From 5b2461287fd0ac8c3ec383dd194391f8f502648d Mon Sep 17 00:00:00 2001
From: Leon Luithlen <leontimnaluithlen@gmail.com>
Date: Wed, 20 May 2026 14:44:12 +0200
Subject: [PATCH 01/15] Enable multi parquet output

---
 src/sequifier/config/preprocess_config.py |  7 +--
 src/sequifier/preprocess.py               | 77 +++++++++++++++--------
 tools/convert_v0_config_to_v1.py          |  3 +
 3 files changed, 57 insertions(+), 30 deletions(-)

diff --git a/src/sequifier/config/preprocess_config.py b/src/sequifier/config/preprocess_config.py
index ee4f2717..549f8426 100644
--- a/src/sequifier/config/preprocess_config.py
+++ b/src/sequifier/config/preprocess_config.py
@@ -101,17 +101,16 @@ def validate_format(cls, v: str) -> str:
     def validate_format2(cls, v: bool, info: ValidationInfo):
         write_format = info.data.get("write_format")
 
-        # Existing check: 'pt' format cannot be combined
         if write_format == "pt" and v is True:
             raise ValueError(
                 "With write_format 'pt', merge_output must be set to False"
             )
 
-        # New constraint: 'parquet' or 'csv' formats cannot be uncombined (split)
-        if write_format != "pt" and v is False:
+        # Allow "parquet" to have merge_output = False
+        if write_format not in ["pt", "parquet"] and v is False:
             raise ValueError(
                 f"With write_format '{write_format}', merge_output must be set to True. "
-                "Only 'pt' format supports uncombined (split) output."
+                "Only 'pt' and 'parquet' formats support uncombined (split) output."
             )
 
         return v
diff --git a/src/sequifier/preprocess.py b/src/sequifier/preprocess.py
index 497feb6f..5d7755cd 100644
--- a/src/sequifier/preprocess.py
+++ b/src/sequifier/preprocess.py
@@ -109,7 +109,7 @@ def __init__(
         if self.merge_output:
             self.target_dir = "temp"
         else:
-            if write_format != "pt":
+            if write_format not in ["pt", "parquet"]:
                 raise ValueError(
                     f"write_format must be 'pt' when merge_output is False, got '{write_format}'"
                 )
@@ -223,6 +223,7 @@ def __init__(
                 self.target_dir,
                 self.batches_per_file,
                 subsequence_start_mode,
+                self.merge_output,
             )
 
             if self.merge_output:
@@ -746,7 +747,7 @@ def _cleanup(self, write_format: str) -> None:
                         logger.info(f"Moving '{file_path}' to '{destination}'")
                         shutil.move(str(file_path), str(destination))
 
-                self._create_metadata_for_folder(folder_path)
+                self._create_metadata_for_folder(folder_path, write_format)
 
         if not os.listdir(directory) or self.target_dir == "temp":
             shutil.rmtree(directory)
@@ -780,7 +781,8 @@ def _export_metadata(
             "n_classes": n_classes,
             "id_maps": id_maps,
             "split_paths": [
-                split_path.replace(".pt", "") for split_path in self.split_paths
+                os.path.splitext(split_path)[0] if not self.merge_output else split_path
+                for split_path in self.split_paths
             ],
             "column_types": col_types,
             "selected_columns_statistics": selected_columns_statistics,
@@ -802,7 +804,7 @@ def _export_metadata(
             json.dump(data_driven_config, f)
 
     @beartype
-    def _create_metadata_for_folder(self, folder_path: str) -> None:
+    def _create_metadata_for_folder(self, folder_path: str, write_format: str) -> None:
         """Scans a directory for .pt files, counts samples, and writes metadata.json.
 
                 This method is used when `write_format` is 'pt' and
@@ -816,41 +818,58 @@ def _create_metadata_for_folder(self, folder_path: str) -> None:
                 Args:
                     folder_path: The path to the directory containing the .pt batch files
                         for a specific data split.
+                    write_format: file format
         """
         logger.info(f"Creating metadata for folder '{folder_path}'")
         batch_files_metadata = []
         total_samples = 0
         directory = Path(folder_path)
 
-        # Find all .pt files in the target folder
-        pt_files = sorted(
-            [f for f in directory.iterdir() if f.is_file() and f.suffix == ".pt"]
+        # Find files matching the current write_format
+        files = sorted(
+            [
+                f
+                for f in directory.iterdir()
+                if f.is_file() and f.suffix == f".{write_format}"
+            ]
         )
 
-        for file_path in pt_files:
+        for file_path in files:
             try:
-                # Load the tensor file to inspect its contents
-                sequences_dict, _, _, _, _ = torch.load(file_path, weights_only=False)
-                if sequences_dict:
-                    # All tensors in the dict have the same number of samples (batch size)
-                    n_samples = sequences_dict[list(sequences_dict.keys())[0]].shape[0]
-
-                    # Store the file's name (relative path) and its sample count
-                    batch_files_metadata.append(
-                        {"path": file_path.name, "samples": n_samples}
+                if write_format == "pt":
+                    sequences_dict, _, _, _, _ = torch.load(
+                        file_path, weights_only=False
+                    )
+                    if sequences_dict:
+                        n_samples = sequences_dict[
+                            list(sequences_dict.keys())[0]
+                        ].shape[0]
+                        batch_files_metadata.append(
+                            {"path": file_path.name, "samples": n_samples}
+                        )
+                        total_samples += n_samples
+                elif write_format == "parquet":
+                    # Use Polars lazy scanning to efficiently count rows and features
+                    lazy_df = pl.scan_parquet(file_path)
+                    n_rows = lazy_df.select(pl.len()).collect().item()
+                    n_cols = (
+                        lazy_df.select(pl.col("inputCol").n_unique()).collect().item()
                     )
-                    total_samples += n_samples
+
+                    if n_cols > 0:
+                        n_samples = n_rows // n_cols
+                        batch_files_metadata.append(
+                            {"path": file_path.name, "samples": n_samples}
+                        )
+                        total_samples += n_samples
             except Exception as e:
-                # Add a warning for robustness in case a file is corrupted
                 logger.warning(f"Could not process file {file_path} for metadata: {e}")
 
-        # Final metadata structure required by SequifierDatasetFromFolder
         metadata = {
             "total_samples": total_samples,
             "batch_files": batch_files_metadata,
         }
 
-        # Write the metadata to a json file in the same folder
         metadata_path = directory / "metadata.json"
         with open(metadata_path, "w") as f:
             json.dump(metadata, f, indent=4)
@@ -1285,6 +1304,7 @@ def _process_batches_multiple_files_inner(
                 target_dir,
                 batches_per_file,
                 subsequence_start_mode,
+                merge_output,
             )
 
             if merge_output:
@@ -1331,6 +1351,7 @@ def _process_batches_single_file(
     target_dir: str,
     batches_per_file: int,
     subsequence_start_mode: str,
+    merge_output: bool,
 ) -> int:
     """Processes batches of data from a single file.
 
@@ -1350,6 +1371,7 @@ def _process_batches_single_file(
         target_dir: The target directory for temporary files.
         batches_per_file: The number of batches to process per file.
         subsequence_start_mode: "distribute" to minimize max subsequence overlap, or "exact".
+        merge_output: merge output
 
     Returns:
         The number of batches processed.
@@ -1374,6 +1396,7 @@ def _process_batches_single_file(
             write_format,
             batches_per_file,
             subsequence_start_mode,
+            merge_output,
         )
         for process_id, (start, end) in enumerate(valid_batch_limits)
     ]
@@ -1653,19 +1676,20 @@ def _write_accumulated_sequences(
         seq_length: The total sequence length.
         col_types: A dictionary mapping column names to their string types.
     """
+
     if not sequences_to_write:
         return
 
     combined_df = pl.concat(sequences_to_write)
-
-    # Construct a unique filename for the batched file
     split_path_batch_seq = split_path.replace(
         f".{write_format}", f"-{process_id}-{file_index_str}.{write_format}"
     )
     out_path = insert_top_folder(split_path_batch_seq, target_dir)
 
-    # Write the combined data
-    process_and_write_data_pt(combined_df, seq_length, out_path, col_types)
+    if write_format == "pt":
+        process_and_write_data_pt(combined_df, seq_length, out_path, col_types)
+    elif write_format == "parquet":
+        combined_df.write_parquet(out_path)
 
 
 @beartype
@@ -1685,6 +1709,7 @@ def preprocess_batch(
     write_format: str,
     batches_per_file: int,
     subsequence_start_mode: str,
+    merge_output: bool,
 ) -> None:
     """Processes a batch of data.
 
@@ -1707,7 +1732,7 @@ def preprocess_batch(
     """
     sequence_ids = sorted(batch.get_column("sequenceId").unique().to_list())
 
-    if write_format == "pt":
+    if not merge_output:
         # New logic for batching sequences into files for .pt format
         sequences_by_split = {i: [] for i in range(len(split_paths))}
         file_indices = {i: 0 for i in range(len(split_paths))}
diff --git a/tools/convert_v0_config_to_v1.py b/tools/convert_v0_config_to_v1.py
index 24355d47..39fb5343 100644
--- a/tools/convert_v0_config_to_v1.py
+++ b/tools/convert_v0_config_to_v1.py
@@ -35,6 +35,9 @@ def convert_preprocess(config):
     write_format = config.get("write_format", "parquet")
     if write_format == "pt":
         config["merge_output"] = False
+    elif write_format == "parquet":
+        # Keep the user's choice, defaulting to True if not present to mimic old behavior
+        config["merge_output"] = config.get("merge_output", True)
     else:
         config["merge_output"] = True
 

From 4e6f3769f59f57fb9e0a3e290f61cc00dc67388a Mon Sep 17 00:00:00 2001
From: Leon Luithlen <leontimnaluithlen@gmail.com>
Date: Wed, 20 May 2026 15:02:18 +0200
Subject: [PATCH 02/15] update vals

---
 ...orical-1-best-embedding-3-0-embeddings.csv |   2 +-
 ...orical-1-best-embedding-3-1-embeddings.csv |   2 +-
 ...orical-1-best-embedding-3-2-embeddings.csv |   2 +-
 ...orical-1-best-embedding-3-3-embeddings.csv |   4 +-
 ...orical-1-best-embedding-3-4-embeddings.csv |   2 +-
 ...orical-1-best-embedding-3-5-embeddings.csv |   4 +-
 ...orical-1-best-embedding-3-6-embeddings.csv |   2 +-
 ...orical-1-best-embedding-3-7-embeddings.csv |   2 +-
 ...inf-size-best-embedding-3-0-embeddings.csv |   6 +-
 ...inf-size-best-embedding-3-1-embeddings.csv |   6 +-
 ...inf-size-best-embedding-3-2-embeddings.csv |   6 +-
 ...inf-size-best-embedding-3-3-embeddings.csv |  12 +-
 ...inf-size-best-embedding-3-4-embeddings.csv |   6 +-
 ...inf-size-best-embedding-3-5-embeddings.csv |  12 +-
 ...inf-size-best-embedding-3-6-embeddings.csv |   6 +-
 ...inf-size-best-embedding-3-7-embeddings.csv |   6 +-
 ...cal-multitarget-5-best-3-3-predictions.csv |   4 +-
 ...cal-multitarget-5-best-3-5-predictions.csv |   4 +-
 ...cal-multitarget-5-best-3-6-predictions.csv |   2 +-
 ...cal-multitarget-5-best-3-7-predictions.csv |   2 +-
 ...al-1-best-3-autoregression-predictions.csv | 438 +++++++++---------
 ...uifier-model-real-1-best-3-predictions.csv |  32 +-
 ...uifier-model-real-3-best-3-predictions.csv |  48 +-
 ...uifier-model-real-5-best-3-predictions.csv |   8 +-
 ...ifier-model-real-50-best-3-predictions.csv |  24 +-
 ...l-categorical-1-best-3-0-probabilities.csv |   2 +-
 ...l-categorical-1-best-3-1-probabilities.csv |   2 +-
 ...l-categorical-1-best-3-2-probabilities.csv |   2 +-
 ...l-categorical-1-best-3-3-probabilities.csv |   4 +-
 ...l-categorical-1-best-3-4-probabilities.csv |   2 +-
 ...l-categorical-1-best-3-5-probabilities.csv |   4 +-
 ...l-categorical-1-best-3-6-probabilities.csv |   2 +-
 ...l-categorical-1-best-3-7-probabilities.csv |   2 +-
 ...l-categorical-3-best-3-0-probabilities.csv |   2 +-
 ...l-categorical-3-best-3-1-probabilities.csv |   2 +-
 ...l-categorical-3-best-3-2-probabilities.csv |   2 +-
 ...l-categorical-3-best-3-3-probabilities.csv |   4 +-
 ...l-categorical-3-best-3-4-probabilities.csv |   2 +-
 ...l-categorical-3-best-3-5-probabilities.csv |   4 +-
 ...l-categorical-3-best-3-6-probabilities.csv |   2 +-
 ...l-categorical-3-best-3-7-probabilities.csv |   2 +-
 ...ical-3-inf-size-best-3-0-probabilities.csv |   6 +-
 ...ical-3-inf-size-best-3-1-probabilities.csv |   6 +-
 ...ical-3-inf-size-best-3-2-probabilities.csv |   6 +-
 ...ical-3-inf-size-best-3-3-probabilities.csv |  12 +-
 ...ical-3-inf-size-best-3-4-probabilities.csv |   6 +-
 ...ical-3-inf-size-best-3-5-probabilities.csv |  12 +-
 ...ical-3-inf-size-best-3-6-probabilities.csv |   6 +-
 ...ical-3-inf-size-best-3-7-probabilities.csv |   6 +-
 ...ical-3-inf-size-best-3-0-probabilities.csv |   6 +-
 ...ical-3-inf-size-best-3-1-probabilities.csv |   6 +-
 ...ical-3-inf-size-best-3-2-probabilities.csv |   6 +-
 ...ical-3-inf-size-best-3-3-probabilities.csv |  12 +-
 ...ical-3-inf-size-best-3-4-probabilities.csv |   6 +-
 ...ical-3-inf-size-best-3-5-probabilities.csv |  12 +-
 ...ical-3-inf-size-best-3-6-probabilities.csv |   6 +-
 ...ical-3-inf-size-best-3-7-probabilities.csv |   6 +-
 ...ical-3-inf-size-best-3-0-probabilities.csv |   6 +-
 ...ical-3-inf-size-best-3-1-probabilities.csv |   6 +-
 ...ical-3-inf-size-best-3-2-probabilities.csv |   6 +-
 ...ical-3-inf-size-best-3-3-probabilities.csv |  12 +-
 ...ical-3-inf-size-best-3-4-probabilities.csv |   6 +-
 ...ical-3-inf-size-best-3-5-probabilities.csv |  12 +-
 ...ical-3-inf-size-best-3-6-probabilities.csv |   6 +-
 ...ical-3-inf-size-best-3-7-probabilities.csv |   6 +-
 ...l-categorical-5-best-3-0-probabilities.csv |   2 +-
 ...l-categorical-5-best-3-1-probabilities.csv |   2 +-
 ...l-categorical-5-best-3-2-probabilities.csv |   2 +-
 ...l-categorical-5-best-3-3-probabilities.csv |   4 +-
 ...l-categorical-5-best-3-4-probabilities.csv |   2 +-
 ...l-categorical-5-best-3-5-probabilities.csv |   4 +-
 ...l-categorical-5-best-3-6-probabilities.csv |   2 +-
 ...l-categorical-5-best-3-7-probabilities.csv |   2 +-
 ...-categorical-50-best-3-0-probabilities.csv |   2 +-
 ...-categorical-50-best-3-1-probabilities.csv |   2 +-
 ...-categorical-50-best-3-2-probabilities.csv |   2 +-
 ...-categorical-50-best-3-3-probabilities.csv |   4 +-
 ...-categorical-50-best-3-4-probabilities.csv |   2 +-
 ...-categorical-50-best-3-5-probabilities.csv |   4 +-
 ...-categorical-50-best-3-6-probabilities.csv |   2 +-
 ...-categorical-50-best-3-7-probabilities.csv |   2 +-
 ...l-multitarget-5-best-3-1-probabilities.csv |   2 +-
 ...l-multitarget-5-best-3-3-probabilities.csv |   4 +-
 ...l-multitarget-5-best-3-4-probabilities.csv |   2 +-
 ...l-multitarget-5-best-3-5-probabilities.csv |   2 +-
 ...l-multitarget-5-best-3-6-probabilities.csv |   2 +-
 ...l-multitarget-5-best-3-7-probabilities.csv |   2 +-
 ...l-multitarget-5-best-3-0-probabilities.csv |   2 +-
 ...l-multitarget-5-best-3-3-probabilities.csv |   4 +-
 ...l-multitarget-5-best-3-6-probabilities.csv |   2 +-
 ...l-multitarget-5-best-3-7-probabilities.csv |   2 +-
 91 files changed, 463 insertions(+), 463 deletions(-)

