrained"]
-                    )
+                    if any(x in self.cat_feature_info[key]["preprocessing"] for x in ["onehot", "pretrained"])  # type: ignore
                     else torch.long
                 )
 
-                cat_key = "cat_" + str(
-                    key
-                )  # Assuming categorical keys are prefixed with 'cat_'
+                cat_key = "cat_" + str(key)  # Assuming categorical keys are prefixed with 'cat_'
                 if cat_key in train_preprocessed_data:
-                    train_cat_tensors.append(
-                        torch.tensor(train_preprocessed_data[cat_key], dtype=dtype)
-                    )
+                    train_cat_tensors.append(torch.tensor(train_preprocessed_data[cat_key], dtype=dtype))
                 if cat_key in val_preprocessed_data:
-                    val_cat_tensors.append(
-                        torch.tensor(val_preprocessed_data[cat_key], dtype=dtype)
-                    )
+                    val_cat_tensors.append(torch.tensor(val_preprocessed_data[cat_key], dtype=dtype))
 
                 binned_key = "num_" + str(key)  # for binned features
                 if binned_key in train_preprocessed_data:
-                    train_cat_tensors.append(
-                        torch.tensor(train_preprocessed_data[binned_key], dtype=dtype)
-                    )
+                    train_cat_tensors.append(torch.tensor(train_preprocessed_data[binned_key], dtype=dtype))
 
                 if binned_key in val_preprocessed_data:
-                    val_cat_tensors.append(
-                        torch.tensor(val_preprocessed_data[binned_key], dtype=dtype)
-                    )
+                    val_cat_tensors.append(torch.tensor(val_preprocessed_data[binned_key], dtype=dtype))
 
             # Populate tensors for numerical features, if present in processed data
             for key in self.num_feature_info:  # type: ignore
-                num_key = "num_" + str(
-                    key
-                )  # Assuming numerical keys are prefixed with 'num_'
+                num_key = "num_" + str(key)  # Assuming numerical keys are prefixed with 'num_'
                 if num_key in train_preprocessed_data:
-                    train_num_tensors.append(
-                        torch.tensor(
-                            train_preprocessed_data[num_key], dtype=torch.float32
-                        )
-                    )
+                    train_num_tensors.append(torch.tensor(train_preprocessed_data[num_key], dtype=torch.float32))
                 if num_key in val_preprocessed_data:
-                    val_num_tensors.append(
-                        torch.tensor(
-                            val_preprocessed_data[num_key], dtype=torch.float32
-                        )
-                    )
+                    val_num_tensors.append(torch.tensor(val_preprocessed_data[num_key], dtype=torch.float32))
 
             if self.embedding_feature_info is not None:
                 for key in self.embedding_feature_info:
                     if key in train_preprocessed_data:
-                        train_emb_tensors.append(
-                            torch.tensor(
-                                train_preprocessed_data[key], dtype=torch.float32
-                            )
-                        )
+                        train_emb_tensors.append(torch.tensor(train_preprocessed_data[key], dtype=torch.float32))
                     if key in val_preprocessed_data:
-                        val_emb_tensors.append(
-                            torch.tensor(
-                                val_preprocessed_data[key], dtype=torch.float32
-                            )
-                        )
-
-            train_labels = torch.tensor(
-                self.y_train, dtype=self.labels_dtype
-            ).unsqueeze(dim=1)
-            val_labels = torch.tensor(self.y_val, dtype=self.labels_dtype).unsqueeze(
-                dim=1
-            )
+                        val_emb_tensors.append(torch.tensor(val_preprocessed_data[key], dtype=torch.float32))
+
+            train_labels = torch.tensor(self.y_train, dtype=self.labels_dtype).unsqueeze(dim=1)
+            val_labels = torch.tensor(self.y_val, dtype=self.labels_dtype).unsqueeze(dim=1)
 
             self.train_dataset = MambularDataset(
                 train_cat_tensors,
@@ -300,42 +257,27 @@ def preprocess_new_data(self, X, embeddings):
         for key in self.cat_feature_info:  # type: ignore
             dtype = (
                 torch.float32
-                if any(
-                    x in self.cat_feature_info[key]["preprocessing"]
-                    for x in ["onehot", "pretrained"]
-                )
+                if any(x in self.cat_feature_info[key]["preprocessing"] for x in ["onehot", "pretrained"])  # type: ignore
                 else torch.long
             )
-            cat_key = "cat_" + str(
-                key
-            )  # Assuming categorical keys are prefixed with 'cat_'
+            cat_key = "cat_" + str(key)  # Assuming categorical keys are prefixed with 'cat_'
             if cat_key in preprocessed_data:
-                cat_tensors.append(
-                    torch.tensor(preprocessed_data[cat_key], dtype=dtype)
-                )
+                cat_tensors.append(torch.tensor(preprocessed_data[cat_key], dtype=dtype))
 
             binned_key = "num_" + str(key)  # for binned features
             if binned_key in preprocessed_data:
-                cat_tensors.append(
-                    torch.tensor(preprocessed_data[binned_key], dtype=dtype)
-                )
+                cat_tensors.append(torch.tensor(preprocessed_data[binned_key], dtype=dtype))
 
         # Populate tensors for numerical features, if present in processed data
         for key in self.num_feature_info:  # type: ignore
-            num_key = "num_" + str(
-                key
-            )  # Assuming numerical keys are prefixed with 'num_'
+            num_key = "num_" + str(key)  # Assuming numerical keys are prefixed with 'num_'
             if num_key in preprocessed_data:
-                num_tensors.append(
-                    torch.tensor(preprocessed_data[num_key], dtype=torch.float32)
-                )
+                num_tensors.append(torch.tensor(preprocessed_data[num_key], dtype=torch.float32))
 
         if self.embedding_feature_info is not None:
             for key in self.embedding_feature_info:
                 if key in preprocessed_data:
-                    emb_tensors.append(
-                        torch.tensor(preprocessed_data[key], dtype=torch.float32)
-                    )
+                    emb_tensors.append(torch.tensor(preprocessed_data[key], dtype=torch.float32))
 
         return MambularDataset(
             cat_tensors,
@@ -374,9 +316,7 @@ def val_dataloader(self):
             DataLoader: DataLoader instance for the validation dataset.
         """
         if hasattr(self, "val_dataset"):
-            return DataLoader(
-                self.val_dataset, batch_size=self.batch_size, **self.dataloader_kwargs
-            )
+            return DataLoader(self.val_dataset, batch_size=self.batch_size, **self.dataloader_kwargs)
         else:
             raise ValueError("No validation dataset provided!")
 
@@ -387,9 +327,7 @@ def test_dataloader(self):
             DataLoader: DataLoader instance for the test dataset.
         """
         if hasattr(self, "test_dataset"):
-            return DataLoader(
-                self.test_dataset, batch_size=self.batch_size, **self.dataloader_kwargs
-            )
+            return DataLoader(self.test_dataset, batch_size=self.batch_size, **self.dataloader_kwargs)
         else:
             raise ValueError("No test dataset provided!")