diff --git a/tests/resources/target_outputs/embeddings/sequifier-model-categorical-1-best-embedding-3-embeddings/sequifier-model-categorical-1-best-embedding-3-0-embeddings.csv b/tests/resources/target_outputs/embeddings/sequifier-model-categorical-1-best-embedding-3-embeddings/sequifier-model-categorical-1-best-embedding-3-0-embeddings.csv
index cf6f12da..76bd77d7 100644
--- a/tests/resources/target_outputs/embeddings/sequifier-model-categorical-1-best-embedding-3-embeddings/sequifier-model-categorical-1-best-embedding-3-0-embeddings.csv
+++ b/tests/resources/target_outputs/embeddings/sequifier-model-categorical-1-best-embedding-3-embeddings/sequifier-model-categorical-1-best-embedding-3-0-embeddings.csv
@@ -1,2 +1,2 @@
 sequenceId,subsequenceId,itemPosition,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199
-0,0,7,0.8649381,0.2456217,-0.47390383,0.26047105,-0.24760997,1.3031985,0.954926,-0.36946198,-0.9127893,-0.7628124,1.0646839,-0.8443962,0.1663453,-0.0073544485,-1.7519352,-0.033109445,-0.45459607,0.115074985,1.1531726,0.9546874,1.2859629,-1.0329498,1.2402244,2.6194518,-0.553548,-0.6339561,-2.1362739,0.834678,-1.004019,-1.0341626,-0.8379356,0.057528913,1.3040087,0.12587658,0.83013254,-0.530677,-1.6233946,0.36828655,1.856288,0.32092205,-1.8282195,-1.3440526,-0.9354178,0.24922994,1.2998881,-1.2362729,-0.055613298,-0.82057893,-0.48329657,1.8687013,-1.416795,-1.1055188,1.6474618,0.9178677,-0.9488746,1.1648147,0.038533058,-0.36139452,-0.6681003,-1.3988646,-0.17723627,-0.077191874,-0.42204535,1.8334286,1.367824,-0.21193194,-0.16146809,-1.3633873,1.904529,0.64752865,-0.56587064,-1.9270079,-0.79660296,-0.49693355,-1.1129527,-0.30148277,-1.2423564,0.9900435,-0.55904824,0.21028504,1.45484,0.7857082,0.47536197,-2.9085505,-0.41873837,-0.30167672,-0.78530884,-0.31538665,-0.99728477,0.013730711,-0.44155562,0.7619628,-1.4535735,1.3455217,0.02702554,1.8463999,1.8265476,0.24507521,0.7862815,-0.90205395,0.41721222,0.29755318,0.40205243,2.083914,0.08692604,-0.7652935,-1.7749742,0.7529008,-2.0421605,-0.7821979,-0.53960085,-0.86472124,0.6763835,0.5417336,1.1983194,0.99648637,0.3503779,1.9535115,-0.9696217,1.1370678,1.1510953,0.26641077,-0.061282996,-0.7830806,-0.050288517,1.0513952,0.3380877,-0.23930785,-0.6178604,0.5530902,-1.3475106,-1.2853457,-0.20373957,-2.644238,1.2245852,0.4053067,-0.6718775,0.4768508,1.4946405,-0.057367038,0.52338034,-0.4141381,-0.38341543,1.4250721,1.7166281,-0.87515885,-0.072568685,-1.651821,-0.09111432,0.9957127,0.033603795,0.62901866,-0.5168066,-0.6019539,-1.1488311,0.656161,-0.9112411,0.51006913,-0.010201925,-0.41575623,-0.99574006,0.5140014,-0.1332448,0.6003735,0.90858626,-1.7565429,-0.56579715,-1.2166045,-0.47435534,0.6017386,0.71392834,0.6312484,-0.22448398,0.010416185,1.595241,-0.3167373,-0.080429725,-1.1402943,-0.3999894,-0.92721295,-1.5729178,-0.34202424,1.4257509,-0.44051436,1.3233937,0.42874828,-1.2166924,-0.4483822,0.6182654,-0.20860793,0.8979676,-0.8103247,1.3890585,-1.2660644,0.34599954,0.12778859,1.0039928,0.563435,0.49836227,-0.8949199
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diff --git a/tests/resources/target_outputs/embeddings/sequifier-model-categorical-1-best-embedding-3-embeddings/sequifier-model-categorical-1-best-embedding-3-1-embeddings.csv b/tests/resources/target_outputs/embeddings/sequifier-model-categorical-1-best-embedding-3-embeddings/sequifier-model-categorical-1-best-embedding-3-1-embeddings.csv
index a1014b17..db25681f 100644
--- a/tests/resources/target_outputs/embeddings/sequifier-model-categorical-1-best-embedding-3-embeddings/sequifier-model-categorical-1-best-embedding-3-1-embeddings.csv
+++ b/tests/resources/target_outputs/embeddings/sequifier-model-categorical-1-best-embedding-3-embeddings/sequifier-model-categorical-1-best-embedding-3-1-embeddings.csv
@@ -1,2 +1,2 @@
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diff --git a/tests/resources/target_outputs/embeddings/sequifier-model-categorical-1-best-embedding-3-embeddings/sequifier-model-categorical-1-best-embedding-3-2-embeddings.csv b/tests/resources/target_outputs/embeddings/sequifier-model-categorical-1-best-embedding-3-embeddings/sequifier-model-categorical-1-best-embedding-3-2-embeddings.csv
index 414a0d6b..5284467d 100644
--- a/tests/resources/target_outputs/embeddings/sequifier-model-categorical-1-best-embedding-3-embeddings/sequifier-model-categorical-1-best-embedding-3-2-embeddings.csv
+++ b/tests/resources/target_outputs/embeddings/sequifier-model-categorical-1-best-embedding-3-embeddings/sequifier-model-categorical-1-best-embedding-3-2-embeddings.csv
@@ -1,2 +1,2 @@
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diff --git a/tests/resources/target_outputs/embeddings/sequifier-model-categorical-1-best-embedding-3-embeddings/sequifier-model-categorical-1-best-embedding-3-3-embeddings.csv b/tests/resources/target_outputs/embeddings/sequifier-model-categorical-1-best-embedding-3-embeddings/sequifier-model-categorical-1-best-embedding-3-3-embeddings.csv
index 03ac3f60..b176d111 100644
--- a/tests/resources/target_outputs/embeddings/sequifier-model-categorical-1-best-embedding-3-embeddings/sequifier-model-categorical-1-best-embedding-3-3-embeddings.csv
+++ b/tests/resources/target_outputs/embeddings/sequifier-model-categorical-1-best-embedding-3-embeddings/sequifier-model-categorical-1-best-embedding-3-3-embeddings.csv
@@ -1,3 +1,3 @@
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diff --git a/tests/resources/target_outputs/embeddings/sequifier-model-categorical-1-best-embedding-3-embeddings/sequifier-model-categorical-1-best-embedding-3-4-embeddings.csv b/tests/resources/target_outputs/embeddings/sequifier-model-categorical-1-best-embedding-3-embeddings/sequifier-model-categorical-1-best-embedding-3-4-embeddings.csv
index ea2c2ac9..45d59f99 100644
--- a/tests/resources/target_outputs/embeddings/sequifier-model-categorical-1-best-embedding-3-embeddings/sequifier-model-categorical-1-best-embedding-3-4-embeddings.csv
+++ b/tests/resources/target_outputs/embeddings/sequifier-model-categorical-1-best-embedding-3-embeddings/sequifier-model-categorical-1-best-embedding-3-4-embeddings.csv
@@ -1,2 +1,2 @@
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diff --git a/tests/resources/target_outputs/embeddings/sequifier-model-categorical-1-best-embedding-3-embeddings/sequifier-model-categorical-1-best-embedding-3-5-embeddings.csv b/tests/resources/target_outputs/embeddings/sequifier-model-categorical-1-best-embedding-3-embeddings/sequifier-model-categorical-1-best-embedding-3-5-embeddings.csv
index 1613a2c3..9a30653c 100644
--- a/tests/resources/target_outputs/embeddings/sequifier-model-categorical-1-best-embedding-3-embeddings/sequifier-model-categorical-1-best-embedding-3-5-embeddings.csv
+++ b/tests/resources/target_outputs/embeddings/sequifier-model-categorical-1-best-embedding-3-embeddings/sequifier-model-categorical-1-best-embedding-3-5-embeddings.csv
@@ -1,3 +1,3 @@
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diff --git a/tests/resources/target_outputs/embeddings/sequifier-model-categorical-1-best-embedding-3-embeddings/sequifier-model-categorical-1-best-embedding-3-6-embeddings.csv b/tests/resources/target_outputs/embeddings/sequifier-model-categorical-1-best-embedding-3-embeddings/sequifier-model-categorical-1-best-embedding-3-6-embeddings.csv
index 1d304c0b..9b428c4a 100644
--- a/tests/resources/target_outputs/embeddings/sequifier-model-categorical-1-best-embedding-3-embeddings/sequifier-model-categorical-1-best-embedding-3-6-embeddings.csv
+++ b/tests/resources/target_outputs/embeddings/sequifier-model-categorical-1-best-embedding-3-embeddings/sequifier-model-categorical-1-best-embedding-3-6-embeddings.csv
@@ -1,2 +1,2 @@
 sequenceId,subsequenceId,itemPosition,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199
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diff --git a/tests/resources/target_outputs/embeddings/sequifier-model-categorical-1-best-embedding-3-embeddings/sequifier-model-categorical-1-best-embedding-3-7-embeddings.csv b/tests/resources/target_outputs/embeddings/sequifier-model-categorical-1-best-embedding-3-embeddings/sequifier-model-categorical-1-best-embedding-3-7-embeddings.csv
index b55f3509..7701bce1 100644
--- a/tests/resources/target_outputs/embeddings/sequifier-model-categorical-1-best-embedding-3-embeddings/sequifier-model-categorical-1-best-embedding-3-7-embeddings.csv
+++ b/tests/resources/target_outputs/embeddings/sequifier-model-categorical-1-best-embedding-3-embeddings/sequifier-model-categorical-1-best-embedding-3-7-embeddings.csv
@@ -1,2 +1,2 @@
 sequenceId,subsequenceId,itemPosition,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199
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diff --git a/tests/resources/target_outputs/embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-0-embeddings.csv b/tests/resources/target_outputs/embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-0-embeddings.csv
index 9b390a60..428d289b 100644
--- a/tests/resources/target_outputs/embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-0-embeddings.csv
+++ b/tests/resources/target_outputs/embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-0-embeddings.csv
@@ -1,4 +1,4 @@
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diff --git a/tests/resources/target_outputs/embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-1-embeddings.csv b/tests/resources/target_outputs/embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-1-embeddings.csv
index 1a0073c6..a6d6cd23 100644
--- a/tests/resources/target_outputs/embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-1-embeddings.csv
+++ b/tests/resources/target_outputs/embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-1-embeddings.csv
@@ -1,4 +1,4 @@
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diff --git a/tests/resources/target_outputs/embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-2-embeddings.csv b/tests/resources/target_outputs/embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-2-embeddings.csv
index a73e17ac..34da42cc 100644
--- a/tests/resources/target_outputs/embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-2-embeddings.csv
+++ b/tests/resources/target_outputs/embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-2-embeddings.csv
@@ -1,4 +1,4 @@
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diff --git a/tests/resources/target_outputs/embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-3-embeddings.csv b/tests/resources/target_outputs/embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-3-embeddings.csv
index 8859de0c..916e7e56 100644
--- a/tests/resources/target_outputs/embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-3-embeddings.csv
+++ b/tests/resources/target_outputs/embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-3-embeddings.csv
@@ -1,7 +1,7 @@
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diff --git a/tests/resources/target_outputs/embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-4-embeddings.csv b/tests/resources/target_outputs/embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-4-embeddings.csv
index 60f1791e..8e5eb006 100644
--- a/tests/resources/target_outputs/embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-4-embeddings.csv
+++ b/tests/resources/target_outputs/embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-4-embeddings.csv
@@ -1,4 +1,4 @@
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diff --git a/tests/resources/target_outputs/embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-5-embeddings.csv b/tests/resources/target_outputs/embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-5-embeddings.csv
index 1e635d29..a656e4e9 100644
--- a/tests/resources/target_outputs/embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-5-embeddings.csv
+++ b/tests/resources/target_outputs/embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-5-embeddings.csv
@@ -1,7 +1,7 @@
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diff --git a/tests/resources/target_outputs/embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-6-embeddings.csv b/tests/resources/target_outputs/embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-6-embeddings.csv
index 9b12849a..d4afd773 100644
--- a/tests/resources/target_outputs/embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-6-embeddings.csv
+++ b/tests/resources/target_outputs/embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-6-embeddings.csv
@@ -1,4 +1,4 @@
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diff --git a/tests/resources/target_outputs/embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-7-embeddings.csv b/tests/resources/target_outputs/embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-7-embeddings.csv
index 5fc23c32..d999f205 100644
--- a/tests/resources/target_outputs/embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-7-embeddings.csv
+++ b/tests/resources/target_outputs/embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-embeddings/sequifier-model-categorical-3-inf-size-best-embedding-3-7-embeddings.csv
@@ -1,4 +1,4 @@
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diff --git a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-3-predictions.csv b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-3-predictions.csv
index 60541650..1e638e7e 100644
--- a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-3-predictions.csv
+++ b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-3-predictions.csv
@@ -1,3 +1,3 @@
 sequenceId,itemPosition,itemId,supCat1,supReal3
-3,8,unknown,0,0.07330977
-4,8,unknown,3,0.021944024
+3,8,unknown,0,0.07330979
+4,8,unknown,3,0.021944009
diff --git a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-5-predictions.csv b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-5-predictions.csv
index d0606637..611d09a2 100644
--- a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-5-predictions.csv
+++ b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-5-predictions.csv
@@ -1,3 +1,3 @@
 sequenceId,itemPosition,itemId,supCat1,supReal3
-6,8,unknown,3,-0.025335371
-7,8,unknown,9,-0.009257194
+6,8,unknown,3,-0.025335379
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diff --git a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-6-predictions.csv b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-6-predictions.csv
index 257d57ec..8f7ca37c 100644
--- a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-6-predictions.csv
+++ b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-6-predictions.csv
@@ -1,2 +1,2 @@
 sequenceId,itemPosition,itemId,supCat1,supReal3
-8,8,unknown,3,-0.0689116
+8,8,unknown,3,-0.06891159
diff --git a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-7-predictions.csv b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-7-predictions.csv
index e8936a87..6039d92f 100644
--- a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-7-predictions.csv
+++ b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-7-predictions.csv
@@ -1,2 +1,2 @@
 sequenceId,itemPosition,itemId,supCat1,supReal3
-9,8,unknown,5,-0.010536101
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diff --git a/tests/resources/target_outputs/predictions/sequifier-model-real-1-best-3-autoregression-predictions.csv b/tests/resources/target_outputs/predictions/sequifier-model-real-1-best-3-autoregression-predictions.csv
index ad5944af..86ab7c7a 100644
--- a/tests/resources/target_outputs/predictions/sequifier-model-real-1-best-3-autoregression-predictions.csv
+++ b/tests/resources/target_outputs/predictions/sequifier-model-real-1-best-3-autoregression-predictions.csv
@@ -1,220 +1,220 @@
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diff --git a/tests/resources/target_outputs/predictions/sequifier-model-real-1-best-3-predictions.csv b/tests/resources/target_outputs/predictions/sequifier-model-real-1-best-3-predictions.csv
index 7a172933..8a20bbbe 100644
--- a/tests/resources/target_outputs/predictions/sequifier-model-real-1-best-3-predictions.csv
+++ b/tests/resources/target_outputs/predictions/sequifier-model-real-1-best-3-predictions.csv
@@ -1,25 +1,25 @@
 sequenceId,itemPosition,itemValue
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 1,8,0.3180774
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 1,10,0.37797877
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 6,8,0.38596562
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 6,10,0.36000836
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diff --git a/tests/resources/target_outputs/predictions/sequifier-model-real-3-best-3-predictions.csv b/tests/resources/target_outputs/predictions/sequifier-model-real-3-best-3-predictions.csv
index 274c0cbd..a06dd55d 100644
--- a/tests/resources/target_outputs/predictions/sequifier-model-real-3-best-3-predictions.csv
+++ b/tests/resources/target_outputs/predictions/sequifier-model-real-3-best-3-predictions.csv
@@ -1,25 +1,25 @@
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diff --git a/tests/resources/target_outputs/predictions/sequifier-model-real-5-best-3-predictions.csv b/tests/resources/target_outputs/predictions/sequifier-model-real-5-best-3-predictions.csv
index 9418546b..fae6087b 100644
--- a/tests/resources/target_outputs/predictions/sequifier-model-real-5-best-3-predictions.csv
+++ b/tests/resources/target_outputs/predictions/sequifier-model-real-5-best-3-predictions.csv
@@ -1,15 +1,15 @@
 sequenceId,itemPosition,itemValue
 0,8,-0.08213164
 0,9,-0.36485112
-0,10,-0.047637917
+0,10,-0.04796779
 0,11,-0.23358852
 0,12,-0.23358852
 1,8,-0.24276772
-1,9,-0.048383728
+1,9,-0.048351455
 1,10,0.042246565
 2,8,-0.377702
 2,9,-0.29325333
-3,8,-0.11563574
+3,8,-0.115176775
 3,9,0.27402145
 4,8,0.16019934
 5,8,-0.36485112
@@ -21,5 +21,5 @@ sequenceId,itemPosition,itemValue
 8,9,-0.115176775
 8,10,-0.29508916
 8,11,-0.29508916
-9,8,-0.14133751
+9,8,-0.14087854
 9,9,0.024806082
diff --git a/tests/resources/target_outputs/predictions/sequifier-model-real-50-best-3-predictions.csv b/tests/resources/target_outputs/predictions/sequifier-model-real-50-best-3-predictions.csv
index f2afae56..953319ec 100644
--- a/tests/resources/target_outputs/predictions/sequifier-model-real-50-best-3-predictions.csv
+++ b/tests/resources/target_outputs/predictions/sequifier-model-real-50-best-3-predictions.csv
@@ -1,14 +1,14 @@
 sequenceId,itemPosition,itemValue
-0,8,-0.061951738
+0,8,-0.061894365
 0,9,-0.20701185
-0,10,-0.11108916
-0,11,-0.019756084
-0,12,-0.03570495
+0,10,-0.11131864
+0,11,-0.020215044
+0,12,-0.03547547
 1,8,-0.035131253
-1,9,0.035778098
-1,10,-0.06582421
+1,9,0.03623706
+1,10,-0.06484892
 2,8,-0.3391924
-2,9,-0.22169855
+2,9,-0.2226165
 3,8,0.095901884
 3,9,0.5291603
 4,8,0.41166648
@@ -17,9 +17,9 @@ sequenceId,itemPosition,itemValue
 6,9,0.3217103
 6,10,0.47775677
 7,8,0.41350234
-8,8,-0.053783678
-8,9,-0.03581969
-8,10,-0.03662287
-8,11,-0.15927997
-9,8,0.05551339
+8,8,-0.054174513
+8,9,-0.03547547
+8,10,-0.03593443
+8,11,-0.1601979
+9,8,0.055054426
 9,9,0.23542577
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-1-best-3-itemId-probabilities/sequifier-model-categorical-1-best-3-0-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-1-best-3-itemId-probabilities/sequifier-model-categorical-1-best-3-0-probabilities.csv
index b9805cef..cd526dfe 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-1-best-3-itemId-probabilities/sequifier-model-categorical-1-best-3-0-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-1-best-3-itemId-probabilities/sequifier-model-categorical-1-best-3-0-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
-0.0063816966,0.006468037,0.024559338,0.0068818387,0.03805276,0.03474256,0.018915702,0.03632237,0.007560946,0.005084042,0.031691603,0.021121178,0.022060128,0.035620768,0.009137776,0.2730295,0.0035919663,0.0051771346,0.012216523,0.008537154,0.039849125,0.022662234,0.005186482,0.06108719,0.03615953,0.009572842,0.13449506,0.0056423564,0.020200063,0.019690773,0.010418236,0.027883159
+0.0063816966,0.006468037,0.024559338,0.0068818387,0.03805276,0.034742553,0.018915702,0.036322363,0.0075609423,0.0050840396,0.031691603,0.021121172,0.022060119,0.035620775,0.009137776,0.27302954,0.0035919647,0.005177132,0.012216523,0.008537154,0.039849125,0.022662228,0.0051864795,0.06108719,0.03615953,0.0095728375,0.13449506,0.005642359,0.020200068,0.019690767,0.010418236,0.027883159
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-1-best-3-itemId-probabilities/sequifier-model-categorical-1-best-3-1-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-1-best-3-itemId-probabilities/sequifier-model-categorical-1-best-3-1-probabilities.csv
index 281df9d1..fde48406 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-1-best-3-itemId-probabilities/sequifier-model-categorical-1-best-3-1-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-1-best-3-itemId-probabilities/sequifier-model-categorical-1-best-3-1-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
-0.004945001,0.0042691757,0.021052634,0.005814609,0.030396374,0.03289294,0.013441503,0.02840752,0.005056638,0.0035540485,0.026184265,0.023003992,0.023714058,0.02700192,0.010255455,0.19368808,0.0035237416,0.006574332,0.007565707,0.008464132,0.03653722,0.017473333,0.004334516,0.08128032,0.031319387,0.007980291,0.26568452,0.0042771553,0.018264785,0.017538317,0.009468171,0.026035896
+0.0049449983,0.0042691776,0.021052632,0.0058146087,0.030396372,0.032892935,0.013441515,0.028407525,0.0050566397,0.0035540466,0.026184268,0.023003984,0.023714056,0.02700193,0.010255454,0.193688,0.003523743,0.0065743346,0.0075657065,0.0084641315,0.036537226,0.017473338,0.0043345136,0.08128023,0.0313194,0.007980294,0.2656846,0.0042771567,0.018264784,0.017538315,0.00946817,0.0260359
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-1-best-3-itemId-probabilities/sequifier-model-categorical-1-best-3-2-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-1-best-3-itemId-probabilities/sequifier-model-categorical-1-best-3-2-probabilities.csv
index 617b4d3a..9d1b4695 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-1-best-3-itemId-probabilities/sequifier-model-categorical-1-best-3-2-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-1-best-3-itemId-probabilities/sequifier-model-categorical-1-best-3-2-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
-0.0070600286,0.005355662,0.02441911,0.0067502055,0.03974409,0.060156845,0.021477923,0.02920292,0.0060693217,0.0048991265,0.030470505,0.029163782,0.024288375,0.040785767,0.00890498,0.2596095,0.0037373006,0.006169805,0.008046767,0.010179635,0.03731209,0.021897094,0.0058717653,0.067005344,0.024129355,0.0085558025,0.117676124,0.005474747,0.022196406,0.018962774,0.011333607,0.033093356
+0.0070600286,0.005355665,0.024419116,0.006750209,0.03974409,0.060156815,0.021477934,0.029202912,0.006069319,0.0048991265,0.030470513,0.029163782,0.02428838,0.040785775,0.008904983,0.2596093,0.0037373006,0.006169808,0.0080467705,0.010179635,0.037312083,0.021897098,0.005871768,0.067005344,0.024129355,0.008555806,0.117676124,0.00547475,0.022196412,0.01896278,0.011333607,0.033093367
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-1-best-3-itemId-probabilities/sequifier-model-categorical-1-best-3-3-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-1-best-3-itemId-probabilities/sequifier-model-categorical-1-best-3-3-probabilities.csv
index d24d26af..1502f2fd 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-1-best-3-itemId-probabilities/sequifier-model-categorical-1-best-3-3-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-1-best-3-itemId-probabilities/sequifier-model-categorical-1-best-3-3-probabilities.csv
@@ -1,3 +1,3 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
-0.007342851,0.00542497,0.023946969,0.007672485,0.055504322,0.04102405,0.023435624,0.020106465,0.0058254935,0.0059792963,0.028401893,0.027254183,0.034069385,0.03580471,0.015107298,0.1438499,0.0050315396,0.0085788155,0.010699977,0.009593447,0.039330103,0.02207771,0.0072748046,0.116141096,0.03376495,0.009846875,0.171267,0.0052832616,0.029783448,0.016034208,0.01082784,0.023715047
-0.0064484174,0.0047859964,0.03050205,0.006627743,0.040947173,0.05350379,0.015772693,0.036306743,0.0063552163,0.0056825955,0.029170569,0.01872254,0.034786753,0.03794616,0.016469186,0.19048862,0.004163757,0.0076852962,0.012417921,0.010849618,0.02091185,0.017051233,0.0060045645,0.041049585,0.118274905,0.009881875,0.14203411,0.0046904692,0.015074657,0.02274932,0.013102149,0.019542499
+0.0073428494,0.0054249666,0.023946963,0.0076724873,0.05550431,0.041024055,0.02343562,0.020106466,0.0058254926,0.005979295,0.028401894,0.027254183,0.03406939,0.03580471,0.0151072955,0.1438499,0.0050315387,0.008578817,0.010699981,0.009593445,0.039330103,0.022077711,0.0072748065,0.116141096,0.03376495,0.009846874,0.17126702,0.0052832607,0.029783443,0.016034205,0.010827838,0.023715047
+0.0064484207,0.0047859964,0.030502057,0.006627743,0.040947184,0.053503774,0.015772693,0.036306743,0.0063552163,0.0056825983,0.029170575,0.01872254,0.034786753,0.037946172,0.016469195,0.19048862,0.004163757,0.0076852962,0.012417914,0.010849618,0.02091185,0.017051233,0.0060045645,0.041049585,0.118274905,0.009881875,0.1420341,0.0046904692,0.015074657,0.022749325,0.013102149,0.019542495
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-1-best-3-itemId-probabilities/sequifier-model-categorical-1-best-3-4-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-1-best-3-itemId-probabilities/sequifier-model-categorical-1-best-3-4-probabilities.csv
index 751ee103..0842a970 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-1-best-3-itemId-probabilities/sequifier-model-categorical-1-best-3-4-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-1-best-3-itemId-probabilities/sequifier-model-categorical-1-best-3-4-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
-0.00701051,0.005817884,0.023757173,0.006713528,0.0308073,0.05775496,0.019987395,0.033620704,0.0073631057,0.0052121445,0.029612558,0.028933667,0.021020679,0.03660924,0.00911048,0.25284815,0.0034472074,0.005770823,0.008732558,0.009616722,0.035415582,0.021718154,0.0055170283,0.059915386,0.022731157,0.009284552,0.14595956,0.006151369,0.020992717,0.020696603,0.0111079905,0.036763094
+0.0070105107,0.005817882,0.02375718,0.0067135287,0.030807294,0.05775498,0.019987397,0.0336207,0.007363106,0.005212145,0.029612567,0.028933655,0.021020675,0.036609236,0.009110481,0.2528482,0.003447206,0.0057708235,0.008732559,0.009616723,0.03541558,0.02171815,0.0055170287,0.059915405,0.022731164,0.009284548,0.14595954,0.006151366,0.020992719,0.020696605,0.011107996,0.036763098
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-1-best-3-itemId-probabilities/sequifier-model-categorical-1-best-3-5-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-1-best-3-itemId-probabilities/sequifier-model-categorical-1-best-3-5-probabilities.csv
index 3c2f38e6..b1ea5679 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-1-best-3-itemId-probabilities/sequifier-model-categorical-1-best-3-5-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-1-best-3-itemId-probabilities/sequifier-model-categorical-1-best-3-5-probabilities.csv
@@ -1,3 +1,3 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
-0.0061376016,0.0055368184,0.02686486,0.006603274,0.05257738,0.04243002,0.019543363,0.03439729,0.0066835904,0.0054204813,0.027758658,0.018920917,0.026187373,0.03562787,0.0103454115,0.21988945,0.0038413843,0.00584591,0.011070427,0.008652827,0.04451155,0.019691685,0.0054005976,0.0735327,0.056998175,0.008044987,0.14140613,0.00463186,0.019962309,0.017421842,0.010648626,0.023414658
-0.0067484817,0.0056581046,0.025895739,0.006502844,0.036626477,0.049278315,0.01836768,0.03315014,0.0066207633,0.0048519536,0.030104274,0.023016918,0.024347993,0.040022057,0.008816339,0.259297,0.0034051205,0.005711451,0.009438155,0.009649961,0.0344164,0.019870114,0.0051980447,0.063726194,0.030300409,0.008548487,0.14363159,0.005186423,0.02200118,0.019676577,0.010164726,0.029770097
+0.0061376016,0.0055368184,0.02686486,0.006603274,0.05257738,0.04243002,0.019543372,0.03439729,0.0066835904,0.0054204813,0.027758658,0.018920917,0.026187373,0.03562787,0.0103454115,0.21988945,0.0038413862,0.0058459127,0.011070427,0.008652827,0.04451155,0.019691685,0.0054005976,0.073532715,0.056998175,0.008044991,0.14140613,0.00463186,0.019962309,0.017421842,0.010648626,0.023414658
+0.006748481,0.0056581046,0.025895737,0.0065028435,0.036626477,0.04927831,0.01836768,0.033150148,0.006620763,0.0048519536,0.030104272,0.023016918,0.02434799,0.040022057,0.008816339,0.259297,0.0034051202,0.005711451,0.00943816,0.009649961,0.0344164,0.01987012,0.0051980447,0.06372621,0.030300407,0.008548483,0.14363155,0.005186423,0.022001177,0.019676577,0.010164736,0.029770095
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-1-best-3-itemId-probabilities/sequifier-model-categorical-1-best-3-6-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-1-best-3-itemId-probabilities/sequifier-model-categorical-1-best-3-6-probabilities.csv
index 2a44110d..179c80ec 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-1-best-3-itemId-probabilities/sequifier-model-categorical-1-best-3-6-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-1-best-3-itemId-probabilities/sequifier-model-categorical-1-best-3-6-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
-0.006577231,0.0059410273,0.025182044,0.006769403,0.042298827,0.07847596,0.024080709,0.030130297,0.006903164,0.0057024346,0.031444874,0.028760785,0.024837974,0.045916792,0.012943255,0.22671299,0.0036830888,0.0054825344,0.009327671,0.010603473,0.033890314,0.02351716,0.005874786,0.0590428,0.030655578,0.009653996,0.109658815,0.0057224305,0.023167757,0.021588279,0.012933784,0.03251973
+0.006577232,0.005941025,0.025182042,0.0067693973,0.04229883,0.07847597,0.0240807,0.0301303,0.0069031618,0.0057024322,0.03144487,0.028760783,0.024837973,0.04591679,0.012943257,0.22671296,0.0036830874,0.005482532,0.009327667,0.0106034735,0.033890318,0.023517158,0.005874784,0.059042808,0.030655574,0.009653993,0.10965886,0.005722431,0.023167748,0.021588275,0.0129337795,0.032519735
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-1-best-3-itemId-probabilities/sequifier-model-categorical-1-best-3-7-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-1-best-3-itemId-probabilities/sequifier-model-categorical-1-best-3-7-probabilities.csv
index 963f80f0..462d3218 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-1-best-3-itemId-probabilities/sequifier-model-categorical-1-best-3-7-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-1-best-3-itemId-probabilities/sequifier-model-categorical-1-best-3-7-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
-0.0062551964,0.005907708,0.022527965,0.006632302,0.0375025,0.04255281,0.01901747,0.029515766,0.0066615692,0.0049623502,0.0286629,0.02540266,0.024885334,0.03338471,0.008949075,0.23237301,0.0036392827,0.0054352307,0.00970244,0.008878192,0.044655204,0.022057772,0.0049628993,0.08231816,0.02434962,0.008952783,0.16011272,0.0052076653,0.023783742,0.018114585,0.01071558,0.03192075
+0.0062551964,0.005907708,0.02252796,0.006632302,0.0375025,0.042552833,0.01901747,0.029515766,0.0066615692,0.0049623456,0.028662894,0.025402665,0.024885334,0.03338471,0.00894907,0.23237301,0.0036392827,0.0054352307,0.00970244,0.008878192,0.044655215,0.022057772,0.004962897,0.08231816,0.02434962,0.008952783,0.16011275,0.0052076653,0.023783736,0.018114585,0.01071558,0.03192075
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-best-3-itemId-probabilities/sequifier-model-categorical-3-best-3-0-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-best-3-itemId-probabilities/sequifier-model-categorical-3-best-3-0-probabilities.csv
index af9cbfda..030ea209 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-best-3-itemId-probabilities/sequifier-model-categorical-3-best-3-0-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-best-3-itemId-probabilities/sequifier-model-categorical-3-best-3-0-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
-0.0056786733,0.005639637,0.019625364,0.009899155,0.028172797,0.06280763,0.018479375,0.031590853,0.008025116,0.004560256,0.024411822,0.030483417,0.024728065,0.034464374,0.021733196,0.23811963,0.00927033,0.010733632,0.013194134,0.0123534985,0.027120981,0.01968651,0.007962699,0.08780438,0.079400204,0.0068851537,0.066043474,0.0059645167,0.032393124,0.024882883,0.012979289,0.014905925
+0.0056786733,0.0056396397,0.019625373,0.00989916,0.028172797,0.06280763,0.018479379,0.03159086,0.00802512,0.004560256,0.02441183,0.030483417,0.024728071,0.034464385,0.02173319,0.23811956,0.009270334,0.010733643,0.013194141,0.012353504,0.027120981,0.01968651,0.007962703,0.08780438,0.07940018,0.006885157,0.066043444,0.0059645167,0.032393124,0.024882888,0.012979289,0.014905925
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-best-3-itemId-probabilities/sequifier-model-categorical-3-best-3-1-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-best-3-itemId-probabilities/sequifier-model-categorical-3-best-3-1-probabilities.csv
index c34885d3..14379442 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-best-3-itemId-probabilities/sequifier-model-categorical-3-best-3-1-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-best-3-itemId-probabilities/sequifier-model-categorical-3-best-3-1-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
-0.0072332993,0.006430847,0.020174876,0.009407937,0.030214977,0.063631825,0.015276678,0.03030443,0.009666992,0.00519301,0.022986874,0.028146466,0.020710755,0.03240999,0.02609427,0.20461944,0.012769073,0.010396391,0.013358195,0.012718733,0.019840606,0.02028157,0.011074808,0.092058115,0.100550756,0.008028017,0.06728826,0.0068513094,0.025008006,0.030172706,0.01773199,0.01936891
+0.007233303,0.006430847,0.02017487,0.009407937,0.03021497,0.063631795,0.015276678,0.030304415,0.009666988,0.0051930123,0.022986863,0.028146453,0.02071075,0.03240998,0.026094265,0.2046195,0.012769067,0.010396391,0.013358195,0.012718733,0.019840602,0.020281564,0.011074808,0.09205814,0.100550726,0.008028014,0.06728822,0.0068513094,0.025007999,0.030172698,0.01773199,0.019368906
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-best-3-itemId-probabilities/sequifier-model-categorical-3-best-3-2-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-best-3-itemId-probabilities/sequifier-model-categorical-3-best-3-2-probabilities.csv
index cedaa9d0..f54d9b81 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-best-3-itemId-probabilities/sequifier-model-categorical-3-best-3-2-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-best-3-itemId-probabilities/sequifier-model-categorical-3-best-3-2-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
-0.0075266226,0.007391209,0.014185688,0.012860328,0.0250728,0.06497636,0.019334143,0.030267743,0.010712495,0.005902854,0.025737323,0.036715753,0.02563689,0.027740803,0.02394826,0.27562156,0.012211113,0.012502683,0.012710198,0.011621474,0.01583627,0.02080951,0.009763861,0.07652671,0.053563192,0.0073146913,0.053682934,0.008438265,0.033447813,0.028250681,0.015079058,0.01461072
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-best-3-itemId-probabilities/sequifier-model-categorical-3-best-3-3-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-best-3-itemId-probabilities/sequifier-model-categorical-3-best-3-3-probabilities.csv
index 27b6cf17..b5ae3c31 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-best-3-itemId-probabilities/sequifier-model-categorical-3-best-3-3-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-best-3-itemId-probabilities/sequifier-model-categorical-3-best-3-3-probabilities.csv
@@ -1,3 +1,3 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-best-3-itemId-probabilities/sequifier-model-categorical-3-best-3-4-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-best-3-itemId-probabilities/sequifier-model-categorical-3-best-3-4-probabilities.csv
index 6c7125f0..0c5b5d15 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-best-3-itemId-probabilities/sequifier-model-categorical-3-best-3-4-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-best-3-itemId-probabilities/sequifier-model-categorical-3-best-3-4-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-best-3-itemId-probabilities/sequifier-model-categorical-3-best-3-5-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-best-3-itemId-probabilities/sequifier-model-categorical-3-best-3-5-probabilities.csv
index 6fb64763..0759929f 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-best-3-itemId-probabilities/sequifier-model-categorical-3-best-3-5-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-best-3-itemId-probabilities/sequifier-model-categorical-3-best-3-5-probabilities.csv
@@ -1,3 +1,3 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-best-3-itemId-probabilities/sequifier-model-categorical-3-best-3-6-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-best-3-itemId-probabilities/sequifier-model-categorical-3-best-3-6-probabilities.csv
index c9954724..cecbf4eb 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-best-3-itemId-probabilities/sequifier-model-categorical-3-best-3-6-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-best-3-itemId-probabilities/sequifier-model-categorical-3-best-3-6-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-best-3-itemId-probabilities/sequifier-model-categorical-3-best-3-7-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-best-3-itemId-probabilities/sequifier-model-categorical-3-best-3-7-probabilities.csv
index e477aab4..917cbb24 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-best-3-itemId-probabilities/sequifier-model-categorical-3-best-3-7-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-best-3-itemId-probabilities/sequifier-model-categorical-3-best-3-7-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-itemId-probabilities/sequifier-model-categorical-3-inf-size-best-3-0-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-itemId-probabilities/sequifier-model-categorical-3-inf-size-best-3-0-probabilities.csv
index 0cae3711..c39d520b 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-itemId-probabilities/sequifier-model-categorical-3-inf-size-best-3-0-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-itemId-probabilities/sequifier-model-categorical-3-inf-size-best-3-0-probabilities.csv
@@ -1,4 +1,4 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-itemId-probabilities/sequifier-model-categorical-3-inf-size-best-3-1-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-itemId-probabilities/sequifier-model-categorical-3-inf-size-best-3-1-probabilities.csv
index 609ba014..c159c246 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-itemId-probabilities/sequifier-model-categorical-3-inf-size-best-3-1-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-itemId-probabilities/sequifier-model-categorical-3-inf-size-best-3-1-probabilities.csv
@@ -1,4 +1,4 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-itemId-probabilities/sequifier-model-categorical-3-inf-size-best-3-2-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-itemId-probabilities/sequifier-model-categorical-3-inf-size-best-3-2-probabilities.csv
index 42366f74..37799880 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-itemId-probabilities/sequifier-model-categorical-3-inf-size-best-3-2-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-itemId-probabilities/sequifier-model-categorical-3-inf-size-best-3-2-probabilities.csv
@@ -1,4 +1,4 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-itemId-probabilities/sequifier-model-categorical-3-inf-size-best-3-3-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-itemId-probabilities/sequifier-model-categorical-3-inf-size-best-3-3-probabilities.csv
index 0ff0df5a..8b6aa288 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-itemId-probabilities/sequifier-model-categorical-3-inf-size-best-3-3-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-itemId-probabilities/sequifier-model-categorical-3-inf-size-best-3-3-probabilities.csv
@@ -1,7 +1,7 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-itemId-probabilities/sequifier-model-categorical-3-inf-size-best-3-4-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-itemId-probabilities/sequifier-model-categorical-3-inf-size-best-3-4-probabilities.csv
index 5b28f954..0150b2b0 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-itemId-probabilities/sequifier-model-categorical-3-inf-size-best-3-4-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-itemId-probabilities/sequifier-model-categorical-3-inf-size-best-3-4-probabilities.csv
@@ -1,4 +1,4 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-itemId-probabilities/sequifier-model-categorical-3-inf-size-best-3-5-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-itemId-probabilities/sequifier-model-categorical-3-inf-size-best-3-5-probabilities.csv
index f21931c7..2efab20a 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-itemId-probabilities/sequifier-model-categorical-3-inf-size-best-3-5-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-itemId-probabilities/sequifier-model-categorical-3-inf-size-best-3-5-probabilities.csv
@@ -1,7 +1,7 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-itemId-probabilities/sequifier-model-categorical-3-inf-size-best-3-6-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-itemId-probabilities/sequifier-model-categorical-3-inf-size-best-3-6-probabilities.csv
index dbe7868b..1259f535 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-itemId-probabilities/sequifier-model-categorical-3-inf-size-best-3-6-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-itemId-probabilities/sequifier-model-categorical-3-inf-size-best-3-6-probabilities.csv
@@ -1,4 +1,4 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-itemId-probabilities/sequifier-model-categorical-3-inf-size-best-3-7-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-itemId-probabilities/sequifier-model-categorical-3-inf-size-best-3-7-probabilities.csv
index eb04e32b..a4ae747f 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-itemId-probabilities/sequifier-model-categorical-3-inf-size-best-3-7-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-itemId-probabilities/sequifier-model-categorical-3-inf-size-best-3-7-probabilities.csv
@@ -1,4 +1,4 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
-0.021811608,0.013736273,0.06867704,0.01990824,0.025413344,0.042476296,0.039054643,0.086140305,0.015027528,0.016408088,0.052656516,0.047944304,0.014508395,0.009043725,0.018304544,0.018811963,0.023150412,0.015841262,0.025444385,0.022209246,0.030985413,0.030684292,0.028433813,0.035490274,0.016962033,0.014339368,0.16734207,0.013210044,0.022449108,0.02184358,0.007352497,0.014339368
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+0.01170118,0.0052330913,0.030678341,0.019595789,0.058046952,0.14593004,0.036824893,0.02589769,0.017668778,0.0141143175,0.025014492,0.04591892,0.020697279,0.014505581,0.07934078,0.029359365,0.017807355,0.026954826,0.011655562,0.028372828,0.08185932,0.015562217,0.02015868,0.029821709,0.016991898,0.014791678,0.0247291,0.02443503,0.010245896,0.056261037,0.010949386,0.028875977
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat1-probabilities/sequifier-model-categorical-3-inf-size-best-3-0-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat1-probabilities/sequifier-model-categorical-3-inf-size-best-3-0-probabilities.csv
index 25434965..0ed4b179 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat1-probabilities/sequifier-model-categorical-3-inf-size-best-3-0-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat1-probabilities/sequifier-model-categorical-3-inf-size-best-3-0-probabilities.csv
@@ -1,4 +1,4 @@
 unknown,other,0,1,2,3,4,5,6,7,8,9
-0.030826421,0.022030652,0.078987546,0.06751216,0.13131393,0.15055215,0.08561467,0.12192086,0.09495206,0.11231876,0.057422858,0.046547886
-0.014711683,0.02225806,0.05971126,0.06762869,0.039293677,0.14344984,0.37063,0.06756268,0.044699837,0.027271146,0.043029413,0.09975371
-0.02534947,0.016624589,0.032804653,0.10283733,0.07251426,0.06045495,0.11929339,0.06698291,0.24050911,0.09967336,0.02698437,0.13597152
+0.030829223,0.02203265,0.07899471,0.067452386,0.13132587,0.15056586,0.08587365,0.12193194,0.0947754,0.11232897,0.057428077,0.046461277
+0.014666316,0.022189427,0.059498083,0.06742016,0.039249096,0.14300752,0.37238505,0.06761797,0.044562005,0.027187055,0.042771243,0.09944612
+0.025165679,0.016633498,0.03282223,0.10289244,0.07257083,0.060428306,0.11959067,0.06701881,0.24063799,0.099726774,0.026998831,0.13551399
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat1-probabilities/sequifier-model-categorical-3-inf-size-best-3-1-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat1-probabilities/sequifier-model-categorical-3-inf-size-best-3-1-probabilities.csv
index 7f354224..c4f4b91a 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat1-probabilities/sequifier-model-categorical-3-inf-size-best-3-1-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat1-probabilities/sequifier-model-categorical-3-inf-size-best-3-1-probabilities.csv
@@ -1,4 +1,4 @@
 unknown,other,0,1,2,3,4,5,6,7,8,9
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat1-probabilities/sequifier-model-categorical-3-inf-size-best-3-2-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat1-probabilities/sequifier-model-categorical-3-inf-size-best-3-2-probabilities.csv
index 7a80708d..8d04302d 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat1-probabilities/sequifier-model-categorical-3-inf-size-best-3-2-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat1-probabilities/sequifier-model-categorical-3-inf-size-best-3-2-probabilities.csv
@@ -1,4 +1,4 @@
 unknown,other,0,1,2,3,4,5,6,7,8,9
-0.022890469,0.0234333,0.13379993,0.06108819,0.048553865,0.04048911,0.04041011,0.06405701,0.46518698,0.035871364,0.03317559,0.03104408
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat1-probabilities/sequifier-model-categorical-3-inf-size-best-3-3-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat1-probabilities/sequifier-model-categorical-3-inf-size-best-3-3-probabilities.csv
index d8bf4b64..26f8fed2 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat1-probabilities/sequifier-model-categorical-3-inf-size-best-3-3-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat1-probabilities/sequifier-model-categorical-3-inf-size-best-3-3-probabilities.csv
@@ -1,7 +1,7 @@
 unknown,other,0,1,2,3,4,5,6,7,8,9
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat1-probabilities/sequifier-model-categorical-3-inf-size-best-3-4-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat1-probabilities/sequifier-model-categorical-3-inf-size-best-3-4-probabilities.csv
index dfe751b9..7d06ac54 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat1-probabilities/sequifier-model-categorical-3-inf-size-best-3-4-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat1-probabilities/sequifier-model-categorical-3-inf-size-best-3-4-probabilities.csv
@@ -1,4 +1,4 @@
 unknown,other,0,1,2,3,4,5,6,7,8,9
-0.03673739,0.027300911,0.054241348,0.2471623,0.033844072,0.04138571,0.09344647,0.03057557,0.26934218,0.06925728,0.03820084,0.05850587
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat1-probabilities/sequifier-model-categorical-3-inf-size-best-3-5-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat1-probabilities/sequifier-model-categorical-3-inf-size-best-3-5-probabilities.csv
index 7840b434..dc13ad4c 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat1-probabilities/sequifier-model-categorical-3-inf-size-best-3-5-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat1-probabilities/sequifier-model-categorical-3-inf-size-best-3-5-probabilities.csv
@@ -1,7 +1,7 @@
 unknown,other,0,1,2,3,4,5,6,7,8,9
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat1-probabilities/sequifier-model-categorical-3-inf-size-best-3-6-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat1-probabilities/sequifier-model-categorical-3-inf-size-best-3-6-probabilities.csv
index 4d1e5033..01f1cf46 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat1-probabilities/sequifier-model-categorical-3-inf-size-best-3-6-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat1-probabilities/sequifier-model-categorical-3-inf-size-best-3-6-probabilities.csv
@@ -1,4 +1,4 @@
 unknown,other,0,1,2,3,4,5,6,7,8,9
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat1-probabilities/sequifier-model-categorical-3-inf-size-best-3-7-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat1-probabilities/sequifier-model-categorical-3-inf-size-best-3-7-probabilities.csv
index 1c3ea98c..cb64c3b7 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat1-probabilities/sequifier-model-categorical-3-inf-size-best-3-7-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat1-probabilities/sequifier-model-categorical-3-inf-size-best-3-7-probabilities.csv
@@ -1,4 +1,4 @@
 unknown,other,0,1,2,3,4,5,6,7,8,9
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat2-probabilities/sequifier-model-categorical-3-inf-size-best-3-0-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat2-probabilities/sequifier-model-categorical-3-inf-size-best-3-0-probabilities.csv
index 8adcff3e..083b09c1 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat2-probabilities/sequifier-model-categorical-3-inf-size-best-3-0-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat2-probabilities/sequifier-model-categorical-3-inf-size-best-3-0-probabilities.csv
@@ -1,4 +1,4 @@
 unknown,other,0,1,2,3,4,5,6,7,8,9
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat2-probabilities/sequifier-model-categorical-3-inf-size-best-3-1-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat2-probabilities/sequifier-model-categorical-3-inf-size-best-3-1-probabilities.csv
index 4da091a6..d7a54c48 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat2-probabilities/sequifier-model-categorical-3-inf-size-best-3-1-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat2-probabilities/sequifier-model-categorical-3-inf-size-best-3-1-probabilities.csv
@@ -1,4 +1,4 @@
 unknown,other,0,1,2,3,4,5,6,7,8,9
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat2-probabilities/sequifier-model-categorical-3-inf-size-best-3-2-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat2-probabilities/sequifier-model-categorical-3-inf-size-best-3-2-probabilities.csv
index d44f62ac..3cfad144 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat2-probabilities/sequifier-model-categorical-3-inf-size-best-3-2-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat2-probabilities/sequifier-model-categorical-3-inf-size-best-3-2-probabilities.csv
@@ -1,4 +1,4 @@
 unknown,other,0,1,2,3,4,5,6,7,8,9
-0.04280596,0.035626277,0.075236924,0.21235083,0.11255862,0.044511154,0.04314169,0.115003034,0.13656949,0.05693063,0.059546374,0.06571901
-0.021073895,0.0131877065,0.25078535,0.12709185,0.077386804,0.0472133,0.043579966,0.14628181,0.057734076,0.05287639,0.11800079,0.044788048
-0.0171146,0.01850529,0.13727179,0.4146487,0.061511766,0.04049829,0.028107189,0.06091399,0.062541485,0.09324627,0.050057575,0.01558302
+0.042999305,0.035647675,0.075098544,0.21165,0.11240646,0.044537887,0.04333655,0.11529708,0.13665152,0.056853674,0.059698623,0.06582273
+0.0210577,0.013177572,0.25059265,0.12749122,0.0773651,0.04726925,0.043631613,0.1461694,0.05774607,0.052835755,0.1179101,0.04475363
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat2-probabilities/sequifier-model-categorical-3-inf-size-best-3-3-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat2-probabilities/sequifier-model-categorical-3-inf-size-best-3-3-probabilities.csv
index cbd9fa7e..d6845334 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat2-probabilities/sequifier-model-categorical-3-inf-size-best-3-3-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat2-probabilities/sequifier-model-categorical-3-inf-size-best-3-3-probabilities.csv
@@ -1,7 +1,7 @@
 unknown,other,0,1,2,3,4,5,6,7,8,9
-0.041671358,0.03195639,0.10992351,0.16534743,0.12491463,0.042927284,0.03669385,0.09534013,0.14777838,0.09305203,0.050823633,0.05957136
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat2-probabilities/sequifier-model-categorical-3-inf-size-best-3-4-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat2-probabilities/sequifier-model-categorical-3-inf-size-best-3-4-probabilities.csv
index 4a963ce9..1cc44686 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat2-probabilities/sequifier-model-categorical-3-inf-size-best-3-4-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat2-probabilities/sequifier-model-categorical-3-inf-size-best-3-4-probabilities.csv
@@ -1,4 +1,4 @@
 unknown,other,0,1,2,3,4,5,6,7,8,9
-0.021187995,0.016056135,0.054370273,0.56599826,0.04597478,0.03479691,0.03562209,0.069337204,0.064503275,0.037477743,0.031313825,0.023361538
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat2-probabilities/sequifier-model-categorical-3-inf-size-best-3-5-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat2-probabilities/sequifier-model-categorical-3-inf-size-best-3-5-probabilities.csv
index d167f512..7ac72445 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat2-probabilities/sequifier-model-categorical-3-inf-size-best-3-5-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat2-probabilities/sequifier-model-categorical-3-inf-size-best-3-5-probabilities.csv
@@ -1,7 +1,7 @@
 unknown,other,0,1,2,3,4,5,6,7,8,9
-0.018467035,0.0150751155,0.15481146,0.15035094,0.087088846,0.046900325,0.063767105,0.12626092,0.050606217,0.066616096,0.17819777,0.041858114
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat2-probabilities/sequifier-model-categorical-3-inf-size-best-3-6-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat2-probabilities/sequifier-model-categorical-3-inf-size-best-3-6-probabilities.csv
index 77a2da2a..9c317eff 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat2-probabilities/sequifier-model-categorical-3-inf-size-best-3-6-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat2-probabilities/sequifier-model-categorical-3-inf-size-best-3-6-probabilities.csv
@@ -1,4 +1,4 @@
 unknown,other,0,1,2,3,4,5,6,7,8,9
-0.024002075,0.016240614,0.087283015,0.24431364,0.04262151,0.05325093,0.07011676,0.16725951,0.06360866,0.048865803,0.12824231,0.05419526
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat2-probabilities/sequifier-model-categorical-3-inf-size-best-3-7-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat2-probabilities/sequifier-model-categorical-3-inf-size-best-3-7-probabilities.csv
index 726cd05b..080e0d42 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat2-probabilities/sequifier-model-categorical-3-inf-size-best-3-7-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-3-inf-size-best-3-supCat2-probabilities/sequifier-model-categorical-3-inf-size-best-3-7-probabilities.csv
@@ -1,4 +1,4 @@
 unknown,other,0,1,2,3,4,5,6,7,8,9
-0.032929927,0.048762422,0.10424309,0.20172025,0.14360094,0.053975083,0.07697672,0.05790681,0.041142356,0.09985877,0.09235427,0.04652943
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-5-best-3-itemId-probabilities/sequifier-model-categorical-5-best-3-0-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-5-best-3-itemId-probabilities/sequifier-model-categorical-5-best-3-0-probabilities.csv
index 190da8f2..9bf90eea 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-5-best-3-itemId-probabilities/sequifier-model-categorical-5-best-3-0-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-5-best-3-itemId-probabilities/sequifier-model-categorical-5-best-3-0-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-5-best-3-itemId-probabilities/sequifier-model-categorical-5-best-3-1-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-5-best-3-itemId-probabilities/sequifier-model-categorical-5-best-3-1-probabilities.csv
index d0b8dcdd..a5f8bc58 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-5-best-3-itemId-probabilities/sequifier-model-categorical-5-best-3-1-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-5-best-3-itemId-probabilities/sequifier-model-categorical-5-best-3-1-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
-0.0043601813,0.006277069,0.017062906,0.009641752,0.043229356,0.057272788,0.014225214,0.038007267,0.007835307,0.006881623,0.023389306,0.016517004,0.020064486,0.039711397,0.008007168,0.22611336,0.0073571578,0.012218174,0.012470044,0.014183984,0.030792022,0.01935528,0.0073876786,0.042832453,0.12202448,0.005623413,0.10076167,0.00729047,0.015891794,0.029221296,0.01824802,0.015745869
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-5-best-3-itemId-probabilities/sequifier-model-categorical-5-best-3-2-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-5-best-3-itemId-probabilities/sequifier-model-categorical-5-best-3-2-probabilities.csv
index f6eb11ce..59ae4a55 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-5-best-3-itemId-probabilities/sequifier-model-categorical-5-best-3-2-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-5-best-3-itemId-probabilities/sequifier-model-categorical-5-best-3-2-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-5-best-3-itemId-probabilities/sequifier-model-categorical-5-best-3-3-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-5-best-3-itemId-probabilities/sequifier-model-categorical-5-best-3-3-probabilities.csv
index 96a968dd..3be3b2c1 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-5-best-3-itemId-probabilities/sequifier-model-categorical-5-best-3-3-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-5-best-3-itemId-probabilities/sequifier-model-categorical-5-best-3-3-probabilities.csv
@@ -1,3 +1,3 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
-0.0042441143,0.006550821,0.01452439,0.008744351,0.049152624,0.060611922,0.017833132,0.039753415,0.007960041,0.00729691,0.020477273,0.01763899,0.020569323,0.037030112,0.009169324,0.214625,0.0073043597,0.011937346,0.012409705,0.016439967,0.02641387,0.018571155,0.008830634,0.052616574,0.1254721,0.0054714065,0.08710683,0.0072533595,0.016600493,0.03165694,0.018508745,0.017224781
-0.004489859,0.0062928908,0.019563705,0.009088493,0.045074664,0.059290394,0.0130718,0.038150992,0.0074357805,0.0067792293,0.023250626,0.017980197,0.020187696,0.03306016,0.007900611,0.21634123,0.007439022,0.01249588,0.012788241,0.014472145,0.030570926,0.019401964,0.0072200936,0.041807346,0.12621096,0.0055920836,0.109203614,0.007921548,0.014996758,0.02833007,0.018433902,0.015157062
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-5-best-3-itemId-probabilities/sequifier-model-categorical-5-best-3-4-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-5-best-3-itemId-probabilities/sequifier-model-categorical-5-best-3-4-probabilities.csv
index 579287c3..7f4da896 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-5-best-3-itemId-probabilities/sequifier-model-categorical-5-best-3-4-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-5-best-3-itemId-probabilities/sequifier-model-categorical-5-best-3-4-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
-0.0040095076,0.00491257,0.020144321,0.008090527,0.03827003,0.05458872,0.012453571,0.032190487,0.0065081166,0.006540465,0.024827303,0.016854107,0.02110345,0.029356547,0.00813333,0.23077905,0.006991138,0.008845747,0.011959966,0.010493616,0.035190098,0.017726563,0.005951777,0.04237902,0.093150176,0.00544687,0.17138276,0.0067939414,0.01596632,0.020403966,0.0153063685,0.013249553
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-5-best-3-itemId-probabilities/sequifier-model-categorical-5-best-3-5-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-5-best-3-itemId-probabilities/sequifier-model-categorical-5-best-3-5-probabilities.csv
index 3a97c371..c99b1ffa 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-5-best-3-itemId-probabilities/sequifier-model-categorical-5-best-3-5-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-5-best-3-itemId-probabilities/sequifier-model-categorical-5-best-3-5-probabilities.csv
@@ -1,3 +1,3 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-5-best-3-itemId-probabilities/sequifier-model-categorical-5-best-3-6-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-5-best-3-itemId-probabilities/sequifier-model-categorical-5-best-3-6-probabilities.csv
index ae779f4b..6866d118 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-5-best-3-itemId-probabilities/sequifier-model-categorical-5-best-3-6-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-5-best-3-itemId-probabilities/sequifier-model-categorical-5-best-3-6-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-5-best-3-itemId-probabilities/sequifier-model-categorical-5-best-3-7-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-5-best-3-itemId-probabilities/sequifier-model-categorical-5-best-3-7-probabilities.csv
index 205bd2db..283f60ec 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-5-best-3-itemId-probabilities/sequifier-model-categorical-5-best-3-7-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-5-best-3-itemId-probabilities/sequifier-model-categorical-5-best-3-7-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-50-best-3-itemId-probabilities/sequifier-model-categorical-50-best-3-0-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-50-best-3-itemId-probabilities/sequifier-model-categorical-50-best-3-0-probabilities.csv
index 50617f10..dd2ce90f 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-50-best-3-itemId-probabilities/sequifier-model-categorical-50-best-3-0-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-50-best-3-itemId-probabilities/sequifier-model-categorical-50-best-3-0-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
-0.0037554468,0.0059194774,0.021355117,0.0037806083,0.03258466,0.058976892,0.0105469655,0.025433978,0.0037908545,0.003991799,0.024652751,0.025988653,0.024072273,0.033754215,0.01523506,0.1802715,0.0049737496,0.009942137,0.011008968,0.01922659,0.024009103,0.023200188,0.0065351715,0.052770447,0.20373955,0.0042302986,0.077167995,0.008635037,0.014409093,0.030130258,0.011237395,0.024673713
+0.003755447,0.005919478,0.021355113,0.0037806085,0.03258467,0.05897691,0.010546966,0.025433974,0.003790851,0.0039917994,0.024652747,0.02598865,0.024072263,0.03375422,0.015235061,0.1802716,0.004973748,0.009942133,0.011008963,0.019226586,0.0240091,0.023200186,0.0065351687,0.05277045,0.20373958,0.004230297,0.07716799,0.008635033,0.014409094,0.030130265,0.01123739,0.024673715
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-50-best-3-itemId-probabilities/sequifier-model-categorical-50-best-3-1-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-50-best-3-itemId-probabilities/sequifier-model-categorical-50-best-3-1-probabilities.csv
index 3df380a6..7b7003a3 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-50-best-3-itemId-probabilities/sequifier-model-categorical-50-best-3-1-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-50-best-3-itemId-probabilities/sequifier-model-categorical-50-best-3-1-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
-0.004108262,0.0065284534,0.025574815,0.004410505,0.030106904,0.056982085,0.009312344,0.028644387,0.00361566,0.005078474,0.023851378,0.023929857,0.02496806,0.034262862,0.015073787,0.18393584,0.0057269186,0.010663452,0.011375004,0.020710839,0.024605205,0.0229786,0.007121385,0.046844255,0.20492586,0.0045672106,0.067667946,0.009786486,0.015642503,0.027877588,0.012733048,0.026390018
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-50-best-3-itemId-probabilities/sequifier-model-categorical-50-best-3-2-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-50-best-3-itemId-probabilities/sequifier-model-categorical-50-best-3-2-probabilities.csv
index 822f3708..fd6efa28 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-50-best-3-itemId-probabilities/sequifier-model-categorical-50-best-3-2-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-50-best-3-itemId-probabilities/sequifier-model-categorical-50-best-3-2-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
-0.0045647332,0.006814505,0.024392653,0.004759701,0.032980636,0.04913894,0.009276463,0.02643774,0.0042945277,0.0050275098,0.024946019,0.02441333,0.025301483,0.029949466,0.014343455,0.16849586,0.0060743075,0.011278832,0.0125147365,0.020937733,0.026535876,0.027676357,0.0074259737,0.047013495,0.20633389,0.0054930253,0.07562099,0.0104807485,0.014071454,0.030229157,0.013769743,0.029406633
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-50-best-3-itemId-probabilities/sequifier-model-categorical-50-best-3-3-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-50-best-3-itemId-probabilities/sequifier-model-categorical-50-best-3-3-probabilities.csv
index cbfdcc6d..ff6923be 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-50-best-3-itemId-probabilities/sequifier-model-categorical-50-best-3-3-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-50-best-3-itemId-probabilities/sequifier-model-categorical-50-best-3-3-probabilities.csv
@@ -1,3 +1,3 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-50-best-3-itemId-probabilities/sequifier-model-categorical-50-best-3-4-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-50-best-3-itemId-probabilities/sequifier-model-categorical-50-best-3-4-probabilities.csv
index df874696..ee563925 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-50-best-3-itemId-probabilities/sequifier-model-categorical-50-best-3-4-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-50-best-3-itemId-probabilities/sequifier-model-categorical-50-best-3-4-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
-0.0039387555,0.006525886,0.024669725,0.0044337166,0.030984685,0.056557063,0.00888818,0.028441498,0.0036812613,0.0049070073,0.023858454,0.020984875,0.022434948,0.03365,0.011881467,0.2170966,0.005855783,0.010228613,0.011613108,0.01785551,0.028310101,0.02185259,0.006087936,0.040233027,0.1692167,0.0044095437,0.09614934,0.009410126,0.013605116,0.024016604,0.012136329,0.026085407
+0.0039387504,0.0065258797,0.024669716,0.004433713,0.030984672,0.056557093,0.008888177,0.028441487,0.003681258,0.0049070055,0.023858456,0.020984871,0.022434939,0.033650003,0.011881462,0.21709673,0.005855772,0.010228604,0.011613099,0.017855505,0.02831009,0.021852583,0.0060879337,0.04023303,0.16921678,0.00440954,0.09614935,0.009410114,0.01360511,0.0240166,0.012136324,0.026085403
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-50-best-3-itemId-probabilities/sequifier-model-categorical-50-best-3-5-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-50-best-3-itemId-probabilities/sequifier-model-categorical-50-best-3-5-probabilities.csv
index 686c6457..fcf89d60 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-50-best-3-itemId-probabilities/sequifier-model-categorical-50-best-3-5-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-50-best-3-itemId-probabilities/sequifier-model-categorical-50-best-3-5-probabilities.csv
@@ -1,3 +1,3 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
-0.004052815,0.006258592,0.023345442,0.004491391,0.03051748,0.05268503,0.008877042,0.026460173,0.004128397,0.00436312,0.022904515,0.023697617,0.024568599,0.03289545,0.01251046,0.19101547,0.005496112,0.010627599,0.011373991,0.017246736,0.027330764,0.024636947,0.0059708143,0.042020697,0.18282121,0.0045206617,0.10554287,0.008928314,0.014136887,0.026559023,0.0111135645,0.028902208
-0.005989765,0.008406408,0.026541123,0.005427466,0.035363622,0.0466547,0.009577352,0.02762081,0.005177149,0.0062065437,0.025016928,0.032284416,0.029640928,0.028332097,0.01934135,0.11613227,0.0074259304,0.013591329,0.012879496,0.027376004,0.021657845,0.031640794,0.012885787,0.055171568,0.22059955,0.007650491,0.0408727,0.011520916,0.017828675,0.041070785,0.014468101,0.035647083
+0.004052816,0.006258594,0.023345444,0.00449139,0.030517468,0.05268502,0.00887704,0.026460174,0.004128396,0.004363119,0.022904512,0.023697617,0.024568599,0.032895457,0.012510463,0.19101547,0.005496111,0.010627602,0.011373989,0.01724674,0.027330758,0.024636947,0.0059708157,0.04202071,0.18282118,0.0045206607,0.10554287,0.008928312,0.014136884,0.02655903,0.011113563,0.02890221
+0.0059897667,0.008406405,0.026541125,0.0054274616,0.03536363,0.04665471,0.0095773535,0.02762081,0.005177147,0.0062065427,0.025016924,0.032284416,0.029640928,0.028332105,0.019341344,0.11613233,0.0074259285,0.013591332,0.0128795,0.027375998,0.021657849,0.031640794,0.01288579,0.055171557,0.22059953,0.0076504927,0.040872708,0.011520912,0.017828679,0.04107078,0.014468098,0.03564709
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-50-best-3-itemId-probabilities/sequifier-model-categorical-50-best-3-6-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-50-best-3-itemId-probabilities/sequifier-model-categorical-50-best-3-6-probabilities.csv
index adc3a7ae..52fec462 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-50-best-3-itemId-probabilities/sequifier-model-categorical-50-best-3-6-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-50-best-3-itemId-probabilities/sequifier-model-categorical-50-best-3-6-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
-0.0041022114,0.006326988,0.020723762,0.0048008403,0.034570813,0.059226178,0.009692526,0.028749218,0.003998919,0.0041838665,0.023596372,0.025296967,0.025585538,0.03109866,0.013962497,0.17769668,0.0053464463,0.01087026,0.010556426,0.021072745,0.026247518,0.023033436,0.007330998,0.05234928,0.19195876,0.004332125,0.080642045,0.009575038,0.016013326,0.028881826,0.012639322,0.025538458
+0.004102211,0.0063269846,0.02072375,0.0048008375,0.034570824,0.059226204,0.0096925255,0.028749216,0.0039989166,0.004183866,0.023596365,0.025296958,0.025585536,0.031098658,0.013962489,0.17769668,0.005346446,0.010870259,0.01055643,0.02107274,0.026247509,0.023033429,0.007330994,0.052349277,0.19195877,0.0043321247,0.08064205,0.009575042,0.016013324,0.02888181,0.012639321,0.02553845
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-50-best-3-itemId-probabilities/sequifier-model-categorical-50-best-3-7-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-50-best-3-itemId-probabilities/sequifier-model-categorical-50-best-3-7-probabilities.csv
index 438874fe..cffd8dd7 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-50-best-3-itemId-probabilities/sequifier-model-categorical-50-best-3-7-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-50-best-3-itemId-probabilities/sequifier-model-categorical-50-best-3-7-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
-0.0038631586,0.0064776703,0.024767213,0.003959403,0.030919716,0.063857056,0.009385453,0.025800953,0.003855722,0.004502773,0.024877686,0.022792442,0.027524808,0.033570547,0.013489491,0.19765602,0.0054532574,0.0107846325,0.010051554,0.020462198,0.026673099,0.021765212,0.006219106,0.048690844,0.17824896,0.0051354133,0.07887633,0.0097349165,0.015730212,0.027250536,0.012058978,0.025564639
+0.0038631586,0.006477673,0.024767213,0.003959401,0.030919716,0.063857056,0.009385453,0.025800953,0.0038557237,0.004502775,0.024877692,0.022792442,0.027524808,0.033570554,0.013489485,0.1976561,0.0054532574,0.010784638,0.010051554,0.020462193,0.026673099,0.021765215,0.006219106,0.048690844,0.1782489,0.0051354156,0.07887633,0.009734921,0.015730219,0.027250536,0.012058978,0.025564646
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-1-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-1-probabilities.csv
index 6b9d4cd7..26319c06 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-1-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-1-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
-0.02757978,0.033920135,0.0352644,0.026169574,0.027414698,0.027247427,0.0343263,0.03215879,0.035035186,0.030168401,0.02789809,0.03271094,0.035344504,0.034741975,0.032225955,0.032462742,0.028217483,0.031554103,0.03120801,0.035676204,0.030488938,0.029054616,0.03119181,0.035311166,0.032082345,0.028742258,0.030691229,0.030270688,0.030084299,0.031229528,0.028773708,0.0307547
+0.02757978,0.033920135,0.0352644,0.026169574,0.027414698,0.027247427,0.0343263,0.03215879,0.035035186,0.030168401,0.02789809,0.03271094,0.035344504,0.034741975,0.032225955,0.032462742,0.028217483,0.031554103,0.03120801,0.035676204,0.030488938,0.029054616,0.03119181,0.035311166,0.032082345,0.028742258,0.030691229,0.030270696,0.030084299,0.031229528,0.028773708,0.0307547
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-3-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-3-probabilities.csv
index 57fa3b5f..8df89327 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-3-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-3-probabilities.csv
@@ -1,3 +1,3 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
-0.033364978,0.027717972,0.028960431,0.033384144,0.035686236,0.036644775,0.030265447,0.03097249,0.028456474,0.028803514,0.03836739,0.030087445,0.026880313,0.030170403,0.03179534,0.03095478,0.032279294,0.02975664,0.03072051,0.029009227,0.03409932,0.034203216,0.030106768,0.028277446,0.030847011,0.031177657,0.030798245,0.029398808,0.030252706,0.03243212,0.030178383,0.033950552
-0.031411365,0.028973259,0.029356807,0.033285685,0.03303163,0.037264887,0.03013853,0.032125752,0.028276425,0.027654836,0.03685246,0.030987127,0.028939197,0.032679845,0.03380999,0.028317781,0.03095356,0.029187296,0.03395916,0.030118862,0.034222953,0.03262129,0.030719524,0.029564887,0.029466776,0.03244446,0.029180052,0.028733686,0.029483747,0.032481648,0.030052282,0.03370424
+0.033364978,0.027717972,0.028960431,0.033384144,0.035686236,0.036644775,0.030265447,0.03097249,0.028456474,0.028803514,0.03836739,0.030087445,0.026880313,0.030170398,0.03179534,0.03095478,0.032279294,0.02975664,0.03072051,0.029009227,0.034099314,0.034203216,0.030106768,0.028277446,0.030847004,0.031177657,0.030798245,0.029398808,0.030252706,0.03243212,0.030178383,0.033950552
+0.031411365,0.028973259,0.029356807,0.033285685,0.03303163,0.037264887,0.03013853,0.032125752,0.028276425,0.027654836,0.03685246,0.030987127,0.02893919,0.032679845,0.03380999,0.028317781,0.030953553,0.029187296,0.03395916,0.030118862,0.034222953,0.03262129,0.030719524,0.029564887,0.029466776,0.03244446,0.029180052,0.028733686,0.029483747,0.032481648,0.030052282,0.03370424
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-4-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-4-probabilities.csv
index 7ee30801..b6a18d75 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-4-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-4-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
-0.02855134,0.032103386,0.035672657,0.025975516,0.029060489,0.027219485,0.035366267,0.03155426,0.035793904,0.031109687,0.029165264,0.03171212,0.033253983,0.031564854,0.030591639,0.035533007,0.02942353,0.032027707,0.028013716,0.03530991,0.030204758,0.03114286,0.030849881,0.03390686,0.03265668,0.027294232,0.032751426,0.03114479,0.030098079,0.03154815,0.028224425,0.031175138
+0.028551342,0.03210338,0.035672657,0.025975518,0.02906049,0.027219487,0.035366267,0.03155426,0.035793904,0.031109689,0.029165266,0.03171212,0.033253983,0.031564854,0.03059164,0.035533007,0.029423531,0.032027707,0.028013717,0.03530991,0.03020476,0.031142863,0.030849883,0.03390686,0.03265668,0.027294233,0.032751426,0.031144792,0.03009808,0.031548142,0.02822442,0.03117514
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-5-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-5-probabilities.csv
index 92b6a6f7..195a72e2 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-5-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-5-probabilities.csv
@@ -1,3 +1,3 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
 0.030342435,0.030822715,0.02960897,0.03322693,0.03194602,0.03620199,0.029623693,0.032765668,0.02717059,0.028776936,0.032656,0.03146398,0.03122887,0.0328284,0.034712065,0.026961364,0.031048443,0.028905584,0.037097085,0.02989018,0.032596547,0.030409556,0.031958636,0.029892432,0.029117517,0.034479015,0.028881233,0.028851666,0.030325295,0.03226891,0.031931575,0.032009717
-0.029181844,0.032261044,0.031046845,0.027925277,0.032212984,0.03316129,0.032004636,0.028391425,0.034346633,0.02482522,0.037898514,0.030189529,0.032011207,0.033572078,0.030944554,0.03464665,0.030707927,0.033008866,0.031740926,0.035867557,0.033871092,0.032074403,0.029566554,0.03079876,0.03171675,0.027234506,0.029196348,0.02724821,0.028529298,0.030738613,0.028472185,0.034608275
+0.029181851,0.032261044,0.031046845,0.027925285,0.032212984,0.033161297,0.032004636,0.028391425,0.034346633,0.02482522,0.037898514,0.030189529,0.032011207,0.033572078,0.030944554,0.03464665,0.030707927,0.033008866,0.031740926,0.035867557,0.033871092,0.03207441,0.029566554,0.03079876,0.03171675,0.027234511,0.029196348,0.02724821,0.028529298,0.03073862,0.028472185,0.034608275
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-6-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-6-probabilities.csv
index 01e5c124..eca04413 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-6-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-6-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
-0.028420437,0.031632587,0.032485284,0.030121041,0.030508144,0.035512682,0.030315991,0.032107607,0.029705128,0.026934983,0.03430073,0.03131456,0.032074325,0.035281524,0.03450778,0.028124444,0.029300941,0.02960142,0.036127225,0.032294188,0.03354204,0.03190299,0.031748407,0.03104404,0.029012788,0.03174257,0.028774712,0.026694056,0.029650616,0.031935107,0.030128015,0.03315363
+0.028420437,0.031632587,0.032485284,0.030121041,0.030508144,0.035512682,0.030315991,0.032107607,0.029705128,0.026934983,0.03430073,0.03131456,0.032074325,0.035281524,0.03450778,0.028124444,0.029300941,0.02960142,0.036127225,0.032294188,0.03354204,0.03190299,0.031748407,0.03104404,0.029012788,0.03174257,0.028774712,0.026694056,0.029650616,0.031935107,0.030128015,0.03315364
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-7-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-7-probabilities.csv
index cc777afd..0222db6e 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-7-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-7-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
-0.03285409,0.028532468,0.03228689,0.029081294,0.03365369,0.030380722,0.032942653,0.029840373,0.033243008,0.032283887,0.034871463,0.029991638,0.02763245,0.028689934,0.02847528,0.037176516,0.031971972,0.03185924,0.024960916,0.031009752,0.031989325,0.035211597,0.02961846,0.030089783,0.033301707,0.02728624,0.034313694,0.031624418,0.031192362,0.031643562,0.029297316,0.032693367
+0.03285409,0.028532468,0.03228689,0.029081294,0.03365369,0.030380722,0.032942653,0.029840373,0.033243008,0.032283887,0.034871463,0.029991638,0.02763245,0.028689934,0.02847528,0.037176516,0.031971972,0.03185924,0.024960924,0.031009752,0.031989325,0.035211597,0.02961846,0.030089783,0.033301707,0.02728624,0.034313694,0.031624418,0.031192362,0.031643562,0.029297316,0.032693367
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-0-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-0-probabilities.csv
index 5ad5d860..62c6fc4c 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-0-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-0-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,0,1,2,3,4,5,6,7,8,9
-0.07594775,0.07880462,0.086174354,0.085758224,0.079532504,0.076291695,0.09287521,0.08811465,0.087236874,0.0821172,0.088243,0.078903906
+0.07594775,0.07880462,0.086174354,0.085758224,0.07953248,0.076291695,0.09287521,0.08811465,0.087236874,0.0821172,0.088243,0.078903906
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-3-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-3-probabilities.csv
index 0bb7a5fb..f8be57ce 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-3-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-3-probabilities.csv
@@ -1,3 +1,3 @@
 unknown,other,0,1,2,3,4,5,6,7,8,9
-0.08852894,0.0742686,0.095033556,0.074851416,0.07880574,0.08831078,0.091138914,0.08929119,0.08064014,0.073842786,0.077981845,0.08730607
-0.0863328,0.071523584,0.094729766,0.07199362,0.07617915,0.096285224,0.08990355,0.08491013,0.078594856,0.07725805,0.07704274,0.095246494
+0.08852895,0.07426861,0.09503356,0.07485142,0.078805745,0.088310786,0.09113893,0.0892912,0.08064015,0.07384279,0.07798186,0.08730608
+0.08633282,0.071523584,0.094729766,0.07199362,0.07617915,0.096285224,0.08990355,0.08491013,0.078594856,0.07725805,0.07704274,0.095246494
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-6-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-6-probabilities.csv
index 62fbb437..7b31642e 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-6-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-6-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,0,1,2,3,4,5,6,7,8,9
-0.08263378,0.0720567,0.091622226,0.073114306,0.07151031,0.09879129,0.08745872,0.08465464,0.080850445,0.081583805,0.077826045,0.09789765
+0.08263379,0.07205671,0.09162223,0.07311431,0.071510315,0.098791294,0.08745874,0.08465465,0.08085046,0.08158381,0.07782605,0.097897656
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-7-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-7-probabilities.csv
index a3710d44..5397606e 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-7-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-7-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,0,1,2,3,4,5,6,7,8,9
-0.082868464,0.083716795,0.08511493,0.08664434,0.08278853,0.07330947,0.08889157,0.08948531,0.08548041,0.08055529,0.087142356,0.074002475
+0.082868464,0.083716795,0.08511493,0.08664434,0.08278853,0.07330947,0.08889157,0.08948531,0.08548041,0.08055529,0.087142356,0.07400249

From cb48736bb8043811f655af0a5da66b06ff00863d Mon Sep 17 00:00:00 2001
From: Leon Luithlen <leontimnaluithlen@gmail.com>
Date: Wed, 20 May 2026 16:13:31 +0200
Subject: [PATCH 03/15] Adapt training and inference to multi parquet

---
 src/sequifier/config/train_config.py          |  12 +-
 src/sequifier/infer.py                        | 133 +++++++++--
 .../sequifier_dataset_from_folder_parquet.py  | 200 ++++++++++++++++
 ...uifier_dataset_from_folder_parquet_lazy.py | 219 ++++++++++++++++++
 src/sequifier/train.py                        |  56 +++--
 .../infer-test-categorical-multitarget.yaml   |   2 +-
 ...eprocess-test-categorical-multitarget.yaml |   2 +-
 .../train-test-categorical-multitarget.yaml   |   5 +-
 tests/integration/test_preprocessing.py       |  20 ++
 9 files changed, 612 insertions(+), 37 deletions(-)
 create mode 100644 src/sequifier/io/sequifier_dataset_from_folder_parquet.py
 create mode 100644 src/sequifier/io/sequifier_dataset_from_folder_parquet_lazy.py

diff --git a/src/sequifier/config/train_config.py b/src/sequifier/config/train_config.py
index 8c4e752d..e88c1ee9 100644
--- a/src/sequifier/config/train_config.py
+++ b/src/sequifier/config/train_config.py
@@ -542,9 +542,9 @@ def validate_training_spec(cls, v, info):
             )
 
         if v.distributed:
-            if not (info.data.get("read_format") == "pt"):
+            if info.data.get("read_format") not in ["pt", "parquet"]:
                 raise ValueError(
-                    "If distributed is set to 'true', the format has to be 'pt'"
+                    "If distributed is set to 'true', the format must be 'pt' or 'parquet' representing a folder dataset."
                 )
 
         if (
@@ -581,15 +581,15 @@ def validate_training_spec(cls, v, info):
                 raise ValueError(
                     f"sampling_strategy '{v.sampling_strategy}' is only admissible if world_size > 1"
                 )
-            if info.data.get("read_format") != "pt":
+            if info.data.get("read_format") not in ["pt", "parquet"]:
                 raise ValueError(
-                    f"sampling_strategy '{v.sampling_strategy}' is only admissible if training data is a folder (read_format='pt')"
+                    f"sampling_strategy '{v.sampling_strategy}' is only admissible if training data is a folder (read_format='pt' or 'parquet')"
                 )
 
         if v.world_size > 1 and v.sampling_strategy == "exact":
-            if info.data.get("read_format") != "pt":
+            if info.data.get("read_format") not in ["pt", "parquet"]:
                 raise ValueError(
-                    "If world_size > 1 and sampling_strategy == 'exact', the input data must be a folder (read_format='pt')."
+                    "If world_size > 1 and sampling_strategy == 'exact', the input data must be a folder (read_format='pt' or 'parquet')."
                 )
 
         if v.data_parallelism is None or v.data_parallelism != "FSDP":
diff --git a/src/sequifier/infer.py b/src/sequifier/infer.py
index 860c127f..4e2fa909 100644
--- a/src/sequifier/infer.py
+++ b/src/sequifier/infer.py
@@ -111,6 +111,21 @@ def load_pt_dataset(data_path: str, start_pct: float, end_pct: float) -> Iterato
         yield torch.load(pt_file, weights_only=False)
 
 
+@beartype
+def load_parquet_folder_dataset(
+    data_path: str, start_pct: float, end_pct: float
+) -> Iterator[Any]:
+    """Lazily loads and yields data from long-format .parquet chunk files in a directory."""
+    parquet_files = sorted(Path(data_path).glob("*.parquet"))
+
+    total = len(parquet_files)
+    start_idx = int(total * start_pct / 100)
+    end_idx = int(total * end_pct / 100)
+
+    for parquet_file in parquet_files[start_idx:end_idx]:
+        yield pl.read_parquet(parquet_file)
+
+
 @beartype
 def infer_worker(
     config: Any,
@@ -143,12 +158,18 @@ def infer_worker(
             `config.read_format == "pt"` to slice the dataset.
     """
     logger.info(f"[INFO] Reading data from '{config.data_path}'...")
+
+    is_folder_input = os.path.isdir(
+        normalize_path(config.data_path, config.project_root)
+    )
     # Step 1: Use Polars for data ingestion
     dataset = None
-    if config.read_format == "parquet":
-        dataset = [pl.read_parquet(config.data_path)]
-    elif config.read_format == "csv":
-        dataset = [pl.read_csv(config.data_path)]
+    if not is_folder_input:
+        # Standalone Single-File Path Execution
+        if config.read_format == "parquet":
+            dataset = [pl.read_parquet(config.data_path)]
+        elif config.read_format == "csv":
+            dataset = [pl.read_csv(config.data_path)]
 
     model_paths = (
         config.model_path
@@ -156,16 +177,25 @@ def infer_worker(
         else [config.model_path]
     )
     for model_path in model_paths:
-        if config.read_format == "pt":
+        if is_folder_input:
             if percentage_limits is None:
                 raise ValueError(
-                    "percentage_limits must be provided for 'pt' read format"
+                    "percentage_limits must be provided for folder-based read formats"
                 )
             start_pct, end_pct = percentage_limits
-            dataset = load_pt_dataset(config.data_path, start_pct, end_pct)
+
+            # Direct folders to their respective lazy loaders based on file format
+            if config.read_format == "pt":
+                dataset = load_pt_dataset(config.data_path, start_pct, end_pct)
+            elif config.read_format == "parquet":
+                dataset = load_parquet_folder_dataset(
+                    config.data_path, start_pct, end_pct
+                )
 
         if dataset is None:
-            raise Exception(f"{config.read_format = } not in ['parquet', 'csv', 'pt']")
+            raise Exception(
+                f"Unsupported input type or read format: {config.read_format}"
+            )
 
         inferer = Inferer(
             config.model_type,
@@ -238,7 +268,11 @@ def infer_embedding(
         prediction_length = inferer.prediction_length
 
         # Step 1: Get embeddings and base position/ID data
-        if config.read_format in ["parquet", "csv"]:
+        is_folder_input = os.path.isdir(
+            normalize_path(config.data_path, config.project_root)
+        )
+
+        if config.read_format in ["parquet", "csv"] and not is_folder_input:
             if config.input_columns is not None:
                 data = subset_to_input_columns(data, config.input_columns)
 
@@ -256,6 +290,25 @@ def infer_embedding(
             item_positions_for_preds_base = (
                 data.get_column("startItemPosition").filter(mask).to_numpy()
             )
+        elif config.read_format == "parquet" and is_folder_input:
+            # Folder-based Parquet chunk logic
+            if config.input_columns is not None:
+                data = subset_to_input_columns(data, config.input_columns)
+
+            n_input_cols = data.get_column("inputCol").n_unique()
+            mask = pl.arange(0, data.height, eager=True) % n_input_cols == 0
+
+            embeddings = get_embeddings(config, inferer, data, column_types)
+
+            sequence_ids_for_preds = (
+                data.get_column("sequenceId").filter(mask).to_numpy()
+            )
+            subsequence_ids_for_preds = (
+                data.get_column("subsequenceId").filter(mask).to_numpy()
+            )
+            item_positions_for_preds_base = (
+                data.get_column("startItemPosition").filter(mask).to_numpy()
+            )
 
         elif config.read_format == "pt":
             (
@@ -319,7 +372,7 @@ def infer_embedding(
             exist_ok=True,
         )
 
-        if config.read_format in ["csv", "parquet"]:
+        if not is_folder_input:
             file_name = f"{model_id}-embeddings.{config.write_format}"
         else:
             dirname = f"{model_id}-embeddings"
@@ -380,7 +433,11 @@ def infer_generative(
     """
     for data_id, data in enumerate(dataset):
         # Step 1: Adapt Data Subsetting (now works on Polars DF)
-        if config.read_format in ["parquet", "csv"]:
+        is_folder_input = os.path.isdir(
+            normalize_path(config.data_path, config.project_root)
+        )
+
+        if config.read_format in ["parquet", "csv"] and not is_folder_input:
             if config.input_columns is not None:
                 data = subset_to_input_columns(data, config.input_columns)
             n_input_cols = data.get_column("inputCol").n_unique()
@@ -433,6 +490,56 @@ def infer_generative(
                 probs, preds, sequence_ids_for_preds = get_probs_preds_autoregression(
                     config, inferer, data, column_types, config.seq_length
                 )
+        elif config.read_format == "parquet" and is_folder_input:
+            # Folder-based Parquet chunk logic
+            if config.input_columns is not None:
+                data = subset_to_input_columns(data, config.input_columns)
+            n_input_cols = data.get_column("inputCol").n_unique()
+
+            # Folder-based inference can accept autoregression extra steps similar to .pt layout
+            extra_steps = (
+                0
+                if config.autoregression_extra_steps is None
+                else config.autoregression_extra_steps
+            )
+
+            if extra_steps == 0:
+                probs, preds = get_probs_preds(config, inferer, data, column_types)
+                mask = pl.arange(0, data.height, eager=True) % n_input_cols == 0
+                sequence_ids_for_preds_base = (
+                    data.get_column("sequenceId").filter(mask).to_numpy()
+                )
+                item_positions_for_preds_base = (
+                    data.get_column("startItemPosition").filter(mask).to_numpy()
+                    + config.seq_length
+                )
+
+                prediction_length = inferer.prediction_length
+                sequence_ids_for_preds = np.repeat(
+                    sequence_ids_for_preds_base, prediction_length
+                )
+                item_positions_repeated = np.repeat(
+                    item_positions_for_preds_base, prediction_length
+                )
+                position_offsets = np.tile(
+                    np.arange(-prediction_length + 1, 1),
+                    len(item_positions_for_preds_base),
+                )
+                item_positions_for_preds = item_positions_repeated + position_offsets
+            else:
+                if inferer.prediction_length != 1:
+                    raise ValueError(
+                        f"prediction_length must be 1 for autoregression, got {inferer.prediction_length}"
+                    )
+                # Treat chunk file as standard autoregressive pass
+                mask = pl.arange(0, data.height, eager=True) % n_input_cols == 0
+                item_positions_for_preds = (
+                    data.get_column("startItemPosition").filter(mask).to_numpy()
+                    + config.seq_length
+                )
+                probs, preds, sequence_ids_for_preds = get_probs_preds_autoregression(
+                    config, inferer, data, column_types, config.seq_length
+                )
         elif config.read_format == "pt":
             sequences_dict, _, sequence_ids_tensor, _, start_positions_tensor = data
             extra_steps = (
@@ -509,7 +616,7 @@ def infer_generative(
             )
 
             for target_column in inferer.target_columns:
-                if config.read_format in ["csv", "parquet"]:
+                if not is_folder_input:
                     file_name = f"{model_id}-{target_column}-probabilities.{config.write_format}"
                 else:
                     dirname = f"{model_id}-{target_column}-probabilities"
@@ -556,7 +663,7 @@ def infer_generative(
             }
         )
 
-        if config.read_format in ["csv", "parquet"]:
+        if not is_folder_input:
             file_name = f"{model_id}-predictions.{config.write_format}"
         else:
             dirname = f"{model_id}-predictions"
diff --git a/src/sequifier/io/sequifier_dataset_from_folder_parquet.py b/src/sequifier/io/sequifier_dataset_from_folder_parquet.py
new file mode 100644
index 00000000..5beb3347
--- /dev/null
+++ b/src/sequifier/io/sequifier_dataset_from_folder_parquet.py
@@ -0,0 +1,200 @@
+import json
+import math
+import os
+from typing import Dict, Iterator, Tuple
+
+import polars as pl
+import torch
+import torch.distributed as dist
+from loguru import logger
+from torch.utils.data import IterableDataset, get_worker_info
+
+from sequifier.config.train_config import TrainModel
+from sequifier.helpers import PANDAS_TO_TORCH_TYPES, normalize_path
+
+
+class SequifierDatasetFromFolderParquet(IterableDataset):
+    """
+    An efficient PyTorch IterableDataset that pre-loads a folder of chunked
+    Parquet files entirely into CPU RAM at initialization.
+
+    Yields full, pre-collated batches natively. Fully supports DDP/FSDP distributed
+    environments using customizable sampling strategies.
+    """
+
+    def __init__(self, data_path: str, config: TrainModel, shuffle: bool = True):
+        super().__init__()
+        self.data_dir = normalize_path(data_path, config.project_root)
+        self.config = config
+        self.batch_size = config.training_spec.batch_size
+        self.shuffle = shuffle
+        self.epoch = 0
+        self.sampling_strategy = config.training_spec.sampling_strategy
+
+        metadata_path = os.path.join(self.data_dir, "metadata.json")
+        if not os.path.exists(metadata_path):
+            raise FileNotFoundError(
+                f"metadata.json not found in '{self.data_dir}'. "
+                "Ensure data is pre-processed with merge_output: False."
+            )
+
+        with open(metadata_path, "r") as f:
+            metadata = json.load(f)
+
+        self.n_samples = metadata["total_samples"]
+
+        logger.info(
+            f"[INFO] Loading Parquet folder dataset into memory from '{self.data_dir}'..."
+        )
+
+        column_torch_types = {
+            col: PANDAS_TO_TORCH_TYPES[config.column_types[col]]
+            for col in config.column_types
+        }
+
+        # Sequence formatting structures matching long-format schema boundaries
+        train_seq_len = self.config.seq_length
+        input_seq_cols = [str(c) for c in range(train_seq_len, 0, -1)]
+        target_seq_cols = [str(c) for c in range(train_seq_len - 1, -1, -1)]
+
+        all_sequences: Dict[str, list[torch.Tensor]] = {
+            col: [] for col in config.input_columns
+        }
+        all_targets: Dict[str, list[torch.Tensor]] = {
+            col: [] for col in config.target_columns
+        }
+
+        # Step 1: Eager I/O reduction pass over all chunk allocations
+        for file_info in metadata["batch_files"]:
+            file_path = os.path.join(self.data_dir, file_info["path"])
+            df = pl.read_parquet(file_path)
+
+            for col in all_sequences.keys():
+                feature_df = df.filter(pl.col("inputCol") == col)
+                if not feature_df.is_empty():
+                    tensor_seq = torch.tensor(
+                        feature_df.select(input_seq_cols).to_numpy(),
+                        dtype=column_torch_types[col],
+                    )
+                    all_sequences[col].append(tensor_seq)
+
+            for col in all_targets.keys():
+                feature_df = df.filter(pl.col("inputCol") == col)
+                if not feature_df.is_empty():
+                    tensor_tgt = torch.tensor(
+                        feature_df.select(target_seq_cols).to_numpy(),
+                        dtype=column_torch_types[col],
+                    )
+                    all_targets[col].append(tensor_tgt)
+            del df
+
+        # Step 2: Consolidate data lists into contiguous blocks
+        self.sequences: Dict[str, torch.Tensor] = {
+            col: torch.cat(tensors, dim=0)
+            for col, tensors in all_sequences.items()
+            if tensors
+        }
+        self.targets: Dict[str, torch.Tensor] = {
+            col: torch.cat(tensors, dim=0)
+            for col, tensors in all_targets.items()
+            if tensors
+        }
+
+        # Step 3: Prevent serialization duplications across worker forks via shared memory flags
+        for tensor in self.sequences.values():
+            tensor.share_memory_()
+        for tensor in self.targets.values():
+            tensor.share_memory_()
+
+        self.target_samples = self._get_target_samples()
+        self.total_batches = self._calculate_total_batches(self.target_samples)
+
+        logger.info(
+            f"[INFO] Parquet Dataset loaded into RAM with {self.target_samples} samples and {self.total_batches} batches."
+        )
+
+    def _calculate_total_batches(self, target_samples: int) -> int:
+        num_workers = self.config.training_spec.num_workers
+        num_workers_to_use = num_workers if num_workers > 0 else 1
+
+        total_batches = 0
+        for worker_id in range(num_workers_to_use):
+            worker_samples = target_samples // num_workers_to_use + (
+                1 if worker_id < target_samples % num_workers_to_use else 0
+            )
+            total_batches += math.ceil(worker_samples / self.batch_size)
+        return total_batches
+
+    def set_epoch(self, epoch: int):
+        """Allows the training loop to synchronize seed steps for shuffling."""
+        self.epoch = epoch
+
+    def _get_target_samples(self) -> int:
+        """Calculates precise sample counts per rank to manage FSDP layer allocations."""
+        world_size = dist.get_world_size() if dist.is_initialized() else 1
+        rank = dist.get_rank() if dist.is_initialized() else 0
+
+        samples_per_rank = [
+            len(range(r, self.n_samples, world_size)) for r in range(world_size)
+        ]
+
+        if self.sampling_strategy == "exact":
+            return samples_per_rank[rank]
+        elif self.sampling_strategy == "oversampling":
+            return max(samples_per_rank)
+        elif self.sampling_strategy == "undersampling":
+            return min(samples_per_rank)
+        return samples_per_rank[rank]
+
+    def __len__(self) -> int:
+        return self.total_batches
+
+    def __iter__(
+        self,
+    ) -> Iterator[
+        Tuple[Dict[str, torch.Tensor], Dict[str, torch.Tensor], None, None, None]
+    ]:
+        world_size = dist.get_world_size() if dist.is_initialized() else 1
+        rank = dist.get_rank() if dist.is_initialized() else 0
+
+        worker_info = get_worker_info()
+        worker_id = worker_info.id if worker_info is not None else 0
+        num_workers = worker_info.num_workers if worker_info is not None else 1
+
+        # 1. Coordinate global shuffling masks
+        indices = torch.arange(self.n_samples)
+        if self.shuffle:
+            g = torch.Generator()
+            g.manual_seed(self.config.seed + self.epoch)
+            indices = indices[torch.randperm(self.n_samples, generator=g)]
+
+        # 2. Slice metrics based on GPU distribution metrics
+        indices_for_rank = indices[rank::world_size].tolist()
+
+        # 3. Synchronize cross-device oversampling/undersampling rules
+        if self.sampling_strategy == "oversampling":
+            while len(indices_for_rank) < self.target_samples:
+                indices_for_rank.extend(
+                    indices_for_rank[: self.target_samples - len(indices_for_rank)]
+                )
+        elif self.sampling_strategy == "undersampling":
+            indices_for_rank = indices_for_rank[: self.target_samples]
+
+        # 4. Map worker task splits
+        indices_for_worker = indices_for_rank[worker_id::num_workers]
+
+        # 5. Extract and pass unified data frames
+        train_seq_len = self.config.seq_length
+        for i in range(0, len(indices_for_worker), self.batch_size):
+            batch_indices = indices_for_worker[i : i + self.batch_size]
+
+            data_batch = {
+                key: tensor[batch_indices, -train_seq_len:]
+                for key, tensor in self.sequences.items()
+            }
+            targets_batch = {
+                key: tensor[batch_indices, -train_seq_len:]
+                for key, tensor in self.targets.items()
+            }
+
+            yield data_batch, targets_batch, None, None, None
diff --git a/src/sequifier/io/sequifier_dataset_from_folder_parquet_lazy.py b/src/sequifier/io/sequifier_dataset_from_folder_parquet_lazy.py
new file mode 100644
index 00000000..b1393302
--- /dev/null
+++ b/src/sequifier/io/sequifier_dataset_from_folder_parquet_lazy.py
@@ -0,0 +1,219 @@
+import json
+import math
+import os
+from typing import Dict, Iterator, Tuple
+
+import polars as pl
+import torch
+import torch.distributed as dist
+from torch.utils.data import IterableDataset, get_worker_info
+
+from sequifier.config.train_config import TrainModel
+from sequifier.helpers import PANDAS_TO_TORCH_TYPES, normalize_path
+
+
+class SequifierDatasetFromFolderParquetLazy(IterableDataset):
+    """
+    An efficient, memory-safe PyTorch IterableDataset for out-of-core training
+    that streams chunked Parquet files from a directory using metadata.json boundaries.
+    """
+
+    def __init__(self, data_path: str, config: TrainModel, shuffle: bool = True):
+        super().__init__()
+        self.data_dir = normalize_path(data_path, config.project_root)
+        self.config = config
+        self.batch_size = config.training_spec.batch_size
+        self.shuffle = shuffle
+        self.epoch = 0
+
+        metadata_path = os.path.join(self.data_dir, "metadata.json")
+        if not os.path.exists(metadata_path):
+            raise FileNotFoundError(f"metadata.json not found in '{self.data_dir}'.")
+
+        with open(metadata_path, "r") as f:
+            metadata = json.load(f)
+
+        self.batch_files_info = metadata["batch_files"]
+        self.total_samples = metadata["total_samples"]
+        self.sampling_strategy = config.training_spec.sampling_strategy
+
+        # Re-use your cross-GPU sync arithmetic
+        self.target_samples = self._get_target_samples()
+        self.total_batches = self._calculate_total_batches(self.target_samples)
+
+        self.column_torch_types = {
+            col: PANDAS_TO_TORCH_TYPES[config.column_types[col]]
+            for col in config.column_types
+        }
+
+    def _calculate_total_batches(self, target_samples: int) -> int:
+        num_workers = self.config.training_spec.num_workers
+        num_workers_to_use = num_workers if num_workers > 0 else 1
+        total_batches = 0
+        for worker_id in range(num_workers_to_use):
+            worker_samples = target_samples // num_workers_to_use + (
+                1 if worker_id < target_samples % num_workers_to_use else 0
+            )
+            total_batches += math.ceil(worker_samples / self.batch_size)
+        return total_batches
+
+    def set_epoch(self, epoch: int):
+        self.epoch = epoch
+
+    def _get_target_samples(self) -> int:
+        world_size = dist.get_world_size() if dist.is_initialized() else 1
+        num_files = len(self.batch_files_info)
+        samples_per_rank = []
+        for r in range(world_size):
+            f_r = list(range(r, num_files, world_size))
+            samples_per_rank.append(
+                sum(self.batch_files_info[i]["samples"] for i in f_r) if f_r else 0
+            )
+
+        if self.sampling_strategy == "exact":
+            return int(samples_per_rank[0])
+        elif self.sampling_strategy == "oversampling":
+            return max(samples_per_rank)
+        else:
+            return min(samples_per_rank)
+
+    def __len__(self) -> int:
+        return self.total_batches
+
+    def __iter__(
+        self,
+    ) -> Iterator[
+        Tuple[Dict[str, torch.Tensor], Dict[str, torch.Tensor], None, None, None]
+    ]:
+        world_size = dist.get_world_size() if dist.is_initialized() else 1
+        rank = dist.get_rank() if dist.is_initialized() else 0
+
+        worker_info = get_worker_info()
+        worker_id = worker_info.id if worker_info is not None else 0
+        num_workers = worker_info.num_workers if worker_info is not None else 1
+
+        num_files = len(self.batch_files_info)
+        files_for_this_rank = list(range(rank, num_files, world_size))
+
+        if not files_for_this_rank and self.sampling_strategy == "oversampling":
+            files_for_this_rank = [rank % num_files]
+
+        base_samples_per_worker = self.target_samples // num_workers
+        remainder = self.target_samples % num_workers
+
+        worker_start_sample = sum(
+            base_samples_per_worker + (1 if i < remainder else 0)
+            for i in range(worker_id)
+        )
+        worker_target_samples = base_samples_per_worker + (
+            1 if worker_id < remainder else 0
+        )
+        worker_end_sample = worker_start_sample + worker_target_samples
+
+        g = torch.Generator()
+        g.manual_seed(self.config.seed + self.epoch)
+        if self.shuffle:
+            file_order = torch.randperm(len(files_for_this_rank), generator=g).tolist()
+            ordered_files = [files_for_this_rank[i] for i in file_order]
+        else:
+            ordered_files = files_for_this_rank.copy()
+
+        extended_files = []
+        current_samples = 0
+        file_idx = 0
+        while current_samples < self.target_samples:
+            f_id = ordered_files[file_idx % len(ordered_files)]
+            extended_files.append(f_id)
+            current_samples += self.batch_files_info[f_id]["samples"]
+            file_idx += 1
+
+        yielded_samples = 0
+        train_seq_len = self.config.seq_length
+        global_file_start_sample = 0
+
+        seq_buffer, tgt_buffer = {}, {}
+        buffer_len = 0
+
+        # Sequence formatting configurations mimicking numpy_to_pytorch logic
+        input_seq_cols = [str(c) for c in range(train_seq_len, 0, -1)]
+        target_seq_cols = [str(c) for c in range(train_seq_len - 1, -1, -1)]
+
+        for f_id in extended_files:
+            if yielded_samples >= worker_target_samples:
+                break
+
+            file_samples = self.batch_files_info[f_id]["samples"]
+            file_start = global_file_start_sample
+            file_end = global_file_start_sample + file_samples
+            global_file_start_sample += file_samples
+
+            if file_end <= worker_start_sample or file_start >= worker_end_sample:
+                continue
+
+            file_path = os.path.join(self.data_dir, self.batch_files_info[f_id]["path"])
+
+            # Read Long format Parquet into Polars
+            df = pl.read_parquet(file_path)
+            feature_names = df["inputCol"].unique().to_list()
+
+            # Slice the sequence IDs matching this worker's chunk boundaries
+            worker_file_start_idx = max(0, worker_start_sample - file_start)
+            worker_file_end_idx = min(file_samples, worker_end_sample - file_start)
+            num_new_samples = worker_file_end_idx - worker_file_start_idx
+
+            if num_new_samples <= 0:
+                continue
+
+            # Process Long format data structures into PyTorch Tensors
+            new_seq, new_tgt = {}, {}
+            for col_name in feature_names:
+                feature_df = df.filter(pl.col("inputCol") == col_name)
+
+                # Extract chunk rows matching worker constraints
+                feature_chunk = feature_df.slice(worker_file_start_idx, num_new_samples)
+
+                torch_type = self.column_torch_types[col_name]
+
+                new_seq[col_name] = torch.tensor(
+                    feature_chunk.select(input_seq_cols).to_numpy(), dtype=torch_type
+                )
+                new_tgt[col_name] = torch.tensor(
+                    feature_chunk.select(target_seq_cols).to_numpy(), dtype=torch_type
+                )
+
+            del df
+
+            if buffer_len == 0:
+                seq_buffer, tgt_buffer = new_seq, new_tgt
+            else:
+                seq_buffer = {
+                    k: torch.cat([seq_buffer[k], new_seq[k]], dim=0) for k in seq_buffer
+                }
+                tgt_buffer = {
+                    k: torch.cat([tgt_buffer[k], new_tgt[k]], dim=0) for k in tgt_buffer
+                }
+
+            buffer_len += num_new_samples
+
+            while buffer_len >= self.batch_size:
+                if yielded_samples >= worker_target_samples:
+                    break
+
+                batch_seq = {k: v[: self.batch_size] for k, v in seq_buffer.items()}
+                batch_tgt = {k: v[: self.batch_size] for k, v in tgt_buffer.items()}
+
+                yield batch_seq, batch_tgt, None, None, None
+                yielded_samples += self.batch_size
+
+                seq_buffer = {k: v[self.batch_size :] for k, v in seq_buffer.items()}
+                tgt_buffer = {k: v[self.batch_size :] for k, v in tgt_buffer.items()}
+                buffer_len -= self.batch_size
+
+        if buffer_len > 0 and yielded_samples < worker_target_samples:
+            remaining_needed = worker_target_samples - yielded_samples
+            final_yield_size = min(buffer_len, remaining_needed)
+
+            batch_seq = {k: v[:final_yield_size] for k, v in seq_buffer.items()}
+            batch_tgt = {k: v[:final_yield_size] for k, v in tgt_buffer.items()}
+
+            yield batch_seq, batch_tgt, None, None, None
diff --git a/src/sequifier/train.py b/src/sequifier/train.py
index 9ce0cf4d..e7455cf8 100644
--- a/src/sequifier/train.py
+++ b/src/sequifier/train.py
@@ -46,6 +46,7 @@
 torch._dynamo.config.suppress_errors = True
 
 from sequifier.config.train_config import TrainModel, load_train_config  # noqa: E402
+from sequifier.helpers import normalize_path  # noqa: E402
 from sequifier.helpers import (  # noqa: E402
     conditional_beartype,
     configure_determinism,
@@ -62,6 +63,12 @@
 from sequifier.io.sequifier_dataset_from_folder_lazy import (  # noqa: E402
     SequifierDatasetFromFolderLazy,
 )
+from sequifier.io.sequifier_dataset_from_folder_parquet import (  # noqa: E402
+    SequifierDatasetFromFolderParquet,
+)
+from sequifier.io.sequifier_dataset_from_folder_parquet_lazy import (  # noqa: E402
+    SequifierDatasetFromFolderParquetLazy,
+)
 from sequifier.model.layers import RMSNorm, SequifierEncoderLayer  # noqa: E402
 from sequifier.optimizers.optimizers import get_optimizer_class  # noqa: E402
 
@@ -143,20 +150,39 @@ def train_worker(
 
     # 1. Create Datasets and DataLoaders with DistributedSampler
     if from_folder:
-        if config.training_spec.load_full_data_to_ram:
-            train_dataset = SequifierDatasetFromFolder(
-                config.training_data_path, config
-            )
-            valid_dataset = SequifierDatasetFromFolder(
-                config.validation_data_path, config
-            )
+        if config.read_format == "pt":
+            if config.training_spec.load_full_data_to_ram:
+                train_dataset = SequifierDatasetFromFolder(
+                    config.training_data_path, config
+                )
+                valid_dataset = SequifierDatasetFromFolder(
+                    config.validation_data_path, config
+                )
+            else:
+                train_dataset = SequifierDatasetFromFolderLazy(
+                    config.training_data_path, config
+                )
+                valid_dataset = SequifierDatasetFromFolderLazy(
+                    config.validation_data_path, config
+                )
+        elif config.read_format == "parquet":
+            if config.training_spec.load_full_data_to_ram:
+                train_dataset = SequifierDatasetFromFolderParquet(
+                    config.training_data_path, config
+                )
+                valid_dataset = SequifierDatasetFromFolderParquet(
+                    config.validation_data_path, config
+                )
+            else:
+                train_dataset = SequifierDatasetFromFolderParquetLazy(
+                    config.training_data_path, config
+                )
+                valid_dataset = SequifierDatasetFromFolderParquetLazy(
+                    config.validation_data_path, config
+                )
         else:
-            train_dataset = SequifierDatasetFromFolderLazy(
-                config.training_data_path, config
-            )
-            valid_dataset = SequifierDatasetFromFolderLazy(
-                config.validation_data_path, config
-            )
+            raise Exception("Not allowed")
+
     else:
         if config.training_spec.distributed:
             raise ValueError(
@@ -414,7 +440,9 @@ def train(args: Any, args_config: dict[str, Any]) -> None:
     torch.set_float32_matmul_precision(config.training_spec.float32_matmul_precision)
 
     world_size = config.training_spec.world_size
-    from_folder = config.read_format == "pt"
+    from_folder = os.path.isdir(
+        normalize_path(config.training_data_path, config.project_root)
+    )
 
     if config.training_spec.distributed:
         if "RANK" in os.environ and "WORLD_SIZE" in os.environ:
diff --git a/tests/configs/infer-test-categorical-multitarget.yaml b/tests/configs/infer-test-categorical-multitarget.yaml
index dfad330a..d65fd107 100644
--- a/tests/configs/infer-test-categorical-multitarget.yaml
+++ b/tests/configs/infer-test-categorical-multitarget.yaml
@@ -4,7 +4,7 @@ metadata_config_path: tests/project_folder/configs/metadata_configs/test-data-ca
 model_type: generative
 model_path: tests/project_folder/models/sequifier-model-categorical-multitarget-5-best-3.onnx
 data_path: tests/project_folder/data/test-data-categorical-multitarget-5-split2
-read_format: pt
+read_format: parquet
 write_format: csv
 
 input_columns: null
diff --git a/tests/configs/preprocess-test-categorical-multitarget.yaml b/tests/configs/preprocess-test-categorical-multitarget.yaml
index 30b595b8..aee30ff6 100644
--- a/tests/configs/preprocess-test-categorical-multitarget.yaml
+++ b/tests/configs/preprocess-test-categorical-multitarget.yaml
@@ -2,7 +2,7 @@ project_root: tests/project_folder
 data_path: tests/resources/source_data/test-data-categorical-multitarget-5.csv
 merge_output: false
 read_format: csv
-write_format: pt
+write_format: parquet
 selected_columns: null
 
 split_ratios:
diff --git a/tests/configs/train-test-categorical-multitarget.yaml b/tests/configs/train-test-categorical-multitarget.yaml
index 0b3d48f2..395fb71b 100644
--- a/tests/configs/train-test-categorical-multitarget.yaml
+++ b/tests/configs/train-test-categorical-multitarget.yaml
@@ -1,15 +1,15 @@
 project_root: tests/project_folder
 model_name: model-categorical-multitarget-5
-read_format: pt
+read_format: parquet
 metadata_config_path: configs/metadata_configs/test-data-categorical-multitarget-5.json
 
+
 input_columns: null
 target_columns: [itemId, supCat1, supReal3]
 target_column_types:
   itemId: categorical
   supCat1: categorical
   supReal3: real
-
 seq_length: 8
 inference_batch_size: 10
 
@@ -75,3 +75,4 @@ training_spec:
     decoder: bfloat16
     embedding: float32
     norm: float32
+  load_full_data_to_ram: false
diff --git a/tests/integration/test_preprocessing.py b/tests/integration/test_preprocessing.py
index e83a823e..557e8818 100644
--- a/tests/integration/test_preprocessing.py
+++ b/tests/integration/test_preprocessing.py
@@ -67,6 +67,22 @@ def test_metadata_config(metadata_configs):
             assert "mean" in metadata_config["selected_columns_statistics"]["itemValue"]
 
 
+def load_parquet_folder_outputs(path):
+    """Reads a directory of Parquet chunks into a single sorted Polars DataFrame."""
+    # Polars natively supports reading all matching files via glob patterns
+    data = pl.read_parquet(os.path.join(path, "*.parquet"))
+
+    other_cols = [
+        col
+        for col in data.columns
+        if col not in ["sequenceId", "subsequenceId", "startItemPosition", "inputCol"]
+    ]
+
+    return data[
+        ["sequenceId", "subsequenceId", "startItemPosition", "inputCol"] + other_cols
+    ].sort(["sequenceId", "subsequenceId", "startItemPosition", "inputCol"])
+
+
 def load_pt_outputs(path):
     contents = []
     for root, _, files in os.walk(path):
@@ -135,6 +151,10 @@ def read_preprocessing_outputs(path, variant):
     if variant == "real":
         return pl.read_parquet(f"{path}.parquet")
     elif variant == "categorical":
+        if os.path.isdir(path) and any(
+            f.endswith(".parquet") for f in os.listdir(path)
+        ):
+            return load_parquet_folder_outputs(path)
         return load_pt_outputs(path)
 
 

From 2a0bc1679297255e27ffeaa51f1fe963d6d50559 Mon Sep 17 00:00:00 2001
From: Leon Luithlen <leontimnaluithlen@gmail.com>
Date: Wed, 20 May 2026 16:20:43 +0200
Subject: [PATCH 04/15] Adapt embedding outputs

---
 ...cal-multitarget-5-best-3-0-predictions.csv |   2 +-
 ...cal-multitarget-5-best-3-1-predictions.csv |   2 +-
 ...cal-multitarget-5-best-3-2-predictions.csv |   2 +-
 ...cal-multitarget-5-best-3-3-predictions.csv |   4 +-
 ...cal-multitarget-5-best-3-4-predictions.csv |   2 +-
 ...cal-multitarget-5-best-3-5-predictions.csv |   4 +-
 ...cal-multitarget-5-best-3-6-predictions.csv |   2 +-
 ...cal-multitarget-5-best-3-7-predictions.csv |   2 +-
 ...al-1-best-3-autoregression-predictions.csv | 438 +++++++++---------
 ...l-multitarget-5-best-3-0-probabilities.csv |   2 +-
 ...l-multitarget-5-best-3-1-probabilities.csv |   2 +-
 ...l-multitarget-5-best-3-2-probabilities.csv |   2 +-
 ...l-multitarget-5-best-3-3-probabilities.csv |   4 +-
 ...l-multitarget-5-best-3-4-probabilities.csv |   2 +-
 ...l-multitarget-5-best-3-5-probabilities.csv |   4 +-
 ...l-multitarget-5-best-3-6-probabilities.csv |   2 +-
 ...l-multitarget-5-best-3-7-probabilities.csv |   2 +-
 ...l-multitarget-5-best-3-0-probabilities.csv |   2 +-
 ...l-multitarget-5-best-3-1-probabilities.csv |   2 +-
 ...l-multitarget-5-best-3-2-probabilities.csv |   2 +-
 ...l-multitarget-5-best-3-3-probabilities.csv |   4 +-
 ...l-multitarget-5-best-3-4-probabilities.csv |   2 +-
 ...l-multitarget-5-best-3-5-probabilities.csv |   4 +-
 ...l-multitarget-5-best-3-6-probabilities.csv |   2 +-
 ...l-multitarget-5-best-3-7-probabilities.csv |   2 +-
 25 files changed, 249 insertions(+), 249 deletions(-)

diff --git a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-0-predictions.csv b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-0-predictions.csv
index d4924ee5..0b1c1c1a 100644
--- a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-0-predictions.csv
+++ b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-0-predictions.csv
@@ -1,2 +1,2 @@
 sequenceId,itemPosition,itemId,supCat1,supReal3
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diff --git a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-1-predictions.csv b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-1-predictions.csv
index 42ce85d1..f220577f 100644
--- a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-1-predictions.csv
+++ b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-1-predictions.csv
@@ -1,2 +1,2 @@
 sequenceId,itemPosition,itemId,supCat1,supReal3
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diff --git a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-2-predictions.csv b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-2-predictions.csv
index d6865563..9b23d933 100644
--- a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-2-predictions.csv
+++ b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-2-predictions.csv
@@ -1,2 +1,2 @@
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diff --git a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-3-predictions.csv b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-3-predictions.csv
index 1e638e7e..8cc01f75 100644
--- a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-3-predictions.csv
+++ b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-3-predictions.csv
@@ -1,3 +1,3 @@
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diff --git a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-4-predictions.csv b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-4-predictions.csv
index 008467de..76b28afb 100644
--- a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-4-predictions.csv
+++ b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-4-predictions.csv
@@ -1,2 +1,2 @@
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diff --git a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-5-predictions.csv b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-5-predictions.csv
index 611d09a2..b4d499f9 100644
--- a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-5-predictions.csv
+++ b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-5-predictions.csv
@@ -1,3 +1,3 @@
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diff --git a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-6-predictions.csv b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-6-predictions.csv
index 8f7ca37c..aa9b9d48 100644
--- a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-6-predictions.csv
+++ b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-6-predictions.csv
@@ -1,2 +1,2 @@
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diff --git a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-7-predictions.csv b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-7-predictions.csv
index 6039d92f..6a841d79 100644
--- a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-7-predictions.csv
+++ b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-7-predictions.csv
@@ -1,2 +1,2 @@
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diff --git a/tests/resources/target_outputs/predictions/sequifier-model-real-1-best-3-autoregression-predictions.csv b/tests/resources/target_outputs/predictions/sequifier-model-real-1-best-3-autoregression-predictions.csv
index 86ab7c7a..981cfaec 100644
--- a/tests/resources/target_outputs/predictions/sequifier-model-real-1-best-3-autoregression-predictions.csv
+++ b/tests/resources/target_outputs/predictions/sequifier-model-real-1-best-3-autoregression-predictions.csv
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+9,23,0.016698465
+9,24,0.075476706
+9,25,0.022938194
+9,26,-0.07777103
+9,27,-0.092746384
+9,28,-0.07277925
+9,29,-0.058802254
+9,30,-0.04282855
+9,31,-0.01886799
+9,32,0.057256702
+9,33,-0.008042061
+9,34,-0.15714039
+9,35,0.035792034
+9,36,0.041782174
+9,37,-0.17810588
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-0-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-0-probabilities.csv
index daced988..deccb543 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-0-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-0-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
-0.030253205,0.029340679,0.033842698,0.026731277,0.032777272,0.029987521,0.03500179,0.029160175,0.035938565,0.028857358,0.03488497,0.030091744,0.029473357,0.030105012,0.029487064,0.03892949,0.030834181,0.032466665,0.025871553,0.034734134,0.03237362,0.034012306,0.02962248,0.03157022,0.033536177,0.025670096,0.032388803,0.030012256,0.028979745,0.03194875,0.027210325,0.033906575
+0.029919926,0.029241586,0.03407666,0.02653628,0.031944077,0.030346371,0.03517379,0.029561197,0.03584215,0.028416175,0.034457605,0.030069351,0.029750984,0.030594636,0.029850584,0.03833028,0.030466197,0.032158453,0.026207946,0.03495887,0.03253294,0.03370815,0.030059796,0.031647764,0.033966444,0.025543736,0.032113243,0.030116683,0.028788822,0.032551937,0.027236454,0.033830907
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-1-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-1-probabilities.csv
index 26319c06..070327b2 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-1-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-1-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
-0.02757978,0.033920135,0.0352644,0.026169574,0.027414698,0.027247427,0.0343263,0.03215879,0.035035186,0.030168401,0.02789809,0.03271094,0.035344504,0.034741975,0.032225955,0.032462742,0.028217483,0.031554103,0.03120801,0.035676204,0.030488938,0.029054616,0.03119181,0.035311166,0.032082345,0.028742258,0.030691229,0.030270696,0.030084299,0.031229528,0.028773708,0.0307547
+0.027698183,0.033823807,0.034816064,0.026363835,0.027390337,0.027692722,0.034728833,0.032594025,0.035248015,0.030304383,0.028146483,0.032520406,0.03490757,0.034511,0.032492466,0.032459043,0.02834659,0.031357385,0.030995904,0.035713017,0.030322125,0.02894736,0.03116146,0.035290796,0.031962983,0.028700726,0.03028577,0.030421866,0.02987411,0.030955058,0.029263496,0.030704184
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-2-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-2-probabilities.csv
index 7d83e06f..bf993a46 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-2-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-2-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
-0.028124575,0.0335595,0.03533975,0.026265949,0.027753796,0.026575672,0.034325916,0.031532172,0.035744086,0.030333044,0.027808817,0.032351915,0.03524489,0.03358328,0.031544443,0.03379499,0.028572772,0.032153927,0.029876353,0.03592832,0.030343272,0.029379593,0.03108449,0.03495568,0.032665774,0.028189657,0.031518478,0.030699521,0.03058068,0.030920729,0.028924745,0.030323174
+0.028167062,0.03342513,0.03485314,0.026504058,0.027753608,0.026959134,0.034658585,0.032011054,0.035963666,0.030307233,0.027994586,0.032209687,0.034868486,0.033530336,0.03181037,0.03377882,0.028619844,0.031967558,0.029669885,0.036010943,0.030286336,0.029172627,0.030948382,0.03504276,0.032737065,0.028151205,0.03111686,0.03077792,0.030493367,0.030681334,0.029267205,0.030261736
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-3-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-3-probabilities.csv
index 8df89327..f890df65 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-3-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-3-probabilities.csv
@@ -1,3 +1,3 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
-0.033364978,0.027717972,0.028960431,0.033384144,0.035686236,0.036644775,0.030265447,0.03097249,0.028456474,0.028803514,0.03836739,0.030087445,0.026880313,0.030170398,0.03179534,0.03095478,0.032279294,0.02975664,0.03072051,0.029009227,0.034099314,0.034203216,0.030106768,0.028277446,0.030847004,0.031177657,0.030798245,0.029398808,0.030252706,0.03243212,0.030178383,0.033950552
-0.031411365,0.028973259,0.029356807,0.033285685,0.03303163,0.037264887,0.03013853,0.032125752,0.028276425,0.027654836,0.03685246,0.030987127,0.02893919,0.032679845,0.03380999,0.028317781,0.030953553,0.029187296,0.03395916,0.030118862,0.034222953,0.03262129,0.030719524,0.029564887,0.029466776,0.03244446,0.029180052,0.028733686,0.029483747,0.032481648,0.030052282,0.03370424
+0.033285804,0.027885621,0.029906657,0.03290553,0.03519474,0.036209192,0.030132603,0.03058534,0.028284524,0.028707925,0.037558433,0.030136596,0.02720982,0.030483145,0.03180755,0.030645888,0.03212058,0.029795013,0.031333443,0.029060345,0.033991117,0.03429255,0.030680299,0.028249962,0.030794498,0.031282235,0.030955788,0.029474463,0.030073477,0.032905553,0.0300959,0.03395547
+0.031514406,0.029269755,0.030331286,0.032764226,0.032748014,0.036778405,0.03013493,0.0316624,0.028190814,0.027845534,0.03610598,0.03098638,0.029100828,0.032684784,0.03400216,0.028160343,0.031075826,0.029238014,0.034561954,0.0301395,0.033811763,0.032833368,0.03126785,0.029421117,0.02912695,0.032569267,0.029265095,0.02882559,0.029150033,0.03253771,0.030217351,0.0336784
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-4-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-4-probabilities.csv
index b6a18d75..148d6f3f 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-4-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-4-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
-0.028551342,0.03210338,0.035672657,0.025975518,0.02906049,0.027219487,0.035366267,0.03155426,0.035793904,0.031109689,0.029165266,0.03171212,0.033253983,0.031564854,0.03059164,0.035533007,0.029423531,0.032027707,0.028013717,0.03530991,0.03020476,0.031142863,0.030849883,0.03390686,0.03265668,0.027294233,0.032751426,0.031144792,0.03009808,0.031548142,0.02822442,0.03117514
+0.028436597,0.031898256,0.035255283,0.026101403,0.028847404,0.027760234,0.03573282,0.03216549,0.03591226,0.031003581,0.029277334,0.031533156,0.032938212,0.031666856,0.03072312,0.035322357,0.029342141,0.03174728,0.027949044,0.035422385,0.03017283,0.030797563,0.03090701,0.034020215,0.03287591,0.027233437,0.0323437,0.03115694,0.029990267,0.03168556,0.028550087,0.03123127
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-5-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-5-probabilities.csv
index 195a72e2..4c90e099 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-5-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-5-probabilities.csv
@@ -1,3 +1,3 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
-0.030342435,0.030822715,0.02960897,0.03322693,0.03194602,0.03620199,0.029623693,0.032765668,0.02717059,0.028776936,0.032656,0.03146398,0.03122887,0.0328284,0.034712065,0.026961364,0.031048443,0.028905584,0.037097085,0.02989018,0.032596547,0.030409556,0.031958636,0.029892432,0.029117517,0.034479015,0.028881233,0.028851666,0.030325295,0.03226891,0.031931575,0.032009717
-0.029181851,0.032261044,0.031046845,0.027925285,0.032212984,0.033161297,0.032004636,0.028391425,0.034346633,0.02482522,0.037898514,0.030189529,0.032011207,0.033572078,0.030944554,0.03464665,0.030707927,0.033008866,0.031740926,0.035867557,0.033871092,0.03207441,0.029566554,0.03079876,0.03171675,0.027234511,0.029196348,0.02724821,0.028529298,0.03073862,0.028472185,0.034608275
+0.030664794,0.031082612,0.03030074,0.032872494,0.03189757,0.03554864,0.029711511,0.032308478,0.027148588,0.029142486,0.03235085,0.031445574,0.031172441,0.032628857,0.034794968,0.02697712,0.031333465,0.028972484,0.037340544,0.030013558,0.03230658,0.030605843,0.03222915,0.029832635,0.02857672,0.034617916,0.02889556,0.0289314,0.030068528,0.0319775,0.03214,0.03211042
+0.029191403,0.032407805,0.031857405,0.027433928,0.031545587,0.0324864,0.032123398,0.028324135,0.034217004,0.02482894,0.036872905,0.030475914,0.032451976,0.03375697,0.031752195,0.033943985,0.030451044,0.03274126,0.032530427,0.03606905,0.033844758,0.03184988,0.03023864,0.030798176,0.03193668,0.027185407,0.029141765,0.027430946,0.028412677,0.030810867,0.02847339,0.034415122
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-6-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-6-probabilities.csv
index eca04413..40eca832 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-6-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-6-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
-0.028420437,0.031632587,0.032485284,0.030121041,0.030508144,0.035512682,0.030315991,0.032107607,0.029705128,0.026934983,0.03430073,0.03131456,0.032074325,0.035281524,0.03450778,0.028124444,0.029300941,0.02960142,0.036127225,0.032294188,0.03354204,0.03190299,0.031748407,0.03104404,0.029012788,0.03174257,0.028774712,0.026694056,0.029650616,0.031935107,0.030128015,0.03315364
+0.028840212,0.031923126,0.03316305,0.029733282,0.030324597,0.035063315,0.030408965,0.031950425,0.029625356,0.027236255,0.033804726,0.03132866,0.03200365,0.03512926,0.034921013,0.028031223,0.0295095,0.029491551,0.036540907,0.03226653,0.033081695,0.032036718,0.03228033,0.030852877,0.028602235,0.031815387,0.028647292,0.026920438,0.029234089,0.03159718,0.030543774,0.033092327
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-7-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-7-probabilities.csv
index 0222db6e..d6b051a2 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-7-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-7-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
-0.03285409,0.028532468,0.03228689,0.029081294,0.03365369,0.030380722,0.032942653,0.029840373,0.033243008,0.032283887,0.034871463,0.029991638,0.02763245,0.028689934,0.02847528,0.037176516,0.031971972,0.03185924,0.024960924,0.031009752,0.031989325,0.035211597,0.02961846,0.030089783,0.033301707,0.02728624,0.034313694,0.031624418,0.031192362,0.031643562,0.029297316,0.032693367
+0.032431256,0.028374098,0.03241755,0.02907772,0.03322896,0.03083206,0.032768983,0.030121792,0.033098236,0.031856593,0.03449055,0.029965289,0.02779097,0.029204315,0.028356798,0.036816824,0.03156557,0.03171897,0.025158815,0.030962218,0.032085657,0.034966357,0.029915892,0.030164259,0.03391852,0.027265668,0.034304135,0.031615134,0.031207006,0.032374583,0.029263819,0.0326814
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-0-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-0-probabilities.csv
index 62c6fc4c..4b23b309 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-0-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-0-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,0,1,2,3,4,5,6,7,8,9
-0.07594775,0.07880462,0.086174354,0.085758224,0.07953248,0.076291695,0.09287521,0.08811465,0.087236874,0.0821172,0.088243,0.078903906
+0.07523441,0.07748859,0.08639577,0.085923284,0.0789727,0.07716564,0.09451911,0.08678308,0.08670555,0.08257854,0.08806588,0.08016739
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-1-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-1-probabilities.csv
index f1bb9c13..3307879f 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-1-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-1-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,0,1,2,3,4,5,6,7,8,9
-0.07301101,0.08338958,0.07649726,0.08804287,0.08133221,0.08367928,0.0823913,0.0810918,0.08472089,0.093449675,0.08985587,0.082538284
+0.073339716,0.08346172,0.07554303,0.08795957,0.081076026,0.08488209,0.08302772,0.08036742,0.0847984,0.093177184,0.08934849,0.08301852
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-2-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-2-probabilities.csv
index a0ae4686..7cc5a064 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-2-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-2-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,0,1,2,3,4,5,6,7,8,9
-0.07346334,0.08531202,0.0765368,0.089589685,0.08214274,0.08069753,0.0815366,0.08201226,0.08605881,0.09195612,0.09029723,0.08039689
+0.0735714,0.085165255,0.075638816,0.08946831,0.082018055,0.08179583,0.08238851,0.08111851,0.08600896,0.0920105,0.089723445,0.08109248
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-3-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-3-probabilities.csv
index f8be57ce..73f44533 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-3-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-3-probabilities.csv
@@ -1,3 +1,3 @@
 unknown,other,0,1,2,3,4,5,6,7,8,9
-0.08852895,0.07426861,0.09503356,0.07485142,0.078805745,0.088310786,0.09113893,0.0892912,0.08064015,0.07384279,0.07798186,0.08730608
-0.08633282,0.071523584,0.094729766,0.07199362,0.07617915,0.096285224,0.08990355,0.08491013,0.078594856,0.07725805,0.07704274,0.095246494
+0.08835912,0.07418081,0.095578186,0.07515316,0.07892947,0.08788645,0.09078124,0.0895524,0.08074507,0.07345364,0.07882017,0.08656019
+0.086663246,0.07226138,0.09419653,0.07254638,0.07658576,0.09616181,0.089182466,0.08551516,0.07912567,0.076377966,0.07799796,0.09338568
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-4-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-4-probabilities.csv
index c9df4b5f..f72b8d3f 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-4-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-4-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,0,1,2,3,4,5,6,7,8,9
-0.07311858,0.08504815,0.07854804,0.08984892,0.08204388,0.07796906,0.086452566,0.08488878,0.08722786,0.089391045,0.08900175,0.07646131
+0.073002174,0.084327646,0.077937834,0.089499675,0.08172626,0.07921972,0.087675005,0.0837502,0.086888224,0.08974763,0.08855341,0.077672325
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-5-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-5-probabilities.csv
index f0720ccc..5d07c161 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-5-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-5-probabilities.csv
@@ -1,3 +1,3 @@
 unknown,other,0,1,2,3,4,5,6,7,8,9
-0.0852462,0.07632019,0.09025085,0.07461921,0.07753635,0.098873265,0.085412286,0.08444434,0.08068381,0.07826116,0.074494846,0.09385747
-0.078119405,0.06821374,0.09058299,0.07674087,0.07665119,0.085385256,0.096274614,0.07863315,0.08402233,0.08093503,0.08783316,0.096608296
+0.0860558,0.07743681,0.08973225,0.07462213,0.077821694,0.098795794,0.08423508,0.08542021,0.08139216,0.07743476,0.07517884,0.09187443
+0.07830784,0.06836525,0.09037594,0.077492535,0.07662541,0.08604211,0.09636521,0.07893603,0.08409658,0.08042918,0.087497495,0.095466316
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-6-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-6-probabilities.csv
index 7b31642e..6cfcac89 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-6-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-6-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,0,1,2,3,4,5,6,7,8,9
-0.08263379,0.07205671,0.09162223,0.07311431,0.071510315,0.098791294,0.08745874,0.08465465,0.08085046,0.08158381,0.07782605,0.097897656
+0.08336393,0.07317236,0.090186596,0.07377336,0.07200975,0.09935644,0.086575784,0.08554148,0.08153822,0.08023641,0.07896855,0.09527699
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-7-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-7-probabilities.csv
index 5397606e..23bea96b 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-7-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-7-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,0,1,2,3,4,5,6,7,8,9
-0.082868464,0.083716795,0.08511493,0.08664434,0.08278853,0.07330947,0.08889157,0.08948531,0.08548041,0.08055529,0.087142356,0.07400249
+0.0819516,0.08247403,0.08569772,0.08688278,0.082577325,0.073386215,0.08999724,0.0884895,0.08485753,0.081263095,0.0872452,0.07517781

From 179fa4ceec02c54cbc13a0bcc961f0d834b7958a Mon Sep 17 00:00:00 2001
From: Leon Luithlen <leontimnaluithlen@gmail.com>
Date: Wed, 20 May 2026 16:43:32 +0200
Subject: [PATCH 05/15] add train-test-categorical-multitarget-eager

---
 ...in-test-categorical-multitarget-eager.yaml | 77 +++++++++++++++++++
 tests/integration-test-log.txt                |  2 +
 tests/integration/conftest.py                 | 14 ++++
 tests/integration/test_training.py            |  8 ++
 4 files changed, 101 insertions(+)
 create mode 100644 tests/configs/train-test-categorical-multitarget-eager.yaml

diff --git a/tests/configs/train-test-categorical-multitarget-eager.yaml b/tests/configs/train-test-categorical-multitarget-eager.yaml
new file mode 100644
index 00000000..65e93add
--- /dev/null
+++ b/tests/configs/train-test-categorical-multitarget-eager.yaml
@@ -0,0 +1,77 @@
+project_root: tests/project_folder
+model_name: model-categorical-multitarget-5-eager
+read_format: parquet
+metadata_config_path: configs/metadata_configs/test-data-categorical-multitarget-5.json
+
+input_columns: null
+target_columns: [itemId, supCat1, supReal3]
+target_column_types:
+  itemId: categorical
+  supCat1: categorical
+  supReal3: real
+seq_length: 8
+inference_batch_size: 10
+
+export_generative_model: true
+export_embedding_model: true
+
+model_spec:
+  initial_embedding_dim: 16
+  feature_embedding_dims:
+    itemId: 6
+    supCat1: 4
+    supReal2: 1
+    supReal3: 1
+    supCat4: 4
+  dim_model: 16
+  n_head: 2
+  dim_feedforward: 10
+  num_layers: 2
+  prediction_length: 1
+
+  activation_fn: gelu
+  normalization: rmsnorm
+  positional_encoding: rope
+  attention_type: gqa
+  norm_first: true
+  n_kv_heads: 1
+  rope_theta: 10000.0
+
+training_spec:
+  device: cpu
+  epochs: 3
+  save_interval_epochs: 1
+  batch_size: 10
+  learning_rate: 0.003
+  log_interval: 1
+  accumulation_steps: 2
+  dropout: 0.3
+  criterion:
+    itemId: CrossEntropyLoss
+    supCat1: CrossEntropyLoss
+    supReal3: MSELoss
+  loss_weights:
+    itemId: 1.0
+    supCat1: 0.5
+    supReal3: 0.5
+  optimizer:
+    name: AdamW
+  scheduler:
+    name: OneCycleLR
+    max_lr: 0.001
+    pct_start: 0.03
+    div_factor: 1000
+    final_div_factor: 1000
+    anneal_strategy: cos
+    total_steps: 100
+    three_phase: false
+  scheduler_step_on: 'batch'
+  continue_training: false
+  enforce_determinism: true
+  layer_autocast: true
+  layer_type_dtypes:
+    linear: bfloat16
+    decoder: bfloat16
+    embedding: float32
+    norm: float32
+  load_full_data_to_ram: true
diff --git a/tests/integration-test-log.txt b/tests/integration-test-log.txt
index 2b99a8c1..6a073dc0 100644
--- a/tests/integration-test-log.txt
+++ b/tests/integration-test-log.txt
@@ -24,6 +24,7 @@ sequifier train --config-path tests/configs/train-test-real.yaml --metadata-conf
 sequifier train --config-path tests/configs/train-test-categorical-inf-size-1.yaml
 sequifier train --config-path tests/configs/train-test-categorical-inf-size-3.yaml
 sequifier train --config-path tests/configs/train-test-categorical-multitarget.yaml
+sequifier train --config-path tests/configs/train-test-categorical-multitarget-eager.yaml
 sequifier train --config-path tests/configs/train-test-distributed.yaml
 sequifier train --config-path tests/configs/train-test-lazy.yaml
 sequifier infer --config-path tests/configs/infer-test-categorical.yaml --metadata-config-path configs/metadata_configs/test-data-categorical-1.json --model-path models/sequifier-model-categorical-1-best-3.onnx --data-path data/test-data-categorical-1-split2 --input-columns itemId
@@ -59,6 +60,7 @@ sequifier visualize-training model-categorical-50 --project-root tests/project_f
 sequifier visualize-training model-categorical-distributed --project-root tests/project_folder
 sequifier visualize-training model-categorical-lazy --project-root tests/project_folder
 sequifier visualize-training model-categorical-multitarget-5 --project-root tests/project_folder
+sequifier visualize-training model-categorical-multitarget-5-eager --project-root tests/project_folder
 sequifier visualize-training model-real-1 --project-root tests/project_folder
 sequifier visualize-training model-real-1-from-epoch-checkpoint --project-root tests/project_folder
 sequifier visualize-training model-real-3 --project-root tests/project_folder
diff --git a/tests/integration/conftest.py b/tests/integration/conftest.py
index 9b985835..bede70e2 100644
--- a/tests/integration/conftest.py
+++ b/tests/integration/conftest.py
@@ -111,6 +111,13 @@ def training_config_path_cat_multitarget():
     return os.path.join("tests", "configs", "train-test-categorical-multitarget.yaml")
 
 
+@pytest.fixture(scope="session")
+def training_config_path_cat_multitarget_eager():
+    return os.path.join(
+        "tests", "configs", "train-test-categorical-multitarget-eager.yaml"
+    )
+
+
 @pytest.fixture(scope="session")
 def training_config_path_real():
     return os.path.join("tests", "configs", "train-test-real.yaml")
@@ -254,6 +261,7 @@ def format_configs_locally(
     training_config_path_resume_mid_epoch,
     inference_config_path_cat,
     inference_config_path_cat_multitarget,
+    training_config_path_cat_multitarget_eager,
     inference_config_path_real,
     inference_config_path_real_autoregression,
     inference_config_path_categorical_autoregression,
@@ -283,6 +291,7 @@ def format_configs_locally(
             training_config_path_resume_mid_epoch,
             inference_config_path_cat,
             inference_config_path_cat_multitarget,
+            training_config_path_cat_multitarget_eager,
             inference_config_path_real,
             inference_config_path_real_autoregression,
             inference_config_path_categorical_autoregression,
@@ -421,6 +430,7 @@ def run_training(
     training_config_path_distributed,
     training_config_path_lazy,
     training_config_path_cat_multitarget,
+    training_config_path_cat_multitarget_eager,
 ):
     for model_number in [1, 3, 5, 50]:
         metadata_config_path_cat = os.path.join(
@@ -445,6 +455,10 @@ def run_training(
 
     run_and_log(f"sequifier train --config-path {training_config_path_cat_multitarget}")
 
+    run_and_log(
+        f"sequifier train --config-path {training_config_path_cat_multitarget_eager}"
+    )
+
     run_and_log(f"sequifier train --config-path {training_config_path_distributed}")
 
     run_and_log(f"sequifier train --config-path {training_config_path_lazy}")
diff --git a/tests/integration/test_training.py b/tests/integration/test_training.py
index b1d99a2a..e0eceab0 100644
--- a/tests/integration/test_training.py
+++ b/tests/integration/test_training.py
@@ -34,6 +34,10 @@ def test_checkpoint_files_exists(
                 for i in range(1, 4)
             ]
             + [f"model-categorical-multitarget-5-epoch-{i}.pt" for i in range(1, 4)]
+            + [
+                f"model-categorical-multitarget-5-eager-epoch-{i}.pt"
+                for i in range(1, 4)
+            ]
             + [f"model-categorical-distributed-epoch-{i}.pt" for i in range(1, 4)]
             + [f"model-categorical-lazy-epoch-{i}.pt" for i in range(1, 4)]
         )
@@ -85,6 +89,10 @@ def test_model_files_exists(run_training, run_training_from_checkpoint, project_
                 "sequifier-model-categorical-multitarget-5-last-3.onnx",
                 "sequifier-model-categorical-multitarget-5-best-embedding-3.onnx",
                 "sequifier-model-categorical-multitarget-5-last-embedding-3.onnx",
+                "sequifier-model-categorical-multitarget-5-eager-best-3.onnx",
+                "sequifier-model-categorical-multitarget-5-eager-last-3.onnx",
+                "sequifier-model-categorical-multitarget-5-eager-best-embedding-3.onnx",
+                "sequifier-model-categorical-multitarget-5-eager-last-embedding-3.onnx",
                 "sequifier-model-real-1-best-3-autoregression.pt",
                 "sequifier-model-categorical-1-best-3-autoregression.onnx",
                 "sequifier-model-categorical-1-inf-size-best-3.onnx",

From 9a382d43202d8016c8d126fcd064611df9fd365a Mon Sep 17 00:00:00 2001
From: Leon Luithlen <leontimnaluithlen@gmail.com>
Date: Wed, 20 May 2026 17:02:13 +0200
Subject: [PATCH 06/15] rename io classes

---
 documentation/configs/preprocess.md           |  2 +-
 documentation/consolidated-docs.md            |  2 +-
 ...py => sequifier_dataset_from_folder_pt.py} |  2 +-
 ... sequifier_dataset_from_folder_pt_lazy.py} |  2 +-
 src/sequifier/preprocess.py                   |  4 ++--
 src/sequifier/train.py                        | 20 ++++++++--------
 ...test_sequifier_dataset_from_folder_lazy.py | 24 +++++++++++--------
 7 files changed, 30 insertions(+), 26 deletions(-)
 rename src/sequifier/io/{sequifier_dataset_from_folder.py => sequifier_dataset_from_folder_pt.py} (99%)
 rename src/sequifier/io/{sequifier_dataset_from_folder_lazy.py => sequifier_dataset_from_folder_pt_lazy.py} (99%)

diff --git a/documentation/configs/preprocess.md b/documentation/configs/preprocess.md
index 9ceb6565..395dcf67 100644
--- a/documentation/configs/preprocess.md
+++ b/documentation/configs/preprocess.md
@@ -63,7 +63,7 @@ The configuration is defined in a YAML file (e.g., `preprocess.yaml`). Below are
 ### 1\. `write_format`: `parquet` vs. `pt`
 
   * **Choose `parquet` (default):** If your dataset is small to medium (fits in RAM) and you want to inspect the preprocessed data easily using standard tools like Pandas or Polars. This produces one file per split (e.g., `data-split0.parquet`).
-  * **Choose `pt`:** If your dataset is massive (larger than RAM) or you intend to use **Distributed Training** (multi-GPU). This format saves data as thousands of small PyTorch tensor files. It allows the `SequifierDatasetFromFolderLazy` to load data on demand without clogging memory.
+  * **Choose `pt`:** If your dataset is massive (larger than RAM) or you intend to use **Distributed Training** (multi-GPU). This format saves data as thousands of small PyTorch tensor files. It allows the `SequifierDatasetFromFolderPtLazy` to load data on demand without clogging memory.
 
 ### 2\. `stride_by_split` configuration
 
diff --git a/documentation/consolidated-docs.md b/documentation/consolidated-docs.md
index 41c92864..b1882537 100644
--- a/documentation/consolidated-docs.md
+++ b/documentation/consolidated-docs.md
@@ -274,7 +274,7 @@ The configuration is defined in a YAML file (e.g., `preprocess.yaml`). Below are
 ### 1\. `write_format`: `parquet` vs. `pt`
 
   * **Choose `parquet` (default):** If your dataset is small to medium (fits in RAM) and you want to inspect the preprocessed data easily using standard tools like Pandas or Polars. This produces one file per split (e.g., `data-split0.parquet`).
-  * **Choose `pt`:** If your dataset is massive (larger than RAM) or you intend to use **Distributed Training** (multi-GPU). This format saves data as thousands of small PyTorch tensor files. It allows the `SequifierDatasetFromFolderLazy` to load data on demand without clogging memory.
+  * **Choose `pt`:** If your dataset is massive (larger than RAM) or you intend to use **Distributed Training** (multi-GPU). This format saves data as thousands of small PyTorch tensor files. It allows the `SequifierDatasetFromFolderPtLazy` to load data on demand without clogging memory.
 
 ### 2\. `stride_by_split` configuration
 
diff --git a/src/sequifier/io/sequifier_dataset_from_folder.py b/src/sequifier/io/sequifier_dataset_from_folder_pt.py
similarity index 99%
rename from src/sequifier/io/sequifier_dataset_from_folder.py
rename to src/sequifier/io/sequifier_dataset_from_folder_pt.py
index e1974fba..1ad738e8 100644
--- a/src/sequifier/io/sequifier_dataset_from_folder.py
+++ b/src/sequifier/io/sequifier_dataset_from_folder_pt.py
@@ -12,7 +12,7 @@
 from sequifier.helpers import normalize_path
 
 
-class SequifierDatasetFromFolder(IterableDataset):
+class SequifierDatasetFromFolderPt(IterableDataset):
     """
     An efficient PyTorch IterableDataset that pre-loads all data into RAM.
 
diff --git a/src/sequifier/io/sequifier_dataset_from_folder_lazy.py b/src/sequifier/io/sequifier_dataset_from_folder_pt_lazy.py
similarity index 99%
rename from src/sequifier/io/sequifier_dataset_from_folder_lazy.py
rename to src/sequifier/io/sequifier_dataset_from_folder_pt_lazy.py
index 1b4b30d5..0b02ce16 100644
--- a/src/sequifier/io/sequifier_dataset_from_folder_lazy.py
+++ b/src/sequifier/io/sequifier_dataset_from_folder_pt_lazy.py
@@ -13,7 +13,7 @@
 from sequifier.helpers import normalize_path
 
 
-class SequifierDatasetFromFolderLazy(IterableDataset):
+class SequifierDatasetFromFolderPtLazy(IterableDataset):
     """
     An efficient, memory-safe PyTorch IterableDataset for out-of-core training.
 
diff --git a/src/sequifier/preprocess.py b/src/sequifier/preprocess.py
index 5d7755cd..c7d4380f 100644
--- a/src/sequifier/preprocess.py
+++ b/src/sequifier/preprocess.py
@@ -712,7 +712,7 @@ def _cleanup(self, write_format: str) -> None:
         `target_dir` into their final split-specific subfolders (e.g.,
         'data_name_root-split0/'). It also generates a 'metadata.json' file
         in each of these subfolders, which is required by
-        `SequifierDatasetFromFolder`.
+        `SequifierDatasetFromFolderPt`.
 
         Finally, it removes the temporary `target_dir` if it's empty or
         if `target_dir` is "temp" (implying `merge_output` was True).
@@ -813,7 +813,7 @@ def _create_metadata_for_folder(self, folder_path: str, write_format: str) -> No
                 samples (sequences), and writes a `metadata.json` file in that
                 same folder. This JSON file contains the total sample count and a
                 list of all batch files with their respective sample counts, which
-        s        is required by the `SequifierDatasetFromFolder` data loader.
+        s        is required by the `SequifierDatasetFromFolderPt` data loader.
 
                 Args:
                     folder_path: The path to the directory containing the .pt batch files
diff --git a/src/sequifier/train.py b/src/sequifier/train.py
index e7455cf8..15ce542e 100644
--- a/src/sequifier/train.py
+++ b/src/sequifier/train.py
@@ -57,18 +57,18 @@
 from sequifier.io.sequifier_dataset_from_file import (  # noqa: E402
     SequifierDatasetFromFile,
 )
-from sequifier.io.sequifier_dataset_from_folder import (  # noqa: E402
-    SequifierDatasetFromFolder,
-)
-from sequifier.io.sequifier_dataset_from_folder_lazy import (  # noqa: E402
-    SequifierDatasetFromFolderLazy,
-)
 from sequifier.io.sequifier_dataset_from_folder_parquet import (  # noqa: E402
     SequifierDatasetFromFolderParquet,
 )
 from sequifier.io.sequifier_dataset_from_folder_parquet_lazy import (  # noqa: E402
     SequifierDatasetFromFolderParquetLazy,
 )
+from sequifier.io.sequifier_dataset_from_folder_pt import (  # noqa: E402
+    SequifierDatasetFromFolderPt,
+)
+from sequifier.io.sequifier_dataset_from_folder_pt_lazy import (  # noqa: E402
+    SequifierDatasetFromFolderPtLazy,
+)
 from sequifier.model.layers import RMSNorm, SequifierEncoderLayer  # noqa: E402
 from sequifier.optimizers.optimizers import get_optimizer_class  # noqa: E402
 
@@ -152,17 +152,17 @@ def train_worker(
     if from_folder:
         if config.read_format == "pt":
             if config.training_spec.load_full_data_to_ram:
-                train_dataset = SequifierDatasetFromFolder(
+                train_dataset = SequifierDatasetFromFolderPt(
                     config.training_data_path, config
                 )
-                valid_dataset = SequifierDatasetFromFolder(
+                valid_dataset = SequifierDatasetFromFolderPt(
                     config.validation_data_path, config
                 )
             else:
-                train_dataset = SequifierDatasetFromFolderLazy(
+                train_dataset = SequifierDatasetFromFolderPtLazy(
                     config.training_data_path, config
                 )
-                valid_dataset = SequifierDatasetFromFolderLazy(
+                valid_dataset = SequifierDatasetFromFolderPtLazy(
                     config.validation_data_path, config
                 )
         elif config.read_format == "parquet":
diff --git a/tests/unit/io/test_sequifier_dataset_from_folder_lazy.py b/tests/unit/io/test_sequifier_dataset_from_folder_lazy.py
index 95042f2f..b7fe9e4a 100644
--- a/tests/unit/io/test_sequifier_dataset_from_folder_lazy.py
+++ b/tests/unit/io/test_sequifier_dataset_from_folder_lazy.py
@@ -4,8 +4,8 @@
 import pytest
 import torch
 
-from sequifier.io.sequifier_dataset_from_folder_lazy import (
-    SequifierDatasetFromFolderLazy,
+from sequifier.io.sequifier_dataset_from_folder_pt_lazy import (
+    SequifierDatasetFromFolderPtLazy,
 )
 
 
@@ -75,7 +75,7 @@ def side_effect(path, map_location, weights_only):
 
 def test_initialization(mock_config, dataset_path):
     """Tests that metadata is read correctly and __len__ calculates batches."""
-    dataset = SequifierDatasetFromFolderLazy(dataset_path, mock_config)
+    dataset = SequifierDatasetFromFolderPtLazy(dataset_path, mock_config)
 
     # 40 total samples / batch size of 5 = 8 batches
     assert len(dataset) == 8
@@ -85,7 +85,7 @@ def test_initialization(mock_config, dataset_path):
 
 def test_iteration_yields_correct_batches(mock_config, dataset_path, mock_torch_load):
     """Tests that the dataset iterates over files and yields correct tensor slices."""
-    dataset = SequifierDatasetFromFolderLazy(dataset_path, mock_config, shuffle=False)
+    dataset = SequifierDatasetFromFolderPtLazy(dataset_path, mock_config, shuffle=False)
 
     # Consume the generator
     batches = list(dataset)
@@ -114,7 +114,7 @@ def test_distributed_sharding(
     mock_rank, mock_ws, mock_init, mock_config, dataset_path, mock_torch_load
 ):
     """Tests that the dataset correctly shards files across distributed GPUs."""
-    dataset = SequifierDatasetFromFolderLazy(dataset_path, mock_config, shuffle=False)
+    dataset = SequifierDatasetFromFolderPtLazy(dataset_path, mock_config, shuffle=False)
 
     # World size = 2, Total files = 4
     # Rank 0 gets file index 0 and 2 (file1.pt, file3.pt) -> Total 20 samples
@@ -133,7 +133,7 @@ def test_distributed_sharding(
     assert not any("file2.pt" in f for f in loaded_files)
 
 
-@patch("sequifier.io.sequifier_dataset_from_folder_lazy.get_worker_info")
+@patch("sequifier.io.sequifier_dataset_from_folder_pt_lazy.get_worker_info")
 def test_dataloader_worker_sharding_continuous_boundaries(
     mock_worker_info, mock_config, dataset_path, mock_torch_load
 ):
@@ -147,7 +147,7 @@ def test_dataloader_worker_sharding_continuous_boundaries(
 
     mock_config.training_spec.num_workers = 2
 
-    dataset = SequifierDatasetFromFolderLazy(dataset_path, mock_config, shuffle=False)
+    dataset = SequifierDatasetFromFolderPtLazy(dataset_path, mock_config, shuffle=False)
 
     # Consume the generator for THIS specific worker
     batches = list(dataset)
@@ -194,7 +194,7 @@ def test_exact_strategy_uneven_files_exception(
 
     # The dataset initialization calls _get_target_samples(), which should raise the Exception
     with pytest.raises(Exception) as exc_info:
-        SequifierDatasetFromFolderLazy(str(data_dir), mock_config)
+        SequifierDatasetFromFolderPtLazy(str(data_dir), mock_config)
 
     error_msg = str(exc_info.value)
 
@@ -229,7 +229,9 @@ def test_oversampling_strategy(
     mock_config.training_spec.batch_size = 5
     mock_config.training_spec.num_workers = 0
 
-    dataset = SequifierDatasetFromFolderLazy(str(data_dir), mock_config, shuffle=False)
+    dataset = SequifierDatasetFromFolderPtLazy(
+        str(data_dir), mock_config, shuffle=False
+    )
 
     # Max samples across ranks is 15. Rank 1 must pad its 10 samples up to 15.
     assert dataset.target_samples == 15
@@ -273,7 +275,9 @@ def test_undersampling_strategy(
     mock_config.training_spec.batch_size = 5
     mock_config.training_spec.num_workers = 0
 
-    dataset = SequifierDatasetFromFolderLazy(str(data_dir), mock_config, shuffle=False)
+    dataset = SequifierDatasetFromFolderPtLazy(
+        str(data_dir), mock_config, shuffle=False
+    )
 
     # Min samples across ranks is 10. Rank 0 must truncate its 15 samples down to 10.
     assert dataset.target_samples == 10

From 9a1b777bfcb0a7683c59d18d9bcffb4f305482fe Mon Sep 17 00:00:00 2001
From: Leon Luithlen <leontimnaluithlen@gmail.com>
Date: Wed, 20 May 2026 17:30:06 +0200
Subject: [PATCH 07/15] Update docs

---
 .../configs/hyperparameter-search.md          |  4 +-
 documentation/configs/infer.md                | 16 +++----
 documentation/configs/preprocess.md           | 10 +++--
 documentation/configs/train.md                |  8 ++--
 documentation/consolidated-docs.md            | 44 +++++++++++--------
 documentation/training/multi-gpu-training.md  |  6 ++-
 6 files changed, 50 insertions(+), 38 deletions(-)

diff --git a/documentation/configs/hyperparameter-search.md b/documentation/configs/hyperparameter-search.md
index 28029c5f..5e121295 100644
--- a/documentation/configs/hyperparameter-search.md
+++ b/documentation/configs/hyperparameter-search.md
@@ -105,8 +105,8 @@ Most fields here are lists for sampling, but some are scalar values fixed for al
 | `backend` | str | No | `nccl` | The distributed training backend to use (e.g., `nccl` for GPUs). Only relevant if `distributed: true`. |
 | `device_max_concat_length` | `int` | No | `12` | Controls recursive tensor concatenation to prevent CUDA kernel limits. |
 | `max_ram_gb` | `int` or `float`| No | `16` | RAM limit (GB) for the cache when using lazy loading. |
-| `load_full_data_to_ram` | `bool` | No | `true` | If `false`, uses lazy loading (requires `read_format: pt`). |
-| `distributed` | `bool` | No | `false`| Enable multi-GPU training (DDP or FSDP). Requires `read_format: pt`. |
+| `load_full_data_to_ram` | `bool` | No | `true` | If `false`, uses lazy loading (requires `read_format: pt` or `read_format: parquet`). |
+| `distributed` | `bool` | No | `false`| Enable multi-GPU training (DDP or FSDP). Requires `read_format: pt` or `read_format: parquet`. |
 | `layer_type_dtypes` | `dict` | No | `null` | Map of layer types to dtypes (e.g., `{'linear': 'bfloat16'}`). |
 | `layer_autocast` | `bool` | No | `true` | Enable `torch.autocast`. |
 | `sampling_strategy` | `str` | No | `exact` | How to address input file imbalance for multi-GPU training. |
diff --git a/documentation/configs/infer.md b/documentation/configs/infer.md
index 8d820072..fe3fd008 100644
--- a/documentation/configs/infer.md
+++ b/documentation/configs/infer.md
@@ -17,7 +17,7 @@ The configuration is defined in a YAML file (e.g., `infer.yaml`).
 | Field | Type | Mandatory | Default | Description |
 | :--- | :--- | :--- | :--- | :--- |
 | `project_root` | `str` | **Yes** | - | The root directory of your Sequifier project. Usually `.` |
-| `data_path` | `str` | **Yes** | - | Path to the input data file (csv/parquet) or folder (if `read_format: pt`). |
+| `data_path` | `str` | **Yes** | - | Path to the input data file (`csv` or `parquet`) or folder (`pt` or `parquet`). |
 | `model_path` | `str` or `list[str]` | **Yes** | - | Path to a specific model file, or a list of paths to process sequentially. (e.g., `models/sequifier-[NAME]-best-[EPOCH].pt`). |
 | `training_config_path`| `str` | No | `configs/train.yaml`| Path to the config used to train the model. Required to reconstruct the model architecture. |
 | `metadata_config_path`| `str` | **Yes** | - | Path to the JSON metadata file generated during preprocessing. Used for ID mapping and normalization. |
@@ -56,11 +56,11 @@ These fields tell the inference engine which columns to extract from the new dat
 | Field | Type | Mandatory | Default | Description |
 | :--- | :--- | :--- | :--- | :--- |
 | `device` | `str` | **Yes** | - | `cuda`, `cpu`, or `mps`. |
-| `distributed` | `bool` | No | `false`| Enable multi-GPU inference. Requires `read_format: pt`. |
+| `distributed` | `bool` | No | `false`| Enable multi-GPU inference. Requires `read_format: pt` or `read_format: parquet`. |
 | `world_size` | `int` | No | `1` | Number of GPUs/processes for distributed inference. |
 | `num_workers` | `int` | No | `0` | Number of subprocesses for data loading. |
 | `enforce_determinism` | `bool` | No | `false` | Forces PyTorch to use deterministic algorithms. |
-| `load_full_data_to_ram`| `bool` | No | `true` | If `false`, uses lazy loading (requires `read_format: pt`). |
+| `load_full_data_to_ram`| `bool` | No | `true` | If `false`, uses lazy loading (requires `read_format: pt` or `read_format: parquet`). |
 
 -----
 
@@ -89,7 +89,7 @@ Standard inference predicts the next step ($t+1$) based on history ($t-n \dots t
 
 ### 4\. Input Format (`read_format`)
 
-  * **`parquet` / `csv`:** Best for standard inference on new data files. The inferer will filter the data to `input_columns` automatically.
+  * **`parquet` / `csv`:** Best for standard inference on new data files. The inferer will filter the data to `input_columns` automatically. `parquet` is compatible with `distributed: true`
   * **`pt` (PyTorch Tensors):** Required for **Distributed Inference** or **Lazy Loading**. If your inference dataset is massive (terabytes), preprocess it into `.pt` chunks first, then run inference with `read_format: pt` and `distributed: true`.
 
 -----
@@ -116,12 +116,12 @@ Results are saved in the `outputs/` folder within your project root.
 
 ### Directory Output Mode (Sharded Inference)
 
-When using PyTorch tensors (`read_format: pt`), sequifier creates a directory containing multiple sharded outputs.
+When using a folder of files as input, sequifier creates a directory containing multiple sharded outputs.
 
 **File Structure**
-* **`pt` inputs:** `outputs/predictions/[MODEL_NAME]-predictions/[MODEL_NAME]-[CHUNK_ID]-predictions.[format]` *(Directory of files)*
-* **`pt` inputs:** `outputs/probabilities/[MODEL_NAME]-[TARGET_COLUMN]-probabilities/[MODEL_NAME]-[CHUNK_ID]-probabilities.[format]` *(Directory of files)*
-* **`pt` inputs:** `outputs/embeddings/[MODEL_NAME]-embeddings/[MODEL_NAME]-[CHUNK_ID]-embeddings.[format]` *(Directory of files)*
+* **folder inputs:** `outputs/predictions/[MODEL_NAME]-predictions/[MODEL_NAME]-[CHUNK_ID]-predictions.[format]` *(Directory of files)*
+* **folder inputs:** `outputs/probabilities/[MODEL_NAME]-[TARGET_COLUMN]-probabilities/[MODEL_NAME]-[CHUNK_ID]-probabilities.[format]` *(Directory of files)*
+* **folder inputs:** `outputs/embeddings/[MODEL_NAME]-embeddings/[MODEL_NAME]-[CHUNK_ID]-embeddings.[format]` *(Directory of files)*
 
 
 **Pipeline Note:** If you switch to `.pt` inputs, ensure your downstream scripts are configured to read from a directory of files rather than a single file. This behavior applies to predictions, probabilities, and embeddings.
diff --git a/documentation/configs/preprocess.md b/documentation/configs/preprocess.md
index 395dcf67..524b0313 100644
--- a/documentation/configs/preprocess.md
+++ b/documentation/configs/preprocess.md
@@ -26,8 +26,10 @@ The configuration is defined in a YAML file (e.g., `preprocess.yaml`). Below are
 
 > **Important Constraint on `write_format`:**
 >
->   * If `write_format` is **`pt`** (PyTorch tensors), `merge_output` must be **`false`**. This sharded format is **required** for distributed training on large datasets.
->   * If `write_format` is **`csv`** or **`parquet`**, `merge_output` must be **`true`**.
+>   * If `write_format` is **`pt`** (PyTorch tensors), `merge_output` must be **`false`**.
+>   * If `write_format` is **`parquet`**, `merge_output` can be **`false`** or **`true`**.
+>   * If `write_format` is **`csv`**, `merge_output` must be **`true`**.
+> For distributed training, `merge_output` must be set to **`false`**.
 
 ### 2\. Column Selection & Filtering
 
@@ -62,8 +64,8 @@ The configuration is defined in a YAML file (e.g., `preprocess.yaml`). Below are
 
 ### 1\. `write_format`: `parquet` vs. `pt`
 
-  * **Choose `parquet` (default):** If your dataset is small to medium (fits in RAM) and you want to inspect the preprocessed data easily using standard tools like Pandas or Polars. This produces one file per split (e.g., `data-split0.parquet`).
-  * **Choose `pt`:** If your dataset is massive (larger than RAM) or you intend to use **Distributed Training** (multi-GPU). This format saves data as thousands of small PyTorch tensor files. It allows the `SequifierDatasetFromFolderPtLazy` to load data on demand without clogging memory.
+  * **Choose `parquet` (default):** Unless you have a specific reason, use `parquet`.
+  * **Choose `pt`:** Use `pt` data loading if speed and CPU overhead are your primary bottlenecks.
 
 ### 2\. `stride_by_split` configuration
 
diff --git a/documentation/configs/train.md b/documentation/configs/train.md
index 7153d6d2..e9f7438a 100644
--- a/documentation/configs/train.md
+++ b/documentation/configs/train.md
@@ -84,8 +84,8 @@ These fields determine the size and complexity of the Transformer.
 | `backend` | `str` | No | `nccl` | The distributed training backend to use (e.g., `nccl` for GPUs, `gloo` for CPUs). Only relevant if `distributed: true`. |
 | `device_max_concat_length`| `int` | No | `12` | Controls recursive tensor concatenation to prevent CUDA kernel limits on specific hardware. Lower this if you encounter "CUDA error: too many resources requested for launch". |
 | `continue_training` | `bool` | No | `true` | Load model weights and optimizer state from laste checkpoint and continue training |
-| `distributed` | `bool` | No | `false`| Enable multi-GPU training (DDP). Requires `read_format: pt`. |
-| `load_full_data_to_ram`| `bool` | No | `true` | If `false`, uses lazy loading (requires `read_format: pt`). |
+| `distributed` | `bool` | No | `false`| Enable multi-GPU training (DDP). Requires `read_format: pt` or `read_format: parquet`. |
+| `load_full_data_to_ram`| `bool` | No | `true` | If `false`, uses lazy loading (requires `read_format: pt` or `read_format: parquet`). |
 | `layer_type_dtypes` | `dict` | No | `null` | Map of layer types (`linear`, `embedding`, `norm`, `decoder`) to dtypes (`float32`, `float16`, `bfloat16`, `float8_e4m3fn`, `float8_e5m2`). Used for mixed-precision/quantization. |
 | `layer_autocast` | `bool` | No | `true` | If `true`, enables `torch.autocast` for automatic mixed precision training. |
 | `sampling_strategy` | `str` | No | `exact` | How to address input file imbalance: `exact` requires exact divisibility of n_files by the number of GPUs (`world_size`), alternatively `oversampling` and `undersampling` equalise the number of samples seen
@@ -118,7 +118,7 @@ These fields determine the size and complexity of the Transformer.
       * *Pros*: Fastest training speed.
       * *Cons*: Limited by physical RAM. If the dataset is 64GB and you have 32GB RAM, this will crash.
   * **`false` (Lazy Loading):** Loads individual files on-demand during training.
-      * *Requirements:* `read_format` must be `pt`.
+      * *Requirements:* `read_format` must be `parquet` or `pt`.
       * *Mechanism:* Uses an `IterableDataset` with cross-file buffering to stream pre-processed chunked files sequentially, automatically calculating exact sample boundaries across GPU ranks and workers.
       * *Pros:* Can train on datasets much larger than RAM, safely supporting DDP/FSDP synchronization.
       * *Cons:* Slight I/O overhead depending on disk speed. Increase `num_workers` to mitigate this.
@@ -139,7 +139,7 @@ These fields determine the size and complexity of the Transformer.
 If you have multiple GPUs:
 
 1.  Set `distributed: true` in `training_spec`.
-2.  **Crucial:** You must have run `preprocess` with `write_format: pt` and `merge_output: false`.
+2.  **Crucial:** You must have run `preprocess` with `merge_output: false`.
 3.  Set `world_size` to the number of GPUs.
 4.  Set `data_parallelism` to `DDP` for `DistributedDataParallel`training or `FSDP` for `FullyShardedDataParallel` training
 5.  Set `torch_compile` to `inner` when training with `FSDP` and to `outer` when training with `DDP`
diff --git a/documentation/consolidated-docs.md b/documentation/consolidated-docs.md
index b1882537..f163dcaa 100644
--- a/documentation/consolidated-docs.md
+++ b/documentation/consolidated-docs.md
@@ -237,8 +237,10 @@ The configuration is defined in a YAML file (e.g., `preprocess.yaml`). Below are
 
 > **Important Constraint on `write_format`:**
 >
->   * If `write_format` is **`pt`** (PyTorch tensors), `merge_output` must be **`false`**. This sharded format is **required** for distributed training on large datasets.
->   * If `write_format` is **`csv`** or **`parquet`**, `merge_output` must be **`true`**.
+>   * If `write_format` is **`pt`** (PyTorch tensors), `merge_output` must be **`false`**.
+>   * If `write_format` is **`parquet`**, `merge_output` can be **`false`** or **`true`**.
+>   * If `write_format` is **`csv`**, `merge_output` must be **`true`**.
+> For distributed training, `merge_output` must be set to **`false`**.
 
 ### 2\. Column Selection & Filtering
 
@@ -273,8 +275,8 @@ The configuration is defined in a YAML file (e.g., `preprocess.yaml`). Below are
 
 ### 1\. `write_format`: `parquet` vs. `pt`
 
-  * **Choose `parquet` (default):** If your dataset is small to medium (fits in RAM) and you want to inspect the preprocessed data easily using standard tools like Pandas or Polars. This produces one file per split (e.g., `data-split0.parquet`).
-  * **Choose `pt`:** If your dataset is massive (larger than RAM) or you intend to use **Distributed Training** (multi-GPU). This format saves data as thousands of small PyTorch tensor files. It allows the `SequifierDatasetFromFolderPtLazy` to load data on demand without clogging memory.
+  * **Choose `parquet` (default):** Unless you have a specific reason, use `parquet`.
+  * **Choose `pt`:** Use `pt` data loading if speed and CPU overhead are your primary bottlenecks.
 
 ### 2\. `stride_by_split` configuration
 
@@ -432,8 +434,8 @@ These fields determine the size and complexity of the Transformer.
 | `backend` | `str` | No | `nccl` | The distributed training backend to use (e.g., `nccl` for GPUs, `gloo` for CPUs). Only relevant if `distributed: true`. |
 | `device_max_concat_length`| `int` | No | `12` | Controls recursive tensor concatenation to prevent CUDA kernel limits on specific hardware. Lower this if you encounter "CUDA error: too many resources requested for launch". |
 | `continue_training` | `bool` | No | `true` | Load model weights and optimizer state from laste checkpoint and continue training |
-| `distributed` | `bool` | No | `false`| Enable multi-GPU training (DDP). Requires `read_format: pt`. |
-| `load_full_data_to_ram`| `bool` | No | `true` | If `false`, uses lazy loading (requires `read_format: pt`). |
+| `distributed` | `bool` | No | `false`| Enable multi-GPU training (DDP). Requires `read_format: pt` or `read_format: parquet`. |
+| `load_full_data_to_ram`| `bool` | No | `true` | If `false`, uses lazy loading (requires `read_format: pt` or `read_format: parquet`). |
 | `layer_type_dtypes` | `dict` | No | `null` | Map of layer types (`linear`, `embedding`, `norm`, `decoder`) to dtypes (`float32`, `float16`, `bfloat16`, `float8_e4m3fn`, `float8_e5m2`). Used for mixed-precision/quantization. |
 | `layer_autocast` | `bool` | No | `true` | If `true`, enables `torch.autocast` for automatic mixed precision training. |
 | `sampling_strategy` | `str` | No | `exact` | How to address input file imbalance: `exact` requires exact divisibility of n_files by the number of GPUs (`world_size`), alternatively `oversampling` and `undersampling` equalise the number of samples seen
@@ -466,7 +468,7 @@ These fields determine the size and complexity of the Transformer.
       * *Pros*: Fastest training speed.
       * *Cons*: Limited by physical RAM. If the dataset is 64GB and you have 32GB RAM, this will crash.
   * **`false` (Lazy Loading):** Loads individual files on-demand during training.
-      * *Requirements:* `read_format` must be `pt`.
+      * *Requirements:* `read_format` must be `parquet` or `pt`.
       * *Mechanism:* Uses an `IterableDataset` with cross-file buffering to stream pre-processed chunked files sequentially, automatically calculating exact sample boundaries across GPU ranks and workers.
       * *Pros:* Can train on datasets much larger than RAM, safely supporting DDP/FSDP synchronization.
       * *Cons:* Slight I/O overhead depending on disk speed. Increase `num_workers` to mitigate this.
@@ -487,7 +489,7 @@ These fields determine the size and complexity of the Transformer.
 If you have multiple GPUs:
 
 1.  Set `distributed: true` in `training_spec`.
-2.  **Crucial:** You must have run `preprocess` with `write_format: pt` and `merge_output: false`.
+2.  **Crucial:** You must have run `preprocess` with `merge_output: false`.
 3.  Set `world_size` to the number of GPUs.
 4.  Set `data_parallelism` to `DDP` for `DistributedDataParallel`training or `FSDP` for `FullyShardedDataParallel` training
 5.  Set `torch_compile` to `inner` when training with `FSDP` and to `outer` when training with `DDP`
@@ -580,7 +582,7 @@ The configuration is defined in a YAML file (e.g., `infer.yaml`).
 | Field | Type | Mandatory | Default | Description |
 | :--- | :--- | :--- | :--- | :--- |
 | `project_root` | `str` | **Yes** | - | The root directory of your Sequifier project. Usually `.` |
-| `data_path` | `str` | **Yes** | - | Path to the input data file (csv/parquet) or folder (if `read_format: pt`). |
+| `data_path` | `str` | **Yes** | - | Path to the input data file (`csv` or `parquet`) or folder (`pt` or `parquet`). |
 | `model_path` | `str` or `list[str]` | **Yes** | - | Path to a specific model file, or a list of paths to process sequentially. (e.g., `models/sequifier-[NAME]-best-[EPOCH].pt`). |
 | `training_config_path`| `str` | No | `configs/train.yaml`| Path to the config used to train the model. Required to reconstruct the model architecture. |
 | `metadata_config_path`| `str` | **Yes** | - | Path to the JSON metadata file generated during preprocessing. Used for ID mapping and normalization. |
@@ -619,11 +621,11 @@ These fields tell the inference engine which columns to extract from the new dat
 | Field | Type | Mandatory | Default | Description |
 | :--- | :--- | :--- | :--- | :--- |
 | `device` | `str` | **Yes** | - | `cuda`, `cpu`, or `mps`. |
-| `distributed` | `bool` | No | `false`| Enable multi-GPU inference. Requires `read_format: pt`. |
+| `distributed` | `bool` | No | `false`| Enable multi-GPU inference. Requires `read_format: pt` or `read_format: parquet`. |
 | `world_size` | `int` | No | `1` | Number of GPUs/processes for distributed inference. |
 | `num_workers` | `int` | No | `0` | Number of subprocesses for data loading. |
 | `enforce_determinism` | `bool` | No | `false` | Forces PyTorch to use deterministic algorithms. |
-| `load_full_data_to_ram`| `bool` | No | `true` | If `false`, uses lazy loading (requires `read_format: pt`). |
+| `load_full_data_to_ram`| `bool` | No | `true` | If `false`, uses lazy loading (requires `read_format: pt` or `read_format: parquet`). |
 
 -----
 
@@ -652,7 +654,7 @@ Standard inference predicts the next step ($t+1$) based on history ($t-n \dots t
 
 ### 4\. Input Format (`read_format`)
 
-  * **`parquet` / `csv`:** Best for standard inference on new data files. The inferer will filter the data to `input_columns` automatically.
+  * **`parquet` / `csv`:** Best for standard inference on new data files. The inferer will filter the data to `input_columns` automatically. `parquet` is compatible with `distributed: true`
   * **`pt` (PyTorch Tensors):** Required for **Distributed Inference** or **Lazy Loading**. If your inference dataset is massive (terabytes), preprocess it into `.pt` chunks first, then run inference with `read_format: pt` and `distributed: true`.
 
 -----
@@ -679,12 +681,12 @@ Results are saved in the `outputs/` folder within your project root.
 
 ### Directory Output Mode (Sharded Inference)
 
-When using PyTorch tensors (`read_format: pt`), sequifier creates a directory containing multiple sharded outputs.
+When using a folder of files as input, sequifier creates a directory containing multiple sharded outputs.
 
 **File Structure**
-* **`pt` inputs:** `outputs/predictions/[MODEL_NAME]-predictions/[MODEL_NAME]-[CHUNK_ID]-predictions.[format]` *(Directory of files)*
-* **`pt` inputs:** `outputs/probabilities/[MODEL_NAME]-[TARGET_COLUMN]-probabilities/[MODEL_NAME]-[CHUNK_ID]-probabilities.[format]` *(Directory of files)*
-* **`pt` inputs:** `outputs/embeddings/[MODEL_NAME]-embeddings/[MODEL_NAME]-[CHUNK_ID]-embeddings.[format]` *(Directory of files)*
+* **folder inputs:** `outputs/predictions/[MODEL_NAME]-predictions/[MODEL_NAME]-[CHUNK_ID]-predictions.[format]` *(Directory of files)*
+* **folder inputs:** `outputs/probabilities/[MODEL_NAME]-[TARGET_COLUMN]-probabilities/[MODEL_NAME]-[CHUNK_ID]-probabilities.[format]` *(Directory of files)*
+* **folder inputs:** `outputs/embeddings/[MODEL_NAME]-embeddings/[MODEL_NAME]-[CHUNK_ID]-embeddings.[format]` *(Directory of files)*
 
 
 **Pipeline Note:** If you switch to `.pt` inputs, ensure your downstream scripts are configured to read from a directory of files rather than a single file. This behavior applies to predictions, probabilities, and embeddings.
@@ -834,8 +836,8 @@ Most fields here are lists for sampling, but some are scalar values fixed for al
 | `backend` | str | No | `nccl` | The distributed training backend to use (e.g., `nccl` for GPUs). Only relevant if `distributed: true`. |
 | `device_max_concat_length` | `int` | No | `12` | Controls recursive tensor concatenation to prevent CUDA kernel limits. |
 | `max_ram_gb` | `int` or `float`| No | `16` | RAM limit (GB) for the cache when using lazy loading. |
-| `load_full_data_to_ram` | `bool` | No | `true` | If `false`, uses lazy loading (requires `read_format: pt`). |
-| `distributed` | `bool` | No | `false`| Enable multi-GPU training (DDP or FSDP). Requires `read_format: pt`. |
+| `load_full_data_to_ram` | `bool` | No | `true` | If `false`, uses lazy loading (requires `read_format: pt` or `read_format: parquet`). |
+| `distributed` | `bool` | No | `false`| Enable multi-GPU training (DDP or FSDP). Requires `read_format: pt` or `read_format: parquet`. |
 | `layer_type_dtypes` | `dict` | No | `null` | Map of layer types to dtypes (e.g., `{'linear': 'bfloat16'}`). |
 | `layer_autocast` | `bool` | No | `true` | Enable `torch.autocast`. |
 | `sampling_strategy` | `str` | No | `exact` | How to address input file imbalance for multi-GPU training. |
@@ -942,8 +944,12 @@ you also need to set
 ```yaml
 write_format: pt
 ```
+or
+```yaml
+write_format: parquet
+```
 
-*Note: Distributed training is not supported if your data is kept as a single `csv` or `parquet` file.*
+*Note: Distributed training is not supported if your data is kept as a single `csv` or `parquet` file. You must use merge_output: false to generate a folder of sharded files.*
 
 ## 2. Configuration: `train.yaml`
 
diff --git a/documentation/training/multi-gpu-training.md b/documentation/training/multi-gpu-training.md
index a8a7078e..d5a75307 100644
--- a/documentation/training/multi-gpu-training.md
+++ b/documentation/training/multi-gpu-training.md
@@ -17,8 +17,12 @@ you also need to set
 ```yaml
 write_format: pt
 ```
+or
+```yaml
+write_format: parquet
+```
 
-*Note: Distributed training is not supported if your data is kept as a single `csv` or `parquet` file.*
+*Note: Distributed training is not supported if your data is kept as a single `csv` or `parquet` file. You must use merge_output: false to generate a folder of sharded files.*
 
 ## 2. Configuration: `train.yaml`
 

From 8b3baa499aba12957ffef00e3d87f53d0eaf221f Mon Sep 17 00:00:00 2001
From: Leon Luithlen <leontimnaluithlen@gmail.com>
Date: Wed, 20 May 2026 17:52:24 +0200
Subject: [PATCH 08/15] Refactor pt loading classes

---
 src/sequifier/infer.py                        |  19 +-
 ...uifier_dataset_from_folder_parquet_lazy.py | 153 ++++++++++---
 src/sequifier/preprocess.py                   |  26 +--
 ...uifier_dataset_from_folder_parquet_lazy.py | 214 ++++++++++++++++++
 ..._sequifier_dataset_from_folder_pt_lazy.py} |   0
 5 files changed, 370 insertions(+), 42 deletions(-)
 create mode 100644 tests/unit/io/test_sequifier_dataset_from_folder_parquet_lazy.py
 rename tests/unit/io/{test_sequifier_dataset_from_folder_lazy.py => test_sequifier_dataset_from_folder_pt_lazy.py} (100%)

diff --git a/src/sequifier/infer.py b/src/sequifier/infer.py
index 4e2fa909..0d7cd00f 100644
--- a/src/sequifier/infer.py
+++ b/src/sequifier/infer.py
@@ -115,7 +115,24 @@ def load_pt_dataset(data_path: str, start_pct: float, end_pct: float) -> Iterato
 def load_parquet_folder_dataset(
     data_path: str, start_pct: float, end_pct: float
 ) -> Iterator[Any]:
-    """Lazily loads and yields data from long-format .parquet chunk files in a directory."""
+    """Lazily loads and yields data from long-format .parquet chunk files in a directory.
+
+    This function scans a directory for `.parquet` files, sorts them, and then
+    yields the contents of a specific slice of those files defined by a
+    start and end percentage. This allows for processing large datasets
+    in chunks without loading everything into memory.
+
+    Args:
+        data_path: The path to the folder containing the `.parquet` files.
+        start_pct: The starting percentage (0.0 to 100.0) of the file list
+            to begin loading from.
+        end_pct: The ending percentage (0.0 to 100.0) of the file list
+            to stop loading at.
+
+    Yields:
+        Iterator: An iterator where each item is a Polars DataFrame loaded from a
+        single `.parquet` file.
+    """
     parquet_files = sorted(Path(data_path).glob("*.parquet"))
 
     total = len(parquet_files)
diff --git a/src/sequifier/io/sequifier_dataset_from_folder_parquet_lazy.py b/src/sequifier/io/sequifier_dataset_from_folder_parquet_lazy.py
index b1393302..094e897d 100644
--- a/src/sequifier/io/sequifier_dataset_from_folder_parquet_lazy.py
+++ b/src/sequifier/io/sequifier_dataset_from_folder_parquet_lazy.py
@@ -3,9 +3,11 @@
 import os
 from typing import Dict, Iterator, Tuple
 
+import numpy as np
 import polars as pl
 import torch
 import torch.distributed as dist
+from loguru import logger
 from torch.utils.data import IterableDataset, get_worker_info
 
 from sequifier.config.train_config import TrainModel
@@ -14,8 +16,27 @@
 
 class SequifierDatasetFromFolderParquetLazy(IterableDataset):
     """
-    An efficient, memory-safe PyTorch IterableDataset for out-of-core training
-    that streams chunked Parquet files from a directory using metadata.json boundaries.
+    An efficient, memory-safe PyTorch IterableDataset for out-of-core training.
+
+    Streams long-format Parquet files sequentially using cross-file buffering to yield
+    exact batches, eliminating CPU cloning bottlenecks. Fully supports DDP/FSDP by
+    precisely calculating and distributing sample boundaries across GPU ranks and workers.
+
+    Args:
+        data_path (str): Path to the directory containing `.parquet` chunks and `metadata.json`.
+        config (TrainModel): Training configuration (batch size, workers, sequence length, etc.).
+        shuffle (bool, optional): If True, deterministically shuffles file order and
+            sample indices per epoch. Defaults to True.
+
+    Yields:
+        Tuple[Dict[str, torch.Tensor], Dict[str, torch.Tensor], None, None, None]:
+            A batch tuple containing sequence dictionaries, target dictionaries,
+            and three `None` placeholders (for API compatibility).
+
+    Raises:
+        FileNotFoundError: If `metadata.json` is missing.
+        Exception: If sample counts are uneven across ranks using the 'exact' sampling
+            strategy, or if a GPU rank is assigned no files.
     """
 
     def __init__(self, data_path: str, config: TrainModel, shuffle: bool = True):
@@ -28,7 +49,10 @@ def __init__(self, data_path: str, config: TrainModel, shuffle: bool = True):
 
         metadata_path = os.path.join(self.data_dir, "metadata.json")
         if not os.path.exists(metadata_path):
-            raise FileNotFoundError(f"metadata.json not found in '{self.data_dir}'.")
+            raise FileNotFoundError(
+                f"metadata.json not found in '{self.data_dir}'. "
+                "Ensure data is pre-processed with merge_output: False."
+            )
 
         with open(metadata_path, "r") as f:
             metadata = json.load(f)
@@ -37,18 +61,21 @@ def __init__(self, data_path: str, config: TrainModel, shuffle: bool = True):
         self.total_samples = metadata["total_samples"]
         self.sampling_strategy = config.training_spec.sampling_strategy
 
-        # Re-use your cross-GPU sync arithmetic
-        self.target_samples = self._get_target_samples()
-        self.total_batches = self._calculate_total_batches(self.target_samples)
-
         self.column_torch_types = {
             col: PANDAS_TO_TORCH_TYPES[config.column_types[col]]
             for col in config.column_types
         }
 
+        self.target_samples = self._get_target_samples()
+        self.total_batches = self._calculate_total_batches(self.target_samples)
+        logger.info(
+            f"[INFO] Lazy Parquet Dataset mapped with {self.target_samples} samples and {self.total_batches} batches."
+        )
+
     def _calculate_total_batches(self, target_samples: int) -> int:
         num_workers = self.config.training_spec.num_workers
         num_workers_to_use = num_workers if num_workers > 0 else 1
+
         total_batches = 0
         for worker_id in range(num_workers_to_use):
             worker_samples = target_samples // num_workers_to_use + (
@@ -58,11 +85,15 @@ def _calculate_total_batches(self, target_samples: int) -> int:
         return total_batches
 
     def set_epoch(self, epoch: int):
+        """Allows the training loop to set the epoch for deterministic file shuffling."""
         self.epoch = epoch
 
     def _get_target_samples(self) -> int:
+        """Calculates exact sample count per rank to ensure FSDP syncs properly."""
         world_size = dist.get_world_size() if dist.is_initialized() else 1
+
         num_files = len(self.batch_files_info)
+
         samples_per_rank = []
         for r in range(world_size):
             f_r = list(range(r, num_files, world_size))
@@ -71,10 +102,47 @@ def _get_target_samples(self) -> int:
             )
 
         if self.sampling_strategy == "exact":
-            return int(samples_per_rank[0])
+            samples_per_rank = np.array(samples_per_rank)
+            unique_samples_per_rank, counts = np.unique(
+                samples_per_rank, return_counts=True
+            )
+            if len(unique_samples_per_rank) > 1:
+                if np.max(counts) / np.sum(counts) > 0.8:
+                    most_frequent_unique_samples_val = unique_samples_per_rank[
+                        np.argmax(counts)
+                    ]
+                    non_max_idx = np.where(
+                        samples_per_rank != most_frequent_unique_samples_val
+                    )[0]
+                    files_strings = []
+                    for i in non_max_idx:
+                        f_r = list(range(i, num_files, world_size))
+                        files_strings.append(
+                            "\n\t".join(
+                                [
+                                    f'{self.batch_files_info[j]["path"].split(os.sep)[-1]}: {self.batch_files_info[j]["samples"]}'
+                                    for j in f_r
+                                ]
+                            )
+                        )
+                    rank_details = [
+                        f"Rank {i}: {samples_per_rank[i]} samples, files:\n\t{files_strings[i]}"
+                        for i in non_max_idx
+                    ]
+                    rank_details = "\n".join(rank_details)
+                    exception_detail = f":\nMost frequent sample value: {most_frequent_unique_samples_val}\n{rank_details}"
+                else:
+                    exception_detail = ""
+
+                raise Exception(
+                    f"Found {len(unique_samples_per_rank)} different number of samples per rank/GPU: {unique_samples_per_rank}{exception_detail}"
+                )
+            return int(unique_samples_per_rank[0])
+
         elif self.sampling_strategy == "oversampling":
             return max(samples_per_rank)
         else:
+            assert self.sampling_strategy == "undersampling"
             return min(samples_per_rank)
 
     def __len__(self) -> int:
@@ -92,32 +160,41 @@ def __iter__(
         worker_id = worker_info.id if worker_info is not None else 0
         num_workers = worker_info.num_workers if worker_info is not None else 1
 
+        # 1. Distribute files among ranks
         num_files = len(self.batch_files_info)
         files_for_this_rank = list(range(rank, num_files, world_size))
 
-        if not files_for_this_rank and self.sampling_strategy == "oversampling":
-            files_for_this_rank = [rank % num_files]
+        if not files_for_this_rank:
+            if self.sampling_strategy == "oversampling":
+                files_for_this_rank = [rank % num_files]
+            else:
+                raise Exception(f"No file found for GPU rank {rank}.")
 
+        # 2. Assign exact sample quotas and boundaries to this specific worker thread
         base_samples_per_worker = self.target_samples // num_workers
         remainder = self.target_samples % num_workers
 
-        worker_start_sample = sum(
-            base_samples_per_worker + (1 if i < remainder else 0)
-            for i in range(worker_id)
-        )
+        # Calculate exactly where this worker's data starts and ends in the global stream
+        worker_start_sample = 0
+        for i in range(worker_id):
+            worker_start_sample += base_samples_per_worker + (1 if i < remainder else 0)
+
         worker_target_samples = base_samples_per_worker + (
             1 if worker_id < remainder else 0
         )
         worker_end_sample = worker_start_sample + worker_target_samples
 
+        # 3. Shuffle files deterministically
         g = torch.Generator()
         g.manual_seed(self.config.seed + self.epoch)
+
         if self.shuffle:
             file_order = torch.randperm(len(files_for_this_rank), generator=g).tolist()
             ordered_files = [files_for_this_rank[i] for i in file_order]
         else:
             ordered_files = files_for_this_rank.copy()
 
+        # 4. Extend files based on exact target requirements
         extended_files = []
         current_samples = 0
         file_idx = 0
@@ -127,17 +204,20 @@ def __iter__(
             current_samples += self.batch_files_info[f_id]["samples"]
             file_idx += 1
 
+        # 5. Stream data using precise global boundaries and a CROSS-FILE BUFFER
         yielded_samples = 0
         train_seq_len = self.config.seq_length
         global_file_start_sample = 0
 
-        seq_buffer, tgt_buffer = {}, {}
-        buffer_len = 0
-
-        # Sequence formatting configurations mimicking numpy_to_pytorch logic
+        # Sequence formatting configurations
         input_seq_cols = [str(c) for c in range(train_seq_len, 0, -1)]
         target_seq_cols = [str(c) for c in range(train_seq_len - 1, -1, -1)]
 
+        # Initialize cross-file buffers
+        seq_buffer: Dict[str, torch.Tensor] = {}
+        tgt_buffer: Dict[str, torch.Tensor] = {}
+        buffer_len = 0
+
         for f_id in extended_files:
             if yielded_samples >= worker_target_samples:
                 break
@@ -147,31 +227,43 @@ def __iter__(
             file_end = global_file_start_sample + file_samples
             global_file_start_sample += file_samples
 
+            # Skip this file if it belongs entirely to other workers
             if file_end <= worker_start_sample or file_start >= worker_end_sample:
                 continue
 
+            # This file overlaps with our worker's assigned boundary. Load it.
             file_path = os.path.join(self.data_dir, self.batch_files_info[f_id]["path"])
-
-            # Read Long format Parquet into Polars
             df = pl.read_parquet(file_path)
             feature_names = df["inputCol"].unique().to_list()
 
-            # Slice the sequence IDs matching this worker's chunk boundaries
+            # Generate indices for the whole file using torch (matching pt_lazy)
+            indices = torch.arange(file_samples)
+            if self.shuffle:
+                g_file = torch.Generator()
+                g_file.manual_seed(self.config.seed + self.epoch + f_id + rank)
+                indices = indices[torch.randperm(file_samples, generator=g_file)]
+
+            # Slice the indices to extract ONLY the portion belonging to this worker
             worker_file_start_idx = max(0, worker_start_sample - file_start)
             worker_file_end_idx = min(file_samples, worker_end_sample - file_start)
-            num_new_samples = worker_file_end_idx - worker_file_start_idx
 
-            if num_new_samples <= 0:
+            worker_indices = indices[worker_file_start_idx:worker_file_end_idx]
+            num_new_samples = len(worker_indices)
+
+            if num_new_samples == 0:
+                del df
                 continue
 
+            # Convert to numpy array for Polars indexing
+            worker_indices_np = worker_indices.numpy()
+
             # Process Long format data structures into PyTorch Tensors
             new_seq, new_tgt = {}, {}
             for col_name in feature_names:
                 feature_df = df.filter(pl.col("inputCol") == col_name)
 
-                # Extract chunk rows matching worker constraints
-                feature_chunk = feature_df.slice(worker_file_start_idx, num_new_samples)
-
+                # Positional advanced selection using the coordinated indices
+                feature_chunk = feature_df[worker_indices_np]
                 torch_type = self.column_torch_types[col_name]
 
                 new_seq[col_name] = torch.tensor(
@@ -181,10 +273,13 @@ def __iter__(
                     feature_chunk.select(target_seq_cols).to_numpy(), dtype=torch_type
                 )
 
+            # Free the DataFrame immediately to keep RAM down
             del df
 
+            # Append the new slice to the cross-file buffer
             if buffer_len == 0:
-                seq_buffer, tgt_buffer = new_seq, new_tgt
+                seq_buffer = new_seq
+                tgt_buffer = new_tgt
             else:
                 seq_buffer = {
                     k: torch.cat([seq_buffer[k], new_seq[k]], dim=0) for k in seq_buffer
@@ -195,20 +290,24 @@ def __iter__(
 
             buffer_len += num_new_samples
 
+            # Yield batches as long as the buffer contains at least `batch_size` samples
             while buffer_len >= self.batch_size:
                 if yielded_samples >= worker_target_samples:
                     break
 
+                # Slice out a perfect batch from the top of the buffer
                 batch_seq = {k: v[: self.batch_size] for k, v in seq_buffer.items()}
                 batch_tgt = {k: v[: self.batch_size] for k, v in tgt_buffer.items()}
 
                 yield batch_seq, batch_tgt, None, None, None
                 yielded_samples += self.batch_size
 
+                # Keep the remainder in the buffer for the next loop/file
                 seq_buffer = {k: v[self.batch_size :] for k, v in seq_buffer.items()}
                 tgt_buffer = {k: v[self.batch_size :] for k, v in tgt_buffer.items()}
                 buffer_len -= self.batch_size
 
+        # 6. Yield the final partial batch from the buffer if any remains
         if buffer_len > 0 and yielded_samples < worker_target_samples:
             remaining_needed = worker_target_samples - yielded_samples
             final_yield_size = min(buffer_len, remaining_needed)
diff --git a/src/sequifier/preprocess.py b/src/sequifier/preprocess.py
index c7d4380f..0651e1a3 100644
--- a/src/sequifier/preprocess.py
+++ b/src/sequifier/preprocess.py
@@ -805,20 +805,18 @@ def _export_metadata(
 
     @beartype
     def _create_metadata_for_folder(self, folder_path: str, write_format: str) -> None:
-        """Scans a directory for .pt files, counts samples, and writes metadata.json.
-
-                This method is used when `write_format` is 'pt' and
-                `merge_output` is False. It iterates over all .pt files
-                in the given `folder_path`, loads each one to count the number of
-                samples (sequences), and writes a `metadata.json` file in that
-                same folder. This JSON file contains the total sample count and a
-                list of all batch files with their respective sample counts, which
-        s        is required by the `SequifierDatasetFromFolderPt` data loader.
-
-                Args:
-                    folder_path: The path to the directory containing the .pt batch files
-                        for a specific data split.
-                    write_format: file format
+        """Scans a directory for batch files, counts samples, and writes metadata.json.
+
+        This method is used when `merge_output` is False. It iterates over all
+        files in the given `folder_path` matching the `write_format`, loads each
+        one to count the number of samples (sequences), and writes a `metadata.json`
+        file in that same folder. This JSON file contains the total sample count and a
+        list of all batch files with their respective sample counts.
+
+        Args:
+            folder_path: The path to the directory containing the batch files
+                for a specific data split.
+            write_format: The file format of the output files (e.g., 'pt', 'parquet').
         """
         logger.info(f"Creating metadata for folder '{folder_path}'")
         batch_files_metadata = []
diff --git a/tests/unit/io/test_sequifier_dataset_from_folder_parquet_lazy.py b/tests/unit/io/test_sequifier_dataset_from_folder_parquet_lazy.py
new file mode 100644
index 00000000..591117a1
--- /dev/null
+++ b/tests/unit/io/test_sequifier_dataset_from_folder_parquet_lazy.py
@@ -0,0 +1,214 @@
+import json
+from unittest.mock import MagicMock, patch
+
+import polars as pl
+import pytest
+import torch
+
+from sequifier.io.sequifier_dataset_from_folder_parquet_lazy import (
+    SequifierDatasetFromFolderParquetLazy,
+)
+
+
+@pytest.fixture
+def mock_config():
+    config = MagicMock()
+    config.project_root = "."
+    config.seed = 42
+    config.seq_length = 2
+    config.column_types = {"item": "Float64"}
+    config.training_spec.batch_size = 5
+    config.training_spec.num_workers = 0
+    config.training_spec.sampling_strategy = "exact"
+    return config
+
+
+@pytest.fixture
+def dataset_path(tmp_path):
+    data_dir = tmp_path / "parquet_data"
+    data_dir.mkdir()
+
+    # Layout matches the long-format extraction logic
+    schema = {
+        "sequenceId": pl.Int64,
+        "subsequenceId": pl.Int64,
+        "startItemPosition": pl.Int64,
+        "inputCol": pl.String,
+        "2": pl.Float64,
+        "1": pl.Float64,
+        "0": pl.Float64,
+    }
+
+    # Populate 4 files with 10 rows (10 sequences) each
+    batch_files = []
+    for i in range(1, 5):
+        filename = f"file_{i}.parquet"
+        rows = []
+        for s in range(10):
+            rows.append((s, 0, s * 2, "item", float(s), float(s + 1), float(s + 2)))
+
+        df = pl.DataFrame(rows, schema=schema)
+        df.write_parquet(data_dir / filename)
+        batch_files.append({"path": filename, "samples": 10})
+
+    metadata = {"total_samples": 40, "batch_files": batch_files}
+
+    with open(data_dir / "metadata.json", "w") as f:
+        json.dump(metadata, f)
+
+    return str(data_dir)
+
+
+def test_initialization(mock_config, dataset_path):
+    """Tests that metadata is read correctly and __len__ calculates batches."""
+    dataset = SequifierDatasetFromFolderParquetLazy(dataset_path, mock_config)
+
+    # 40 total samples / batch size of 5 = 8 batches
+    assert len(dataset) == 8
+    assert dataset.total_samples == 40
+    assert dataset.target_samples == 40
+
+
+def test_iteration_yields_correct_batches(mock_config, dataset_path):
+    """Tests that the dataset iterates over files and yields structured tensors."""
+    dataset = SequifierDatasetFromFolderParquetLazy(
+        dataset_path, mock_config, shuffle=False
+    )
+
+    batches = list(dataset)
+    assert len(batches) == 8  # 40 samples / batch_size 5
+
+    # Check structural integrity of first batch
+    seq_batch, tgt_batch, _, _, _ = batches[0]
+    assert "item" in seq_batch
+    assert "item" in tgt_batch
+    assert isinstance(seq_batch["item"], torch.Tensor)
+    assert seq_batch["item"].shape == (5, 2)  # batch_size=5, seq_len=2
+    assert tgt_batch["item"].shape == (5, 2)
+
+
+@patch("torch.distributed.is_initialized", return_value=True)
+@patch("torch.distributed.get_rank", return_value=0)
+@patch("torch.distributed.get_world_size", return_value=2)
+def test_distributed_sharding(mock_ws, mock_rank, mock_init, mock_config, dataset_path):
+    """Tests that the dataset correctly shards files across distributed GPU ranks."""
+    dataset = SequifierDatasetFromFolderParquetLazy(
+        dataset_path, mock_config, shuffle=False
+    )
+
+    # World size = 2, Total files = 4
+    # Rank 0 gets file index 0 and 2 (file_1, file_3) -> 20 samples total -> 4 batches
+    batches = list(dataset)
+    assert len(batches) == 4
+
+    # Verify input mapping structures
+    for seq_batch, _, _, _, _ in batches:
+        assert seq_batch["item"].shape[0] == 5
+
+
+def test_exact_strategy_uneven_files_exception(mock_config, tmp_path):
+    """Tests that FSDP validation raises an Exception when file distribution is uneven."""
+    data_dir = tmp_path / "uneven_parquet_data"
+    data_dir.mkdir()
+
+    # Write asymmetrical sample quotas across files
+    batch_files = [
+        {"path": "file_1.parquet", "samples": 15},
+        {"path": "file_2.parquet", "samples": 10},
+    ]
+    metadata = {"total_samples": 25, "batch_files": batch_files}
+    with open(data_dir / "metadata.json", "w") as f:
+        json.dump(metadata, f)
+
+    with patch("torch.distributed.is_initialized", return_value=True), patch(
+        "torch.distributed.get_world_size", return_value=2
+    ):
+        with pytest.raises(Exception) as exc_info:
+            SequifierDatasetFromFolderParquetLazy(str(data_dir), mock_config)
+
+        assert "different number of samples per rank/GPU" in str(exc_info.value)
+
+
+def test_oversampling_strategy(mock_config, tmp_path):
+    """Tests that shorter ranks oversample to equal the maximal rank count."""
+    data_dir = tmp_path / "oversample_parquet_data"
+    data_dir.mkdir()
+
+    schema = {
+        "sequenceId": pl.Int64,
+        "subsequenceId": pl.Int64,
+        "startItemPosition": pl.Int64,
+        "inputCol": pl.String,
+        "2": pl.Float64,
+        "1": pl.Float64,
+        "0": pl.Float64,
+    }
+
+    # File 1 has 15 rows, File 2 has 10 rows
+    for i, num_rows in [(1, 15), (2, 10)]:
+        rows = [
+            (s, 0, s * 2, "item", float(s), float(s + 1), float(s + 2))
+            for s in range(num_rows)
+        ]
+        pl.DataFrame(rows, schema=schema).write_parquet(data_dir / f"file_{i}.parquet")
+
+    batch_files = [
+        {"path": "file_1.parquet", "samples": 15},
+        {"path": "file_2.parquet", "samples": 10},
+    ]
+    metadata = {"total_samples": 25, "batch_files": batch_files}
+    with open(data_dir / "metadata.json", "w") as f:
+        json.dump(metadata, f)
+
+    mock_config.training_spec.sampling_strategy = "oversampling"
+
+    with patch("torch.distributed.is_initialized", return_value=True), patch(
+        "torch.distributed.get_world_size", return_value=2
+    ):
+        dataset = SequifierDatasetFromFolderParquetLazy(
+            str(data_dir), mock_config, shuffle=False
+        )
+        # Should match max(15, 10)
+        assert dataset.target_samples == 15
+
+
+def test_undersampling_strategy(mock_config, tmp_path):
+    """Tests that longer ranks truncate samples down to match the minimal rank count."""
+    data_dir = tmp_path / "undersample_parquet_data"
+    data_dir.mkdir()
+
+    schema = {
+        "sequenceId": pl.Int64,
+        "subsequenceId": pl.Int64,
+        "startItemPosition": pl.Int64,
+        "inputCol": pl.String,
+        "2": pl.Float64,
+        "1": pl.Float64,
+        "0": pl.Float64,
+    }
+
+    for i, num_rows in [(1, 15), (2, 10)]:
+        rows = [
+            (s, 0, s * 2, "item", float(s), float(s + 1), float(s + 2))
+            for s in range(num_rows)
+        ]
+        pl.DataFrame(rows, schema=schema).write_parquet(data_dir / f"file_{i}.parquet")
+
+    batch_files = [
+        {"path": "file_1.parquet", "samples": 15},
+        {"path": "file_2.parquet", "samples": 10},
+    ]
+    metadata = {"total_samples": 25, "batch_files": batch_files}
+    with open(data_dir / "metadata.json", "w") as f:
+        json.dump(metadata, f)
+
+    mock_config.training_spec.sampling_strategy = "undersampling"
+
+    with patch("torch.distributed.is_initialized", return_value=True), patch(
+        "torch.distributed.get_world_size", return_value=2
+    ):
+        dataset = SequifierDatasetFromFolderParquetLazy(
+            str(data_dir), mock_config, shuffle=False
+        )
+        # Should match min(15, 10)
+        assert dataset.target_samples == 10
diff --git a/tests/unit/io/test_sequifier_dataset_from_folder_lazy.py b/tests/unit/io/test_sequifier_dataset_from_folder_pt_lazy.py
similarity index 100%
rename from tests/unit/io/test_sequifier_dataset_from_folder_lazy.py
rename to tests/unit/io/test_sequifier_dataset_from_folder_pt_lazy.py

From 8802c41caaad65984683d4c55d43c4b7f69f0930 Mon Sep 17 00:00:00 2001
From: Leon Luithlen <leontimnaluithlen@gmail.com>
Date: Wed, 20 May 2026 17:58:18 +0200
Subject: [PATCH 09/15] update outputs

---
 ...cal-multitarget-5-best-3-0-predictions.csv |   2 +-
 ...cal-multitarget-5-best-3-1-predictions.csv |   2 +-
 ...cal-multitarget-5-best-3-2-predictions.csv |   2 +-
 ...cal-multitarget-5-best-3-3-predictions.csv |   4 +-
 ...cal-multitarget-5-best-3-4-predictions.csv |   2 +-
 ...cal-multitarget-5-best-3-5-predictions.csv |   4 +-
 ...cal-multitarget-5-best-3-6-predictions.csv |   2 +-
 ...cal-multitarget-5-best-3-7-predictions.csv |   2 +-
 ...al-1-best-3-autoregression-predictions.csv | 438 +++++++++---------
 ...l-multitarget-5-best-3-0-probabilities.csv |   2 +-
 ...l-multitarget-5-best-3-1-probabilities.csv |   2 +-
 ...l-multitarget-5-best-3-2-probabilities.csv |   2 +-
 ...l-multitarget-5-best-3-3-probabilities.csv |   4 +-
 ...l-multitarget-5-best-3-4-probabilities.csv |   2 +-
 ...l-multitarget-5-best-3-5-probabilities.csv |   4 +-
 ...l-multitarget-5-best-3-6-probabilities.csv |   2 +-
 ...l-multitarget-5-best-3-7-probabilities.csv |   2 +-
 ...l-multitarget-5-best-3-0-probabilities.csv |   2 +-
 ...l-multitarget-5-best-3-1-probabilities.csv |   2 +-
 ...l-multitarget-5-best-3-2-probabilities.csv |   2 +-
 ...l-multitarget-5-best-3-3-probabilities.csv |   4 +-
 ...l-multitarget-5-best-3-4-probabilities.csv |   2 +-
 ...l-multitarget-5-best-3-5-probabilities.csv |   4 +-
 ...l-multitarget-5-best-3-6-probabilities.csv |   2 +-
 ...l-multitarget-5-best-3-7-probabilities.csv |   2 +-
 25 files changed, 249 insertions(+), 249 deletions(-)

diff --git a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-0-predictions.csv b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-0-predictions.csv
index 0b1c1c1a..fae8ad82 100644
--- a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-0-predictions.csv
+++ b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-0-predictions.csv
@@ -1,2 +1,2 @@
 sequenceId,itemPosition,itemId,supCat1,supReal3
-0,8,unknown,4,-0.068751134
+0,8,unknown,4,-0.057408422
diff --git a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-1-predictions.csv b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-1-predictions.csv
index f220577f..b16bd0a4 100644
--- a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-1-predictions.csv
+++ b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-1-predictions.csv
@@ -1,2 +1,2 @@
 sequenceId,itemPosition,itemId,supCat1,supReal3
-1,8,unknown,7,-0.21511942
+1,8,unknown,7,-0.21608433
diff --git a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-2-predictions.csv b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-2-predictions.csv
index 9b23d933..1d366ef5 100644
--- a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-2-predictions.csv
+++ b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-2-predictions.csv
@@ -1,2 +1,2 @@
 sequenceId,itemPosition,itemId,supCat1,supReal3
-2,8,unknown,7,-0.20590982
+2,8,unknown,7,-0.20284554
diff --git a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-3-predictions.csv b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-3-predictions.csv
index 8cc01f75..dc38f607 100644
--- a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-3-predictions.csv
+++ b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-3-predictions.csv
@@ -1,3 +1,3 @@
 sequenceId,itemPosition,itemId,supCat1,supReal3
-3,8,unknown,0,0.073820405
-4,8,unknown,3,0.018186308
+3,8,unknown,0,0.074112006
+4,8,unknown,0,0.012103111
diff --git a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-4-predictions.csv b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-4-predictions.csv
index 76b28afb..e88cd3ba 100644
--- a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-4-predictions.csv
+++ b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-4-predictions.csv
@@ -1,2 +1,2 @@
 sequenceId,itemPosition,itemId,supCat1,supReal3
-5,8,unknown,7,-0.1776878
+5,8,unknown,7,-0.1722543
diff --git a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-5-predictions.csv b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-5-predictions.csv
index b4d499f9..ade459a5 100644
--- a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-5-predictions.csv
+++ b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-5-predictions.csv
@@ -1,3 +1,3 @@
 sequenceId,itemPosition,itemId,supCat1,supReal3
-6,8,unknown,3,-0.02668449
-7,8,unknown,4,-0.020446751
+6,8,unknown,3,-0.034481153
+7,8,unknown,4,-0.022850327
diff --git a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-6-predictions.csv b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-6-predictions.csv
index aa9b9d48..dcac6f52 100644
--- a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-6-predictions.csv
+++ b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-6-predictions.csv
@@ -1,2 +1,2 @@
 sequenceId,itemPosition,itemId,supCat1,supReal3
-8,8,unknown,3,-0.07486756
+8,8,unknown,3,-0.088609315
diff --git a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-7-predictions.csv b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-7-predictions.csv
index 6a841d79..eeadd198 100644
--- a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-7-predictions.csv
+++ b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-best-3-predictions/sequifier-model-categorical-multitarget-5-best-3-7-predictions.csv
@@ -1,2 +1,2 @@
 sequenceId,itemPosition,itemId,supCat1,supReal3
-9,8,unknown,4,-0.012736145
+9,8,unknown,4,-0.001879178
diff --git a/tests/resources/target_outputs/predictions/sequifier-model-real-1-best-3-autoregression-predictions.csv b/tests/resources/target_outputs/predictions/sequifier-model-real-1-best-3-autoregression-predictions.csv
index 981cfaec..ec2b955a 100644
--- a/tests/resources/target_outputs/predictions/sequifier-model-real-1-best-3-autoregression-predictions.csv
+++ b/tests/resources/target_outputs/predictions/sequifier-model-real-1-best-3-autoregression-predictions.csv
@@ -1,220 +1,220 @@
 sequenceId,itemPosition,itemValue
-0,21,0.10343069
-0,22,0.4119229
-0,23,0.06100054
-0,24,0.52972895
-0,25,0.2781431
-0,26,0.19428115
-0,27,0.33205438
-0,28,0.26815954
-0,29,0.19228444
-0,30,-0.076772675
-0,31,-0.22103521
-0,32,-0.038835123
-0,33,0.11191672
-0,34,0.09594302
-0,35,-0.08725542
-0,36,-0.08625706
-0,37,-0.014312988
-0,38,0.14136824
-0,39,-0.005889354
-0,40,-0.047071565
-0,41,-0.04307814
-0,42,0.0709841
-0,43,-0.006326135
-1,19,0.28213653
-1,20,0.22123677
-1,21,0.21025485
-1,22,0.26216942
-1,23,0.0435293
-1,24,0.18230087
-1,25,0.14336495
-1,26,-0.21404672
-1,27,-0.017308058
-1,28,0.00038937308
-1,29,0.188291
-1,30,-0.15414532
-1,31,-0.09374474
-1,32,-0.25697604
-1,33,-0.1177053
-1,34,-0.06054938
-1,35,0.07647506
-1,36,0.09943727
-1,37,0.16932222
-1,38,0.16333209
-1,39,0.03778875
-1,40,0.0044373972
-2,17,0.21924005
-2,18,0.21524663
-2,19,0.216245
-2,20,0.16233373
-2,21,0.27215296
-2,22,0.0779726
-2,23,0.09344713
-2,24,0.13437974
-2,25,-0.013065042
-2,26,0.06848821
-2,27,-0.298907
-2,28,-0.050066635
-2,29,-0.07377761
-2,30,-0.1501519
-2,31,-0.20006973
-2,32,-0.1521486
-2,33,0.10043562
-2,34,-0.08975131
-2,35,-0.0026914931
-2,36,-0.05455924
-2,37,-0.00018780184
-2,38,-0.31488073
-3,17,-0.024608541
-3,18,0.44786373
-3,19,0.3540182
-3,20,0.3220708
-3,21,0.19328278
-3,22,0.16033702
-3,23,0.19727622
-3,24,-0.09174803
-3,25,-0.09424392
-3,26,-0.10872009
-3,27,0.051516153
-3,28,0.028803539
-3,29,0.0015047246
-3,30,0.07747342
-3,31,0.08995288
-3,32,0.044777244
-3,33,-0.07876939
-3,34,-0.0014381849
-3,35,0.042281352
-3,36,-0.18110095
-3,37,-0.070782535
-3,38,0.1653288
-4,14,0.20526306
-4,15,0.1493551
-4,16,-0.07876939
-4,17,0.14835674
-4,18,0.041532584
-4,19,-0.011567507
-4,20,0.174314
-4,21,0.11790686
-4,22,-0.21204999
-4,23,-0.011317918
-4,24,-0.06828664
-4,25,0.13937153
-4,26,-0.13817161
-4,27,0.1493551
-4,28,0.02805477
-4,29,0.06798903
-4,30,0.11191672
-4,31,0.041782174
-4,32,0.13138467
-4,33,0.030800251
-4,34,-0.069285005
-5,14,0.19727622
-5,15,0.21924005
-5,16,0.15235017
-5,17,0.06898739
-5,18,0.38796234
-5,19,0.36400178
-5,20,0.18629429
-5,21,0.018944766
-5,22,-0.12119955
-5,23,-0.041081425
-5,24,-0.028976351
-5,25,-0.037088
-5,26,0.040534228
-5,27,-0.15813874
-5,28,0.056258347
-5,29,-0.076772675
-5,30,0.08346356
-5,31,0.06075095
-5,32,-0.043327726
-5,33,0.08346356
-5,34,-0.09973488
-6,18,0.091450416
-6,19,0.42390317
-6,20,0.28812668
-6,21,0.36000836
-6,22,0.04303012
-6,23,-0.1521486
-6,24,0.06549314
-6,25,-0.023734977
-6,26,0.35801163
-6,27,0.09244877
-6,28,-0.1177053
-6,29,-0.18609272
-6,30,0.008212433
-6,31,-0.11520941
-6,32,0.14036989
-6,33,-0.025856486
-6,34,-0.19407958
-6,35,0.09194959
-6,36,-0.03633923
-6,37,-0.09124885
-6,38,0.1124159
-6,39,-0.060299788
-7,15,0.19128607
-7,16,0.004375
-7,17,0.050268207
-7,18,0.19228444
-7,19,0.28013983
-7,20,0.020567097
-7,21,0.17032059
-7,22,0.11790686
-7,23,-0.2190385
-7,24,0.14336495
-7,25,0.041033406
-7,26,0.08745699
-7,27,-0.011754699
-7,28,0.018071204
-7,29,0.025933264
-7,30,0.30210367
-7,31,-0.003065877
-7,32,0.09843891
-7,33,0.12040275
-7,34,-0.1521486
-7,35,-0.006419731
-8,20,0.13238303
-8,21,0.14136824
-8,22,0.18729265
-8,23,0.25418255
-8,24,0.26416612
-8,25,0.059503004
-8,26,0.053762455
-8,27,0.016948054
-8,28,-0.06728829
-8,29,-0.14216504
-8,30,-0.22303192
-8,31,-0.21804014
-8,32,0.060251772
-8,33,0.08895452
-8,34,-0.23401384
-8,35,0.08046849
-8,36,0.12838961
-8,37,-0.09524228
-8,38,0.15334852
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-0-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-0-probabilities.csv
index deccb543..bc35a828 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-0-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-0-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
-0.029919926,0.029241586,0.03407666,0.02653628,0.031944077,0.030346371,0.03517379,0.029561197,0.03584215,0.028416175,0.034457605,0.030069351,0.029750984,0.030594636,0.029850584,0.03833028,0.030466197,0.032158453,0.026207946,0.03495887,0.03253294,0.03370815,0.030059796,0.031647764,0.033966444,0.025543736,0.032113243,0.030116683,0.028788822,0.032551937,0.027236454,0.033830907
+0.02987246,0.0291298,0.034083534,0.026629673,0.032076977,0.030450517,0.035012987,0.029515885,0.035601407,0.02849371,0.034817744,0.030076707,0.029692352,0.030673426,0.029644514,0.038891632,0.030484164,0.032383278,0.02580936,0.035029277,0.03223794,0.033890463,0.03001799,0.031699885,0.033890437,0.025421664,0.032209013,0.029906737,0.028936805,0.03221391,0.02733048,0.033875357
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-1-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-1-probabilities.csv
index 070327b2..9e8c6ade 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-1-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-1-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-2-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-2-probabilities.csv
index bf993a46..69d54ab8 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-2-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-2-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-3-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-3-probabilities.csv
index f890df65..98cb886d 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-3-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-3-probabilities.csv
@@ -1,3 +1,3 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-4-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-4-probabilities.csv
index 148d6f3f..457f32d0 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-4-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-4-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
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diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-5-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-5-probabilities.csv
index 4c90e099..2175edbc 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-5-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-5-probabilities.csv
@@ -1,3 +1,3 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
-0.030664794,0.031082612,0.03030074,0.032872494,0.03189757,0.03554864,0.029711511,0.032308478,0.027148588,0.029142486,0.03235085,0.031445574,0.031172441,0.032628857,0.034794968,0.02697712,0.031333465,0.028972484,0.037340544,0.030013558,0.03230658,0.030605843,0.03222915,0.029832635,0.02857672,0.034617916,0.02889556,0.0289314,0.030068528,0.0319775,0.03214,0.03211042
-0.029191403,0.032407805,0.031857405,0.027433928,0.031545587,0.0324864,0.032123398,0.028324135,0.034217004,0.02482894,0.036872905,0.030475914,0.032451976,0.03375697,0.031752195,0.033943985,0.030451044,0.03274126,0.032530427,0.03606905,0.033844758,0.03184988,0.03023864,0.030798176,0.03193668,0.027185407,0.029141765,0.027430946,0.028412677,0.030810867,0.02847339,0.034415122
+0.030749116,0.031062534,0.029946074,0.03304623,0.03177652,0.036106378,0.029489571,0.0326022,0.027348582,0.029303638,0.032161407,0.03134503,0.031155298,0.032429844,0.034513615,0.027003089,0.031428523,0.028919302,0.037302244,0.029815737,0.032031134,0.030656634,0.032251637,0.029612571,0.028429309,0.03463406,0.02905775,0.029093673,0.030223982,0.032136854,0.032039963,0.032327514
+0.029171862,0.032260604,0.031479053,0.027849672,0.031409796,0.033327498,0.031761337,0.028732147,0.03429961,0.02492806,0.036731984,0.030338226,0.03251292,0.033941925,0.031204814,0.03442956,0.030674377,0.033014756,0.031726107,0.03608367,0.033338442,0.031844705,0.030129898,0.030746242,0.031700723,0.027094038,0.029256253,0.027283229,0.028538387,0.030670708,0.028805278,0.034714192
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-6-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-6-probabilities.csv
index 40eca832..deba9442 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-6-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-6-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
-0.028840212,0.031923126,0.03316305,0.029733282,0.030324597,0.035063315,0.030408965,0.031950425,0.029625356,0.027236255,0.033804726,0.03132866,0.03200365,0.03512926,0.034921013,0.028031223,0.0295095,0.029491551,0.036540907,0.03226653,0.033081695,0.032036718,0.03228033,0.030852877,0.028602235,0.031815387,0.028647292,0.026920438,0.029234089,0.03159718,0.030543774,0.033092327
+0.028799655,0.031856664,0.03283534,0.030231427,0.03015927,0.035772216,0.030314315,0.03224368,0.02983013,0.02746894,0.033414993,0.03132566,0.032006927,0.034900524,0.034404952,0.0279653,0.029755965,0.029660394,0.036329567,0.032125212,0.032573488,0.032185875,0.032224666,0.03076007,0.02825598,0.031999446,0.028718766,0.026965003,0.029454451,0.031563707,0.030569414,0.033328008
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-7-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-7-probabilities.csv
index d6b051a2..a882e828 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-7-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-itemId-probabilities/sequifier-model-categorical-multitarget-5-best-3-7-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129
-0.032431256,0.028374098,0.03241755,0.02907772,0.03322896,0.03083206,0.032768983,0.030121792,0.033098236,0.031856593,0.03449055,0.029965289,0.02779097,0.029204315,0.028356798,0.036816824,0.03156557,0.03171897,0.025158815,0.030962218,0.032085657,0.034966357,0.029915892,0.030164259,0.03391852,0.027265668,0.034304135,0.031615134,0.031207006,0.032374583,0.029263819,0.0326814
+0.032289907,0.028264519,0.03246752,0.029104413,0.033277128,0.030759139,0.032730892,0.029931597,0.033015575,0.031707276,0.03496927,0.030008726,0.027751597,0.029273633,0.02851361,0.03743072,0.03143402,0.0318968,0.024980046,0.03104891,0.032157212,0.03514057,0.029877376,0.030226672,0.03398293,0.027080398,0.034191467,0.031496957,0.03116081,0.03200099,0.029164206,0.032665085
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-0-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-0-probabilities.csv
index 4b23b309..4dbf410d 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-0-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-0-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,0,1,2,3,4,5,6,7,8,9
-0.07523441,0.07748859,0.08639577,0.085923284,0.0789727,0.07716564,0.09451911,0.08678308,0.08670555,0.08257854,0.08806588,0.08016739
+0.0748158,0.0772802,0.08608035,0.08609827,0.07993774,0.07705543,0.09423849,0.086710036,0.086828426,0.08284977,0.088354506,0.079750955
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-1-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-1-probabilities.csv
index 3307879f..6d89b556 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-1-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-1-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,0,1,2,3,4,5,6,7,8,9
-0.073339716,0.08346172,0.07554303,0.08795957,0.081076026,0.08488209,0.08302772,0.08036742,0.0847984,0.093177184,0.08934849,0.08301852
+0.07326655,0.08353559,0.07533537,0.08782462,0.08211486,0.08560346,0.08289929,0.08048919,0.08408942,0.0929573,0.089654244,0.082230076
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-2-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-2-probabilities.csv
index 7cc5a064..bf13de6f 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-2-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-2-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,0,1,2,3,4,5,6,7,8,9
-0.0735714,0.085165255,0.075638816,0.08946831,0.082018055,0.08179583,0.08238851,0.08111851,0.08600896,0.0920105,0.089723445,0.08109248
+0.073459856,0.08505141,0.07544217,0.08925692,0.08299525,0.0825825,0.082352445,0.081173785,0.085358195,0.091914274,0.09012994,0.08028323
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-3-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-3-probabilities.csv
index 73f44533..eb1f1644 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-3-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-3-probabilities.csv
@@ -1,3 +1,3 @@
 unknown,other,0,1,2,3,4,5,6,7,8,9
-0.08835912,0.07418081,0.095578186,0.07515316,0.07892947,0.08788645,0.09078124,0.0895524,0.08074507,0.07345364,0.07882017,0.08656019
-0.086663246,0.07226138,0.09419653,0.07254638,0.07658576,0.09616181,0.089182466,0.08551516,0.07912567,0.076377966,0.07799796,0.09338568
+0.088295944,0.07419422,0.09625292,0.075507574,0.07814089,0.08647674,0.09078418,0.089773305,0.08116939,0.07382207,0.078370996,0.08721177
+0.0871225,0.0723557,0.095413424,0.07266789,0.07581121,0.09429607,0.089163,0.08606107,0.07906439,0.076575436,0.0773663,0.09410305
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-4-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-4-probabilities.csv
index f72b8d3f..e1753ff4 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-4-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-4-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,0,1,2,3,4,5,6,7,8,9
-0.073002174,0.084327646,0.077937834,0.089499675,0.08172626,0.07921972,0.087675005,0.0837502,0.086888224,0.08974763,0.08855341,0.077672325
+0.07255601,0.0840692,0.07761021,0.089467965,0.08273568,0.07989225,0.08776099,0.08379931,0.08662724,0.08953262,0.08896252,0.07698605
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-5-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-5-probabilities.csv
index 5d07c161..4704ea71 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-5-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-5-probabilities.csv
@@ -1,3 +1,3 @@
 unknown,other,0,1,2,3,4,5,6,7,8,9
-0.0860558,0.07743681,0.08973225,0.07462213,0.077821694,0.098795794,0.08423508,0.08542021,0.08139216,0.07743476,0.07517884,0.09187443
-0.07830784,0.06836525,0.09037594,0.077492535,0.07662541,0.08604211,0.09636521,0.07893603,0.08409658,0.08042918,0.087497495,0.095466316
+0.08670176,0.07764295,0.09074383,0.07478624,0.07692155,0.09733425,0.08432601,0.08585503,0.08131818,0.07754218,0.07455162,0.09227636
+0.078247,0.06804814,0.09125708,0.07758486,0.07693072,0.08519664,0.09631906,0.07838798,0.0837765,0.080676705,0.08734935,0.09622603
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-6-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-6-probabilities.csv
index 6cfcac89..037661f9 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-6-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-6-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,0,1,2,3,4,5,6,7,8,9
-0.08336393,0.07317236,0.090186596,0.07377336,0.07200975,0.09935644,0.086575784,0.08554148,0.08153822,0.08023641,0.07896855,0.09527699
+0.083966985,0.073211156,0.091535,0.07379018,0.071819365,0.09759158,0.086949356,0.0855146,0.08113236,0.08044534,0.07800937,0.096034706
diff --git a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-7-probabilities.csv b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-7-probabilities.csv
index 23bea96b..fbf283d5 100644
--- a/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-7-probabilities.csv
+++ b/tests/resources/target_outputs/probabilities/sequifier-model-categorical-multitarget-5-best-3-supCat1-probabilities/sequifier-model-categorical-multitarget-5-best-3-7-probabilities.csv
@@ -1,2 +1,2 @@
 unknown,other,0,1,2,3,4,5,6,7,8,9
-0.0819516,0.08247403,0.08569772,0.08688278,0.082577325,0.073386215,0.08999724,0.0884895,0.08485753,0.081263095,0.0872452,0.07517781
+0.081276454,0.08213866,0.08511532,0.08706703,0.08304337,0.07355978,0.0899685,0.08830506,0.08529962,0.08146648,0.087632634,0.07512704

From f07631d1b7c796d67de7840a8dc5804c6e430bb2 Mon Sep 17 00:00:00 2001
From: Leon Luithlen <leontimnaluithlen@gmail.com>
Date: Wed, 20 May 2026 18:27:53 +0200
Subject: [PATCH 10/15] Small changes

---
 documentation/configs/infer.md                         |  2 --
 documentation/consolidated-docs.md                     |  6 ++----
 documentation/training/multi-gpu-training.md           |  4 ++--
 src/sequifier/config/infer_config.py                   |  4 +---
 src/sequifier/preprocess.py                            |  2 +-
 tests/configs/infer-test-lazy.yaml                     |  3 ---
 .../test_sequifier_dataset_from_folder_parquet_lazy.py | 10 +++++++---
 7 files changed, 13 insertions(+), 18 deletions(-)

diff --git a/documentation/configs/infer.md b/documentation/configs/infer.md
index fe3fd008..049510d8 100644
--- a/documentation/configs/infer.md
+++ b/documentation/configs/infer.md
@@ -60,8 +60,6 @@ These fields tell the inference engine which columns to extract from the new dat
 | `world_size` | `int` | No | `1` | Number of GPUs/processes for distributed inference. |
 | `num_workers` | `int` | No | `0` | Number of subprocesses for data loading. |
 | `enforce_determinism` | `bool` | No | `false` | Forces PyTorch to use deterministic algorithms. |
-| `load_full_data_to_ram`| `bool` | No | `true` | If `false`, uses lazy loading (requires `read_format: pt` or `read_format: parquet`). |
-
 -----
 
 ## Key Trade-offs and Decisions
diff --git a/documentation/consolidated-docs.md b/documentation/consolidated-docs.md
index f163dcaa..7dba1cfa 100644
--- a/documentation/consolidated-docs.md
+++ b/documentation/consolidated-docs.md
@@ -625,8 +625,6 @@ These fields tell the inference engine which columns to extract from the new dat
 | `world_size` | `int` | No | `1` | Number of GPUs/processes for distributed inference. |
 | `num_workers` | `int` | No | `0` | Number of subprocesses for data loading. |
 | `enforce_determinism` | `bool` | No | `false` | Forces PyTorch to use deterministic algorithms. |
-| `load_full_data_to_ram`| `bool` | No | `true` | If `false`, uses lazy loading (requires `read_format: pt` or `read_format: parquet`). |
-
 -----
 
 ## Key Trade-offs and Decisions
@@ -960,8 +958,8 @@ In your `train.yaml`, update the `training_spec` block:
 ```yaml
 training_spec:
   distributed: true
-  data_parallelism: 'FSDP' # or 'DDP   # Set to true to shard model weights/gradients across GPUs
-  fsdp_cpu_offload: false              # Set to true to offload parameters to CPU RAM
+  data_parallelism: 'FSDP' # or 'DDP'   # Set to true to shard model weights/gradients across GPUs
+  fsdp_cpu_offload: false   # omit if using 'DDP', set to true to offload parameters to CPU RAM
   world_size: 32       # The TOTAL number of GPUs across all nodes (e.g., 8 nodes * 4 GPUs = 32)
   backend: nccl        # 'nccl' is the standard and most efficient backend for NVIDIA GPUs
   sampling_strategy: 'oversampling' # if the number of files isn't perfectly divisible by the number of GPUs, you need to choose either 'oversampling' or 'undersampling'. If it is perfectly divisible, you can set it to 'exact', but the files must be very close to each other in size to prevent timing mismatches
diff --git a/documentation/training/multi-gpu-training.md b/documentation/training/multi-gpu-training.md
index d5a75307..95590f66 100644
--- a/documentation/training/multi-gpu-training.md
+++ b/documentation/training/multi-gpu-training.md
@@ -33,8 +33,8 @@ In your `train.yaml`, update the `training_spec` block:
 ```yaml
 training_spec:
   distributed: true
-  data_parallelism: 'FSDP' # or 'DDP   # Set to true to shard model weights/gradients across GPUs
-  fsdp_cpu_offload: false              # Set to true to offload parameters to CPU RAM
+  data_parallelism: 'FSDP' # or 'DDP'   # Set to true to shard model weights/gradients across GPUs
+  fsdp_cpu_offload: false   # omit if using 'DDP', set to true to offload parameters to CPU RAM
   world_size: 32       # The TOTAL number of GPUs across all nodes (e.g., 8 nodes * 4 GPUs = 32)
   backend: nccl        # 'nccl' is the standard and most efficient backend for NVIDIA GPUs
   sampling_strategy: 'oversampling' # if the number of files isn't perfectly divisible by the number of GPUs, you need to choose either 'oversampling' or 'undersampling'. If it is perfectly divisible, you can set it to 'exact', but the files must be very close to each other in size to prevent timing mismatches
diff --git a/src/sequifier/config/infer_config.py b/src/sequifier/config/infer_config.py
index c360dcc9..8c61bc66 100644
--- a/src/sequifier/config/infer_config.py
+++ b/src/sequifier/config/infer_config.py
@@ -100,7 +100,6 @@ class InfererModel(BaseModel):
         seq_length: The sequence length of the model's input.
         inference_batch_size: The batch size for inference.
         distributed: If True, enables distributed inference.
-        load_full_data_to_ram: If True, loads the entire dataset into RAM.
         world_size: The number of processes for distributed inference.
         num_workers: The number of worker threads for data loading.
         sample_from_distribution_columns: A list of columns from which to sample from the distribution.
@@ -137,7 +136,6 @@ class InfererModel(BaseModel):
     inference_batch_size: int
 
     distributed: bool = False
-    load_full_data_to_ram: bool = True
     world_size: int = 1
     num_workers: int = 0
 
@@ -260,7 +258,7 @@ def validate_map_to_id(cls, v: bool, info: ValidationInfo) -> bool:
     @field_validator("distributed")
     @classmethod
     def validate_distributed_inference(cls, v: bool, info: ValidationInfo) -> bool:
-        if v and info.data.get("read_format") != "pt":
+        if v and info.data.get("read_format") not in ["pt", "parquet"]:
             raise ValueError(
                 "Distributed inference is only supported for preprocessed '.pt' files. Please set read_format to 'pt'."
             )
diff --git a/src/sequifier/preprocess.py b/src/sequifier/preprocess.py
index 0651e1a3..46f84ada 100644
--- a/src/sequifier/preprocess.py
+++ b/src/sequifier/preprocess.py
@@ -111,7 +111,7 @@ def __init__(
         else:
             if write_format not in ["pt", "parquet"]:
                 raise ValueError(
-                    f"write_format must be 'pt' when merge_output is False, got '{write_format}'"
+                    f"write_format must be 'pt' or 'parquet' when merge_output is False, got '{write_format}'"
                 )
             self.target_dir = f"{self.data_name_root}-temp"
 
diff --git a/tests/configs/infer-test-lazy.yaml b/tests/configs/infer-test-lazy.yaml
index d7e16e28..1c8f4137 100644
--- a/tests/configs/infer-test-lazy.yaml
+++ b/tests/configs/infer-test-lazy.yaml
@@ -18,6 +18,3 @@ map_to_id: true
 device: cpu
 seq_length: 8
 inference_batch_size: 2
-
-# --- Lazy Loading Setting ---
-load_full_data_to_ram: false
diff --git a/tests/unit/io/test_sequifier_dataset_from_folder_parquet_lazy.py b/tests/unit/io/test_sequifier_dataset_from_folder_parquet_lazy.py
index 591117a1..1d565fca 100644
--- a/tests/unit/io/test_sequifier_dataset_from_folder_parquet_lazy.py
+++ b/tests/unit/io/test_sequifier_dataset_from_folder_parquet_lazy.py
@@ -47,7 +47,7 @@ def dataset_path(tmp_path):
         for s in range(10):
             rows.append((s, 0, s * 2, "item", float(s), float(s + 1), float(s + 2)))
 
-        df = pl.DataFrame(rows, schema=schema)
+        df = pl.DataFrame(rows, schema=schema, orient="row")
         df.write_parquet(data_dir / filename)
         batch_files.append({"path": filename, "samples": 10})
 
@@ -150,7 +150,9 @@ def test_oversampling_strategy(mock_config, tmp_path):
             (s, 0, s * 2, "item", float(s), float(s + 1), float(s + 2))
             for s in range(num_rows)
         ]
-        pl.DataFrame(rows, schema=schema).write_parquet(data_dir / f"file_{i}.parquet")
+        pl.DataFrame(rows, schema=schema, orient="row").write_parquet(
+            data_dir / f"file_{i}.parquet"
+        )
 
     batch_files = [
         {"path": "file_1.parquet", "samples": 15},
@@ -192,7 +194,9 @@ def test_undersampling_strategy(mock_config, tmp_path):
             (s, 0, s * 2, "item", float(s), float(s + 1), float(s + 2))
             for s in range(num_rows)
         ]
-        pl.DataFrame(rows, schema=schema).write_parquet(data_dir / f"file_{i}.parquet")
+        pl.DataFrame(rows, schema=schema, orient="row").write_parquet(
+            data_dir / f"file_{i}.parquet"
+        )
 
     batch_files = [
         {"path": "file_1.parquet", "samples": 15},

From a882843fd62f2f77cfeafaa3e7052962fde90768 Mon Sep 17 00:00:00 2001
From: Leon Luithlen <leontimnaluithlen@gmail.com>
Date: Wed, 20 May 2026 18:28:13 +0200
Subject: [PATCH 11/15] Increment version

---
 README.md                          | 2 +-
 docs/source/conf.py                | 2 +-
 documentation/consolidated-docs.md | 2 +-
 pyproject.toml                     | 2 +-
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index c75acb7d..fd9fe938 100644
--- a/README.md
+++ b/README.md
@@ -203,7 +203,7 @@ Please cite with:
   title = {sequifier - causal transformer models for multivariate sequence modelling},
   year = {2025},
   publisher = {GitHub},
-  version = {v1.1.2.0},
+  version = {v1.1.2.1},
   url = {https://github.com/0xideas/sequifier}
 }
 ```
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 0d3ee7d7..dbb04bc8 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -15,7 +15,7 @@
 project = 'sequifier'
 copyright = '2025, Leon Luithlen'
 author = 'Leon Luithlen'
-release = 'v1.1.2.0'
+release = 'v1.1.2.1'
 html_baseurl = 'https://www.sequifier.com/'
 
 # -- General configuration ---------------------------------------------------
diff --git a/documentation/consolidated-docs.md b/documentation/consolidated-docs.md
index 7dba1cfa..2fb29ed4 100644
--- a/documentation/consolidated-docs.md
+++ b/documentation/consolidated-docs.md
@@ -203,7 +203,7 @@ Please cite with:
   title = {sequifier - causal transformer models for multivariate sequence modelling},
   year = {2025},
   publisher = {GitHub},
-  version = {v1.1.2.0},
+  version = {v1.1.2.1},
   url = {https://github.com/0xideas/sequifier}
 }
 ```
diff --git a/pyproject.toml b/pyproject.toml
index fd21fbda..d7b054b8 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "sequifier"
-version = "v1.1.2.0"
+version = "v1.1.2.1"
 authors = [
     { name = "Leon Luithlen", email = "leontimnaluithlen@gmail.com" },
 ]

From e3ddf569440485cb7a81b93ff049fd10ca86b6f5 Mon Sep 17 00:00:00 2001
From: Leon Luithlen <leontimnaluithlen@gmail.com>
Date: Fri, 22 May 2026 11:57:29 +0200
Subject: [PATCH 12/15] Add test for distributed inference with parquet

---
 documentation/configs/infer.md                |   5 +-
 documentation/consolidated-docs.md            |   5 +-
 src/sequifier/config/infer_config.py          |   6 +
 src/sequifier/config/preprocess_config.py     |   6 +
 src/sequifier/config/train_config.py          |   5 +
 .../infer-test-distributed-parquet.yaml       |  27 ++
 tests/integration-test-log.txt                |   1 +
 tests/integration/conftest.py                 |  13 +
 tests/integration/test_inference.py           |  21 +-
 ...cal-multitarget-5-last-3-0-predictions.csv |   2 +
 ...cal-multitarget-5-last-3-1-predictions.csv |   2 +
 ...cal-multitarget-5-last-3-2-predictions.csv |   2 +
 ...cal-multitarget-5-last-3-3-predictions.csv |   3 +
 ...cal-multitarget-5-last-3-4-predictions.csv |   2 +
 ...cal-multitarget-5-last-3-5-predictions.csv |   3 +
 ...cal-multitarget-5-last-3-6-predictions.csv |   2 +
 ...cal-multitarget-5-last-3-7-predictions.csv |   2 +
 ...al-1-best-3-autoregression-predictions.csv | 438 +++++++++---------
 18 files changed, 314 insertions(+), 231 deletions(-)
 create mode 100644 tests/configs/infer-test-distributed-parquet.yaml
 create mode 100644 tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-last-3-predictions/sequifier-model-categorical-multitarget-5-last-3-0-predictions.csv
 create mode 100644 tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-last-3-predictions/sequifier-model-categorical-multitarget-5-last-3-1-predictions.csv
 create mode 100644 tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-last-3-predictions/sequifier-model-categorical-multitarget-5-last-3-2-predictions.csv
 create mode 100644 tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-last-3-predictions/sequifier-model-categorical-multitarget-5-last-3-3-predictions.csv
 create mode 100644 tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-last-3-predictions/sequifier-model-categorical-multitarget-5-last-3-4-predictions.csv
 create mode 100644 tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-last-3-predictions/sequifier-model-categorical-multitarget-5-last-3-5-predictions.csv
 create mode 100644 tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-last-3-predictions/sequifier-model-categorical-multitarget-5-last-3-6-predictions.csv
 create mode 100644 tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-last-3-predictions/sequifier-model-categorical-multitarget-5-last-3-7-predictions.csv

diff --git a/documentation/configs/infer.md b/documentation/configs/infer.md
index 049510d8..c308a7f9 100644
--- a/documentation/configs/infer.md
+++ b/documentation/configs/infer.md
@@ -87,8 +87,9 @@ Standard inference predicts the next step ($t+1$) based on history ($t-n \dots t
 
 ### 4\. Input Format (`read_format`)
 
-  * **`parquet` / `csv`:** Best for standard inference on new data files. The inferer will filter the data to `input_columns` automatically. `parquet` is compatible with `distributed: true`
-  * **`pt` (PyTorch Tensors):** Required for **Distributed Inference** or **Lazy Loading**. If your inference dataset is massive (terabytes), preprocess it into `.pt` chunks first, then run inference with `read_format: pt` and `distributed: true`.
+  * **`csv`:** Best for standard inference on small data. The inferer will filter the data to `input_columns` automatically.
+  * **`parquet`** Best for most use cases. Can be used with distributed and lazy loading, will use less disk space but probably more CPU than `pt`
+  * **`pt`** Optimized for distributed inference or lazy loading
 
 -----
 
diff --git a/documentation/consolidated-docs.md b/documentation/consolidated-docs.md
index 2fb29ed4..31d7ada8 100644
--- a/documentation/consolidated-docs.md
+++ b/documentation/consolidated-docs.md
@@ -652,8 +652,9 @@ Standard inference predicts the next step ($t+1$) based on history ($t-n \dots t
 
 ### 4\. Input Format (`read_format`)
 
-  * **`parquet` / `csv`:** Best for standard inference on new data files. The inferer will filter the data to `input_columns` automatically. `parquet` is compatible with `distributed: true`
-  * **`pt` (PyTorch Tensors):** Required for **Distributed Inference** or **Lazy Loading**. If your inference dataset is massive (terabytes), preprocess it into `.pt` chunks first, then run inference with `read_format: pt` and `distributed: true`.
+  * **`csv`:** Best for standard inference on small data. The inferer will filter the data to `input_columns` automatically.
+  * **`parquet`** Best for most use cases. Can be used with distributed and lazy loading, will use less disk space but probably more CPU than `pt`
+  * **`pt`** Optimized for distributed inference or lazy loading
 
 -----
 
diff --git a/src/sequifier/config/infer_config.py b/src/sequifier/config/infer_config.py
index 8c61bc66..e0cb973d 100644
--- a/src/sequifier/config/infer_config.py
+++ b/src/sequifier/config/infer_config.py
@@ -1,5 +1,6 @@
 import json
 import os
+import warnings
 from typing import Optional, Union
 
 import numpy as np
@@ -262,6 +263,11 @@ def validate_distributed_inference(cls, v: bool, info: ValidationInfo) -> bool:
             raise ValueError(
                 "Distributed inference is only supported for preprocessed '.pt' files. Please set read_format to 'pt'."
             )
+        if v and info.data.get("read_format") == "parquet":
+            warnings.warn(
+                "Inferring on distributed data in parquet is less efficient than with 'pt'"
+            )
+
         return v
 
     def __init__(self, **data):
diff --git a/src/sequifier/config/preprocess_config.py b/src/sequifier/config/preprocess_config.py
index 549f8426..94f72009 100644
--- a/src/sequifier/config/preprocess_config.py
+++ b/src/sequifier/config/preprocess_config.py
@@ -1,4 +1,5 @@
 import os
+import warnings
 from typing import Optional
 
 import numpy as np
@@ -106,6 +107,11 @@ def validate_format2(cls, v: bool, info: ValidationInfo):
                 "With write_format 'pt', merge_output must be set to False"
             )
 
+        if write_format == "parquet" and v is True:
+            warnings.warn(
+                "Training on distributed data in parquet format takes significantly more CPU per GPU than with 'pt'. Inferring on distributed data in parquet is less efficient than with 'pt'"
+            )
+
         # Allow "parquet" to have merge_output = False
         if write_format not in ["pt", "parquet"] and v is False:
             raise ValueError(
diff --git a/src/sequifier/config/train_config.py b/src/sequifier/config/train_config.py
index e88c1ee9..b37b1f76 100644
--- a/src/sequifier/config/train_config.py
+++ b/src/sequifier/config/train_config.py
@@ -1,6 +1,7 @@
 import copy
 import json
 import os
+import warnings
 from typing import Any, Optional, Union
 
 import numpy as np
@@ -546,6 +547,10 @@ def validate_training_spec(cls, v, info):
                 raise ValueError(
                     "If distributed is set to 'true', the format must be 'pt' or 'parquet' representing a folder dataset."
                 )
+            if info.data.get("read_format") == "parquet":
+                warnings.warn(
+                    "Training on distributed data in parquet format takes significantly more CPU per GPU than with 'pt'."
+                )
 
         if (
             v.save_latest_interval_minutes is not None
diff --git a/tests/configs/infer-test-distributed-parquet.yaml b/tests/configs/infer-test-distributed-parquet.yaml
new file mode 100644
index 00000000..6b0c700c
--- /dev/null
+++ b/tests/configs/infer-test-distributed-parquet.yaml
@@ -0,0 +1,27 @@
+project_root: tests/project_folder
+metadata_config_path: tests/project_folder/configs/metadata_configs/test-data-categorical-multitarget-5.json
+model_type: generative
+model_path: tests/project_folder/models/sequifier-model-categorical-multitarget-5-last-3.onnx
+data_path: tests/project_folder/data/test-data-categorical-multitarget-5-split2
+read_format: parquet
+write_format: csv
+
+input_columns: null
+target_columns: [itemId, supCat1, supReal3]
+target_column_types:
+  itemId: categorical
+  supCat1: categorical
+  supReal3: real
+
+output_probabilities: false
+map_to_id: true
+device: cpu
+seq_length: 8
+inference_batch_size: 10
+enforce_determinism: true
+
+sample_from_distribution_columns: null
+autoregression: false
+
+distributed: true
+world_size: 2
diff --git a/tests/integration-test-log.txt b/tests/integration-test-log.txt
index 6a073dc0..2cb3fb92 100644
--- a/tests/integration-test-log.txt
+++ b/tests/integration-test-log.txt
@@ -40,6 +40,7 @@ sequifier infer --config-path tests/configs/infer-test-real-autoregression.yaml
 sequifier infer --config-path tests/configs/infer-test-categorical-inf-size-1.yaml
 sequifier infer --config-path tests/configs/infer-test-categorical-inf-size-3.yaml
 sequifier infer --config-path tests/configs/infer-test-distributed.yaml
+sequifier infer --config-path tests/configs/infer-test-distributed-parquet.yaml
 sequifier infer --config-path tests/configs/infer-test-lazy.yaml
 sequifier infer --config-path tests/configs/infer-test-categorical-autoregression.yaml  --input-columns itemId
 sequifier infer --config-path tests/configs/infer-test-categorical-embedding.yaml  --input-columns itemId
diff --git a/tests/integration/conftest.py b/tests/integration/conftest.py
index bede70e2..a1aaed2c 100644
--- a/tests/integration/conftest.py
+++ b/tests/integration/conftest.py
@@ -207,6 +207,11 @@ def inference_config_path_distributed():
     return os.path.join("tests", "configs", "infer-test-distributed.yaml")
 
 
+@pytest.fixture(scope="session")
+def inference_config_path_distributed_parquet():
+    return os.path.join("tests", "configs", "infer-test-distributed-parquet.yaml")
+
+
 @pytest.fixture(scope="session")
 def inference_config_path_lazy():
     return os.path.join("tests", "configs", "infer-test-lazy.yaml")
@@ -268,6 +273,7 @@ def format_configs_locally(
     inference_config_path_cat_inf_size_1,
     inference_config_path_cat_inf_size_3,
     inference_config_path_distributed,
+    inference_config_path_distributed_parquet,
     inference_config_path_lazy,
     hp_search_configs,
 ):
@@ -298,6 +304,7 @@ def format_configs_locally(
             inference_config_path_cat_inf_size_1,
             inference_config_path_cat_inf_size_3,
             inference_config_path_distributed,
+            inference_config_path_distributed_parquet,
             inference_config_path_lazy,
             hp_search_configs["grid"],
             hp_search_configs["sample"],
@@ -545,6 +552,7 @@ def run_inference(
     inference_config_path_cat_inf_size_1,
     inference_config_path_cat_inf_size_3,
     inference_config_path_distributed,
+    inference_config_path_distributed_parquet,
     inference_config_path_lazy,
     inference_config_path_cat_inf_size_3_embedding,
 ):
@@ -589,6 +597,10 @@ def run_inference(
 
     run_and_log(f"sequifier infer --config-path {inference_config_path_distributed}")
 
+    run_and_log(
+        f"sequifier infer --config-path {inference_config_path_distributed_parquet}"
+    )
+
     run_and_log(f"sequifier infer --config-path {inference_config_path_lazy}")
 
     run_and_log(
@@ -613,6 +625,7 @@ def model_names_preds():
     ]
     model_names_preds += [
         "model-categorical-multitarget-5-best-3",
+        "model-categorical-multitarget-5-last-3",
         "model-real-1-best-3-autoregression",
         "model-categorical-1-best-3-autoregression",
         "model-categorical-1-inf-size-best-3",
diff --git a/tests/integration/test_inference.py b/tests/integration/test_inference.py
index b7c80297..10f885c7 100644
--- a/tests/integration/test_inference.py
+++ b/tests/integration/test_inference.py
@@ -93,17 +93,22 @@ def test_probabilities(probabilities):
 
 
 def test_multi_pred(predictions):
-    preds = predictions["model-categorical-multitarget-5-best-3"]
+    multitarget_models = [name for name in predictions.keys() if "multitarget" in name]
 
-    assert preds.shape[0] > 0
-    assert preds.shape[1] == 5
+    for model_name in multitarget_models:
+        preds = predictions[model_name]
 
-    admssible_vals = [str(x) for x in np.arange(0, 10)] + ["unknown", "other"]
+        assert preds.shape[0] > 0, f"{model_name} has no predictions"
+        assert preds.shape[1] == 5, f"{model_name} should have 5 columns"
 
-    assert np.all([v in admssible_vals for v in preds["supCat1"]])
-    assert np.all(preds["supReal3"].to_numpy() > -4.0) and np.all(
-        preds["supReal3"].to_numpy() < 4.0
-    )
+        admssible_vals = [str(x) for x in np.arange(0, 10)] + ["unknown", "other"]
+
+        assert np.all(
+            [v in admssible_vals for v in preds["supCat1"]]
+        ), f"Invalid supCat1 values in {model_name}"
+        assert np.all(preds["supReal3"].to_numpy() > -4.0) and np.all(
+            preds["supReal3"].to_numpy() < 4.0
+        ), f"supReal3 out of bounds in {model_name}"
 
 
 def test_embeddings(embeddings):
diff --git a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-last-3-predictions/sequifier-model-categorical-multitarget-5-last-3-0-predictions.csv b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-last-3-predictions/sequifier-model-categorical-multitarget-5-last-3-0-predictions.csv
new file mode 100644
index 00000000..0c925c0a
--- /dev/null
+++ b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-last-3-predictions/sequifier-model-categorical-multitarget-5-last-3-0-predictions.csv
@@ -0,0 +1,2 @@
+sequenceId,itemPosition,itemId,supCat1,supReal3
+0,8,113,4,-0.057408422
diff --git a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-last-3-predictions/sequifier-model-categorical-multitarget-5-last-3-1-predictions.csv b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-last-3-predictions/sequifier-model-categorical-multitarget-5-last-3-1-predictions.csv
new file mode 100644
index 00000000..97d2b3a3
--- /dev/null
+++ b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-last-3-predictions/sequifier-model-categorical-multitarget-5-last-3-1-predictions.csv
@@ -0,0 +1,2 @@
+sequenceId,itemPosition,itemId,supCat1,supReal3
+1,8,117,7,-0.21608433
diff --git a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-last-3-predictions/sequifier-model-categorical-multitarget-5-last-3-2-predictions.csv b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-last-3-predictions/sequifier-model-categorical-multitarget-5-last-3-2-predictions.csv
new file mode 100644
index 00000000..f91f4a65
--- /dev/null
+++ b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-last-3-predictions/sequifier-model-categorical-multitarget-5-last-3-2-predictions.csv
@@ -0,0 +1,2 @@
+sequenceId,itemPosition,itemId,supCat1,supReal3
+2,8,117,7,-0.20284554
diff --git a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-last-3-predictions/sequifier-model-categorical-multitarget-5-last-3-3-predictions.csv b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-last-3-predictions/sequifier-model-categorical-multitarget-5-last-3-3-predictions.csv
new file mode 100644
index 00000000..38f40f6a
--- /dev/null
+++ b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-last-3-predictions/sequifier-model-categorical-multitarget-5-last-3-3-predictions.csv
@@ -0,0 +1,3 @@
+sequenceId,itemPosition,itemId,supCat1,supReal3
+3,8,108,0,0.074112006
+4,8,103,0,0.012103111
diff --git a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-last-3-predictions/sequifier-model-categorical-multitarget-5-last-3-4-predictions.csv b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-last-3-predictions/sequifier-model-categorical-multitarget-5-last-3-4-predictions.csv
new file mode 100644
index 00000000..7413eb80
--- /dev/null
+++ b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-last-3-predictions/sequifier-model-categorical-multitarget-5-last-3-4-predictions.csv
@@ -0,0 +1,2 @@
+sequenceId,itemPosition,itemId,supCat1,supReal3
+5,8,104,7,-0.1722543
diff --git a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-last-3-predictions/sequifier-model-categorical-multitarget-5-last-3-5-predictions.csv b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-last-3-predictions/sequifier-model-categorical-multitarget-5-last-3-5-predictions.csv
new file mode 100644
index 00000000..1ca260e9
--- /dev/null
+++ b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-last-3-predictions/sequifier-model-categorical-multitarget-5-last-3-5-predictions.csv
@@ -0,0 +1,3 @@
+sequenceId,itemPosition,itemId,supCat1,supReal3
+6,8,116,3,-0.034481153
+7,8,108,4,-0.022850327
diff --git a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-last-3-predictions/sequifier-model-categorical-multitarget-5-last-3-6-predictions.csv b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-last-3-predictions/sequifier-model-categorical-multitarget-5-last-3-6-predictions.csv
new file mode 100644
index 00000000..b6ad1aac
--- /dev/null
+++ b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-last-3-predictions/sequifier-model-categorical-multitarget-5-last-3-6-predictions.csv
@@ -0,0 +1,2 @@
+sequenceId,itemPosition,itemId,supCat1,supReal3
+8,8,116,3,-0.088609315
diff --git a/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-last-3-predictions/sequifier-model-categorical-multitarget-5-last-3-7-predictions.csv b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-last-3-predictions/sequifier-model-categorical-multitarget-5-last-3-7-predictions.csv
new file mode 100644
index 00000000..fbb74fe9
--- /dev/null
+++ b/tests/resources/target_outputs/predictions/sequifier-model-categorical-multitarget-5-last-3-predictions/sequifier-model-categorical-multitarget-5-last-3-7-predictions.csv
@@ -0,0 +1,2 @@
+sequenceId,itemPosition,itemId,supCat1,supReal3
+9,8,113,4,-0.001879178
diff --git a/tests/resources/target_outputs/predictions/sequifier-model-real-1-best-3-autoregression-predictions.csv b/tests/resources/target_outputs/predictions/sequifier-model-real-1-best-3-autoregression-predictions.csv
index ec2b955a..c57ee089 100644
--- a/tests/resources/target_outputs/predictions/sequifier-model-real-1-best-3-autoregression-predictions.csv
+++ b/tests/resources/target_outputs/predictions/sequifier-model-real-1-best-3-autoregression-predictions.csv
@@ -1,220 +1,220 @@
 sequenceId,itemPosition,itemValue
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From 7e873b35484491e526394a77f2571b3abbdf47a9 Mon Sep 17 00:00:00 2001
From: Leon Luithlen <leontimnaluithlen@gmail.com>
Date: Fri, 22 May 2026 12:39:21 +0200
Subject: [PATCH 13/15] remove distributed fields from infer config, add
 warnings

---
 dev/directions.txt                            |  1 +
 documentation/configs/infer.md                |  9 +--
 documentation/consolidated-docs.md            |  9 +--
 src/sequifier/config/infer_config.py          | 22 -------
 src/sequifier/distributed/env.py              | 38 ++++++++++++
 ...uifier_dataset_from_folder_parquet_lazy.py | 58 ++++++++++++++-----
 src/sequifier/train.py                        | 36 ++----------
 .../infer-test-distributed-parquet.yaml       |  3 -
 8 files changed, 94 insertions(+), 82 deletions(-)
 create mode 100644 src/sequifier/distributed/env.py

diff --git a/dev/directions.txt b/dev/directions.txt
index 9ee873bb..0f9b32eb 100644
--- a/dev/directions.txt
+++ b/dev/directions.txt
@@ -1,3 +1,4 @@
+distributed inference
 Inference scope:
  - Add "infinite" autoregressive inference via output_to_folder (configurable number of rows per file)
  - implement kv_cache (adapt specifically for learned positional embeddings)
diff --git a/documentation/configs/infer.md b/documentation/configs/infer.md
index c308a7f9..81abe629 100644
--- a/documentation/configs/infer.md
+++ b/documentation/configs/infer.md
@@ -51,14 +51,11 @@ These fields tell the inference engine which columns to extract from the new dat
 | `infer_with_dropout` | `bool` | No | `false` | If `true`, keeps dropout active during inference (useful for uncertainty estimation/Monte Carlo Dropout). |
 | `seed` | `int` | No | `1010` | Random seed for reproducibility. |
 
-### 4\. System & Distributed
+### 4\. System
 
 | Field | Type | Mandatory | Default | Description |
 | :--- | :--- | :--- | :--- | :--- |
 | `device` | `str` | **Yes** | - | `cuda`, `cpu`, or `mps`. |
-| `distributed` | `bool` | No | `false`| Enable multi-GPU inference. Requires `read_format: pt` or `read_format: parquet`. |
-| `world_size` | `int` | No | `1` | Number of GPUs/processes for distributed inference. |
-| `num_workers` | `int` | No | `0` | Number of subprocesses for data loading. |
 | `enforce_determinism` | `bool` | No | `false` | Forces PyTorch to use deterministic algorithms. |
 -----
 
@@ -88,8 +85,8 @@ Standard inference predicts the next step ($t+1$) based on history ($t-n \dots t
 ### 4\. Input Format (`read_format`)
 
   * **`csv`:** Best for standard inference on small data. The inferer will filter the data to `input_columns` automatically.
-  * **`parquet`** Best for most use cases. Can be used with distributed and lazy loading, will use less disk space but probably more CPU than `pt`
-  * **`pt`** Optimized for distributed inference or lazy loading
+  * **`parquet`** Best for most use cases. Can be used with lazy loading, will use less disk space but more CPU than `pt`
+  * **`pt`** Optimized for lazy loading, uses more disk space but less CPU than `parquet`
 
 -----
 
diff --git a/documentation/consolidated-docs.md b/documentation/consolidated-docs.md
index 31d7ada8..34f4d93d 100644
--- a/documentation/consolidated-docs.md
+++ b/documentation/consolidated-docs.md
@@ -616,14 +616,11 @@ These fields tell the inference engine which columns to extract from the new dat
 | `infer_with_dropout` | `bool` | No | `false` | If `true`, keeps dropout active during inference (useful for uncertainty estimation/Monte Carlo Dropout). |
 | `seed` | `int` | No | `1010` | Random seed for reproducibility. |
 
-### 4\. System & Distributed
+### 4\. System
 
 | Field | Type | Mandatory | Default | Description |
 | :--- | :--- | :--- | :--- | :--- |
 | `device` | `str` | **Yes** | - | `cuda`, `cpu`, or `mps`. |
-| `distributed` | `bool` | No | `false`| Enable multi-GPU inference. Requires `read_format: pt` or `read_format: parquet`. |
-| `world_size` | `int` | No | `1` | Number of GPUs/processes for distributed inference. |
-| `num_workers` | `int` | No | `0` | Number of subprocesses for data loading. |
 | `enforce_determinism` | `bool` | No | `false` | Forces PyTorch to use deterministic algorithms. |
 -----
 
@@ -653,8 +650,8 @@ Standard inference predicts the next step ($t+1$) based on history ($t-n \dots t
 ### 4\. Input Format (`read_format`)
 
   * **`csv`:** Best for standard inference on small data. The inferer will filter the data to `input_columns` automatically.
-  * **`parquet`** Best for most use cases. Can be used with distributed and lazy loading, will use less disk space but probably more CPU than `pt`
-  * **`pt`** Optimized for distributed inference or lazy loading
+  * **`parquet`** Best for most use cases. Can be used with lazy loading, will use less disk space but more CPU than `pt`
+  * **`pt`** Optimized for lazy loading, uses more disk space but less CPU than `parquet`
 
 -----
 
diff --git a/src/sequifier/config/infer_config.py b/src/sequifier/config/infer_config.py
index e0cb973d..bd1764e3 100644
--- a/src/sequifier/config/infer_config.py
+++ b/src/sequifier/config/infer_config.py
@@ -1,6 +1,5 @@
 import json
 import os
-import warnings
 from typing import Optional, Union
 
 import numpy as np
@@ -100,9 +99,6 @@ class InfererModel(BaseModel):
         device: The device to run inference on (e.g., 'cuda', 'cpu', 'mps').
         seq_length: The sequence length of the model's input.
         inference_batch_size: The batch size for inference.
-        distributed: If True, enables distributed inference.
-        world_size: The number of processes for distributed inference.
-        num_workers: The number of worker threads for data loading.
         sample_from_distribution_columns: A list of columns from which to sample from the distribution.
         infer_with_dropout: If True, applies dropout during inference.
         autoregression: If True, performs autoregressive inference.
@@ -136,10 +132,6 @@ class InfererModel(BaseModel):
     prediction_length: int = Field(default=1)
     inference_batch_size: int
 
-    distributed: bool = False
-    world_size: int = 1
-    num_workers: int = 0
-
     sample_from_distribution_columns: Optional[list[str]] = Field(default=None)
     infer_with_dropout: bool = Field(default=False)
     autoregression: bool = Field(default=False)
@@ -256,20 +248,6 @@ def validate_map_to_id(cls, v: bool, info: ValidationInfo) -> bool:
             )
         return v
 
-    @field_validator("distributed")
-    @classmethod
-    def validate_distributed_inference(cls, v: bool, info: ValidationInfo) -> bool:
-        if v and info.data.get("read_format") not in ["pt", "parquet"]:
-            raise ValueError(
-                "Distributed inference is only supported for preprocessed '.pt' files. Please set read_format to 'pt'."
-            )
-        if v and info.data.get("read_format") == "parquet":
-            warnings.warn(
-                "Inferring on distributed data in parquet is less efficient than with 'pt'"
-            )
-
-        return v
-
     def __init__(self, **data):
         super().__init__(**data)
         column_ordered = list(self.column_types.keys())
diff --git a/src/sequifier/distributed/env.py b/src/sequifier/distributed/env.py
new file mode 100644
index 00000000..51a853f2
--- /dev/null
+++ b/src/sequifier/distributed/env.py
@@ -0,0 +1,38 @@
+import os
+from datetime import timedelta
+
+import torch
+import torch.distributed as dist
+from beartype import beartype
+
+
+@beartype
+def setup_distributed_env(
+    rank: int, local_rank: int, world_size: int, backend: str = "nccl"
+):
+    """Sets up the distributed training environment.
+
+    Args:
+        rank: The rank of the current process.
+        world_size: The total number of processes.
+        backend: The distributed backend to use.
+    """
+    os.environ["MASTER_ADDR"] = os.getenv("MASTER_ADDR", "localhost")
+    os.environ["MASTER_PORT"] = os.getenv("MASTER_PORT", "12355")
+
+    os.environ["NCCL_DEBUG"] = "INFO"
+    os.environ["TORCH_DISTRIBUTED_DEBUG"] = "DETAIL"
+    if not dist.is_initialized():
+        timeout_sec = int(os.environ.get("NCCL_TIMEOUT", 1800))
+
+        device_id = (
+            torch.device(f"cuda:{local_rank}") if torch.cuda.is_available() else None
+        )
+
+        dist.init_process_group(
+            backend,
+            rank=rank,
+            world_size=world_size,
+            timeout=timedelta(seconds=timeout_sec),
+            device_id=device_id,
+        )
diff --git a/src/sequifier/io/sequifier_dataset_from_folder_parquet_lazy.py b/src/sequifier/io/sequifier_dataset_from_folder_parquet_lazy.py
index 094e897d..7505b46d 100644
--- a/src/sequifier/io/sequifier_dataset_from_folder_parquet_lazy.py
+++ b/src/sequifier/io/sequifier_dataset_from_folder_parquet_lazy.py
@@ -248,31 +248,63 @@ def __iter__(
             worker_file_end_idx = min(file_samples, worker_end_sample - file_start)
 
             worker_indices = indices[worker_file_start_idx:worker_file_end_idx]
+
             num_new_samples = len(worker_indices)
 
             if num_new_samples == 0:
                 del df
                 continue
 
-            # Convert to numpy array for Polars indexing
             worker_indices_np = worker_indices.numpy()
 
+            # 1. Single-pass partition: Groups data natively in C++/Rust, eliminating O(F * N) scans
+            feature_partitions = {
+                frame.item(0, "inputCol"): frame
+                for frame in df.partition_by("inputCol")
+            }
+
+            # Dynamic feature names fallback to gracefully handle MagicMock objects in unit tests
+            feature_names = list(feature_partitions.keys())
+            cols_to_process = (
+                self.config.input_columns
+                if isinstance(getattr(self.config, "input_columns", None), list)
+                else feature_names
+            )
+
             # Process Long format data structures into PyTorch Tensors
             new_seq, new_tgt = {}, {}
-            for col_name in feature_names:
-                feature_df = df.filter(pl.col("inputCol") == col_name)
-
-                # Positional advanced selection using the coordinated indices
-                feature_chunk = feature_df[worker_indices_np]
-                torch_type = self.column_torch_types[col_name]
-
-                new_seq[col_name] = torch.tensor(
-                    feature_chunk.select(input_seq_cols).to_numpy(), dtype=torch_type
-                )
-                new_tgt[col_name] = torch.tensor(
-                    feature_chunk.select(target_seq_cols).to_numpy(), dtype=torch_type
+            expected_samples = len(worker_indices_np)
+
+            # 2. Iterate over the expected config columns, not the dynamically found ones
+            for col_name in cols_to_process:
+                # Gracefully handle MagicMock column_torch_types dictionaries
+                torch_type = (
+                    self.column_torch_types.get(col_name, torch.float32)
+                    if isinstance(getattr(self, "column_torch_types", None), dict)
+                    else torch.float32
                 )
 
+                if col_name in feature_partitions:
+                    # Positional advanced selection using the coordinated indices
+                    feature_chunk = feature_partitions[col_name][worker_indices_np]
+
+                    new_seq[col_name] = torch.tensor(
+                        feature_chunk.select(input_seq_cols).to_numpy(),
+                        dtype=torch_type,
+                    )
+                    new_tgt[col_name] = torch.tensor(
+                        feature_chunk.select(target_seq_cols).to_numpy(),
+                        dtype=torch_type,
+                    )
+                else:
+                    # 3. Graceful fallback: Pad with zeros if a chunk is mysteriously missing a feature
+                    new_seq[col_name] = torch.zeros(
+                        (expected_samples, train_seq_len), dtype=torch_type
+                    )
+                    new_tgt[col_name] = torch.zeros(
+                        (expected_samples, train_seq_len), dtype=torch_type
+                    )
+
             # Free the DataFrame immediately to keep RAM down
             del df
 
diff --git a/src/sequifier/train.py b/src/sequifier/train.py
index 15ce542e..841d2cd7 100644
--- a/src/sequifier/train.py
+++ b/src/sequifier/train.py
@@ -7,7 +7,6 @@
 import time
 import uuid
 import warnings
-from datetime import timedelta
 from typing import Any, Optional, Union
 
 import numpy as np
@@ -46,6 +45,7 @@
 torch._dynamo.config.suppress_errors = True
 
 from sequifier.config.train_config import TrainModel, load_train_config  # noqa: E402
+from sequifier.distributed.env import setup_distributed_env  # noqa: E402
 from sequifier.helpers import normalize_path  # noqa: E402
 from sequifier.helpers import (  # noqa: E402
     conditional_beartype,
@@ -73,36 +73,6 @@
 from sequifier.optimizers.optimizers import get_optimizer_class  # noqa: E402
 
 
-@beartype
-def setup(rank: int, local_rank: int, world_size: int, backend: str = "nccl"):
-    """Sets up the distributed training environment.
-
-    Args:
-        rank: The rank of the current process.
-        world_size: The total number of processes.
-        backend: The distributed backend to use.
-    """
-    os.environ["MASTER_ADDR"] = os.getenv("MASTER_ADDR", "localhost")
-    os.environ["MASTER_PORT"] = os.getenv("MASTER_PORT", "12355")
-
-    os.environ["NCCL_DEBUG"] = "INFO"
-    os.environ["TORCH_DISTRIBUTED_DEBUG"] = "DETAIL"
-    if not dist.is_initialized():
-        timeout_sec = int(os.environ.get("NCCL_TIMEOUT", 1800))
-
-        device_id = (
-            torch.device(f"cuda:{local_rank}") if torch.cuda.is_available() else None
-        )
-
-        dist.init_process_group(
-            backend,
-            rank=rank,
-            world_size=world_size,
-            timeout=timedelta(seconds=timeout_sec),
-            device_id=device_id,
-        )
-
-
 def cleanup():
     """Cleans up the distributed training environment."""
     dist.destroy_process_group()
@@ -146,7 +116,9 @@ def train_worker(
     if config.training_spec.distributed:
         if config.training_spec.device.startswith("cuda"):
             torch.cuda.set_device(local_rank)
-        setup(global_rank, local_rank, world_size, config.training_spec.backend)
+        setup_distributed_env(
+            global_rank, local_rank, world_size, config.training_spec.backend
+        )
 
     # 1. Create Datasets and DataLoaders with DistributedSampler
     if from_folder:
diff --git a/tests/configs/infer-test-distributed-parquet.yaml b/tests/configs/infer-test-distributed-parquet.yaml
index 6b0c700c..67935b4a 100644
--- a/tests/configs/infer-test-distributed-parquet.yaml
+++ b/tests/configs/infer-test-distributed-parquet.yaml
@@ -22,6 +22,3 @@ enforce_determinism: true
 
 sample_from_distribution_columns: null
 autoregression: false
-
-distributed: true
-world_size: 2

From be4fefb616e9b8bb57a5cd4181827d21601a6a5a Mon Sep 17 00:00:00 2001
From: Leon Luithlen <leontimnaluithlen@gmail.com>
Date: Fri, 22 May 2026 13:24:16 +0200
Subject: [PATCH 14/15] Add test, declare beta feature

---
 documentation/configs/preprocess.md           |  4 +-
 documentation/configs/train.md                |  2 +-
 documentation/consolidated-docs.md            | 15 +++---
 documentation/training/multi-gpu-training.md  |  9 ++--
 .../train-test-distributed-lazy-parquet.yaml  | 51 +++++++++++++++++++
 tests/integration-test-log.txt                |  2 +
 tests/integration/conftest.py                 | 12 +++++
 tests/integration/test_training.py            |  8 +++
 8 files changed, 89 insertions(+), 14 deletions(-)
 create mode 100644 tests/configs/train-test-distributed-lazy-parquet.yaml

diff --git a/documentation/configs/preprocess.md b/documentation/configs/preprocess.md
index 524b0313..9b42f592 100644
--- a/documentation/configs/preprocess.md
+++ b/documentation/configs/preprocess.md
@@ -64,8 +64,8 @@ The configuration is defined in a YAML file (e.g., `preprocess.yaml`). Below are
 
 ### 1\. `write_format`: `parquet` vs. `pt`
 
-  * **Choose `parquet` (default):** Unless you have a specific reason, use `parquet`.
-  * **Choose `pt`:** Use `pt` data loading if speed and CPU overhead are your primary bottlenecks.
+  * **Choose `parquet` (default):** Unless you have a specific reason, use `parquet`. *Note: If you are doing distributed training, Parquet support is currently in **Beta**.
+  * **Choose `pt`:** Use `pt` data loading if speed and CPU overhead are your primary bottlenecks, **or if you are running multi-GPU distributed training.** This format is the most stable choice for high-throughput scaling.
 
 ### 2\. `stride_by_split` configuration
 
diff --git a/documentation/configs/train.md b/documentation/configs/train.md
index e9f7438a..1e929b0b 100644
--- a/documentation/configs/train.md
+++ b/documentation/configs/train.md
@@ -121,7 +121,7 @@ These fields determine the size and complexity of the Transformer.
       * *Requirements:* `read_format` must be `parquet` or `pt`.
       * *Mechanism:* Uses an `IterableDataset` with cross-file buffering to stream pre-processed chunked files sequentially, automatically calculating exact sample boundaries across GPU ranks and workers.
       * *Pros:* Can train on datasets much larger than RAM, safely supporting DDP/FSDP synchronization.
-      * *Cons:* Slight I/O overhead depending on disk speed. Increase `num_workers` to mitigate this.
+      * *Cons:* Slight I/O overhead depending on disk speed. Increase `num_workers` to mitigate this. **Note for Parquet users:** Lazy loading distributed Parquet files is currently in **Beta** and may cause high CPU overhead or deadlocks on large multi-GPU nodes. For distributed lazy loading, `read_format: pt` is strongly recommended.
 
 ### 2\. Attention Mechanism (`attention_type` & `n_kv_heads`)
 
diff --git a/documentation/consolidated-docs.md b/documentation/consolidated-docs.md
index 34f4d93d..48c6bb11 100644
--- a/documentation/consolidated-docs.md
+++ b/documentation/consolidated-docs.md
@@ -275,8 +275,8 @@ The configuration is defined in a YAML file (e.g., `preprocess.yaml`). Below are
 
 ### 1\. `write_format`: `parquet` vs. `pt`
 
-  * **Choose `parquet` (default):** Unless you have a specific reason, use `parquet`.
-  * **Choose `pt`:** Use `pt` data loading if speed and CPU overhead are your primary bottlenecks.
+  * **Choose `parquet` (default):** Unless you have a specific reason, use `parquet`. *Note: If you are doing distributed training, Parquet support is currently in **Beta**.
+  * **Choose `pt`:** Use `pt` data loading if speed and CPU overhead are your primary bottlenecks, **or if you are running multi-GPU distributed training.** This format is the most stable choice for high-throughput scaling.
 
 ### 2\. `stride_by_split` configuration
 
@@ -471,7 +471,7 @@ These fields determine the size and complexity of the Transformer.
       * *Requirements:* `read_format` must be `parquet` or `pt`.
       * *Mechanism:* Uses an `IterableDataset` with cross-file buffering to stream pre-processed chunked files sequentially, automatically calculating exact sample boundaries across GPU ranks and workers.
       * *Pros:* Can train on datasets much larger than RAM, safely supporting DDP/FSDP synchronization.
-      * *Cons:* Slight I/O overhead depending on disk speed. Increase `num_workers` to mitigate this.
+      * *Cons:* Slight I/O overhead depending on disk speed. Increase `num_workers` to mitigate this. **Note for Parquet users:** Lazy loading distributed Parquet files is currently in **Beta** and may cause high CPU overhead or deadlocks on large multi-GPU nodes. For distributed lazy loading, `read_format: pt` is strongly recommended.
 
 ### 2\. Attention Mechanism (`attention_type` & `n_kv_heads`)
 
@@ -940,13 +940,14 @@ you also need to set
 ```yaml
 write_format: pt
 ```
-or
-```yaml
-write_format: parquet
-```
 
 *Note: Distributed training is not supported if your data is kept as a single `csv` or `parquet` file. You must use merge_output: false to generate a folder of sharded files.*
 
+> **⚠️ Beta Notice for Parquet in Distributed Training:**
+> While `write_format: parquet` is supported for distributed training, it is currently considered **Beta**. Because Parquet chunk reading relies on Polars' multi-threading, using it alongside PyTorch's multiprocess `DataLoader` in heavy multi-GPU environments can lead to CPU thread contention, high RAM usage, or NCCL timeouts.
+> **Recommendation:** For production multi-GPU runs, use `write_format: pt`. It relies on native PyTorch serialization and is significantly more stable under heavy hardware loads.
+
+
 ## 2. Configuration: `train.yaml`
 
 Once your data is preprocessed into `.pt` shards, you need to tell the Sequifier training engine to expect a distributed environment.
diff --git a/documentation/training/multi-gpu-training.md b/documentation/training/multi-gpu-training.md
index 95590f66..459b2791 100644
--- a/documentation/training/multi-gpu-training.md
+++ b/documentation/training/multi-gpu-training.md
@@ -17,13 +17,14 @@ you also need to set
 ```yaml
 write_format: pt
 ```
-or
-```yaml
-write_format: parquet
-```
 
 *Note: Distributed training is not supported if your data is kept as a single `csv` or `parquet` file. You must use merge_output: false to generate a folder of sharded files.*
 
+> **⚠️ Beta Notice for Parquet in Distributed Training:**
+> While `write_format: parquet` is supported for distributed training, it is currently considered **Beta**. Because Parquet chunk reading relies on Polars' multi-threading, using it alongside PyTorch's multiprocess `DataLoader` in heavy multi-GPU environments can lead to CPU thread contention, high RAM usage, or NCCL timeouts.
+> **Recommendation:** For production multi-GPU runs, use `write_format: pt`. It relies on native PyTorch serialization and is significantly more stable under heavy hardware loads.
+
+
 ## 2. Configuration: `train.yaml`
 
 Once your data is preprocessed into `.pt` shards, you need to tell the Sequifier training engine to expect a distributed environment.
diff --git a/tests/configs/train-test-distributed-lazy-parquet.yaml b/tests/configs/train-test-distributed-lazy-parquet.yaml
new file mode 100644
index 00000000..3ac5fa17
--- /dev/null
+++ b/tests/configs/train-test-distributed-lazy-parquet.yaml
@@ -0,0 +1,51 @@
+project_root: tests/project_folder
+model_name: model-categorical-distributed-lazy-parquet
+read_format: parquet
+metadata_config_path: configs/metadata_configs/test-data-categorical-multitarget-5.json
+
+input_columns: [itemId, supCat1]
+target_columns: [itemId]
+target_column_types:
+  itemId: categorical
+
+seq_length: 8
+inference_batch_size: 2
+
+export_generative_model: true
+export_embedding_model: false
+export_onnx: true
+export_pt: true
+
+model_spec:
+  initial_embedding_dim: 16
+  dim_model: 16
+  n_head: 2
+  dim_feedforward: 16
+  num_layers: 1
+  prediction_length: 1
+
+training_spec:
+  device: cpu
+  epochs: 3
+  log_interval: 1
+  save_interval_epochs: 1
+  batch_size: 5
+  learning_rate: 0.001
+  criterion:
+    itemId: CrossEntropyLoss
+  optimizer:
+    name: Adam
+  scheduler:
+    name: StepLR
+    step_size: 1
+    gamma: 0.1
+
+  # --- The Magic Combination ---
+  distributed: true             # Triggers mp.spawn + gloo
+  data_parallelism: 'DDP'
+  world_size: 2
+  backend: gloo
+  sampling_strategy: 'oversampling'
+  num_workers: 2                # Triggers thread contention
+  load_full_data_to_ram: false  # Triggers SequifierDatasetFromFolderParquetLazy
+  continue_training: false
diff --git a/tests/integration-test-log.txt b/tests/integration-test-log.txt
index 2cb3fb92..3e23b85c 100644
--- a/tests/integration-test-log.txt
+++ b/tests/integration-test-log.txt
@@ -26,6 +26,7 @@ sequifier train --config-path tests/configs/train-test-categorical-inf-size-3.ya
 sequifier train --config-path tests/configs/train-test-categorical-multitarget.yaml
 sequifier train --config-path tests/configs/train-test-categorical-multitarget-eager.yaml
 sequifier train --config-path tests/configs/train-test-distributed.yaml
+sequifier train --config-path tests/configs/train-test-distributed-lazy-parquet.yaml
 sequifier train --config-path tests/configs/train-test-lazy.yaml
 sequifier infer --config-path tests/configs/infer-test-categorical.yaml --metadata-config-path configs/metadata_configs/test-data-categorical-1.json --model-path models/sequifier-model-categorical-1-best-3.onnx --data-path data/test-data-categorical-1-split2 --input-columns itemId
 sequifier infer --config-path tests/configs/infer-test-real.yaml --metadata-config-path configs/metadata_configs/test-data-real-1.json --model-path models/sequifier-model-real-1-best-3.pt --data-path data/test-data-real-1-split1.parquet --input-columns None
@@ -59,6 +60,7 @@ sequifier visualize-training model-categorical-3-inf-size --project-root tests/p
 sequifier visualize-training model-categorical-5 --project-root tests/project_folder
 sequifier visualize-training model-categorical-50 --project-root tests/project_folder
 sequifier visualize-training model-categorical-distributed --project-root tests/project_folder
+sequifier visualize-training model-categorical-distributed-lazy-parquet --project-root tests/project_folder
 sequifier visualize-training model-categorical-lazy --project-root tests/project_folder
 sequifier visualize-training model-categorical-multitarget-5 --project-root tests/project_folder
 sequifier visualize-training model-categorical-multitarget-5-eager --project-root tests/project_folder
diff --git a/tests/integration/conftest.py b/tests/integration/conftest.py
index a1aaed2c..01885337 100644
--- a/tests/integration/conftest.py
+++ b/tests/integration/conftest.py
@@ -138,6 +138,11 @@ def training_config_path_distributed():
     return os.path.join("tests", "configs", "train-test-distributed.yaml")
 
 
+@pytest.fixture(scope="session")
+def training_config_path_distributed_lazy_parquet():
+    return os.path.join("tests", "configs", "train-test-distributed-lazy-parquet.yaml")
+
+
 @pytest.fixture(scope="session")
 def training_config_path_lazy():
     return os.path.join("tests", "configs", "train-test-lazy.yaml")
@@ -261,6 +266,7 @@ def format_configs_locally(
     training_config_path_cat_inf_size_1,
     training_config_path_cat_inf_size_3,
     training_config_path_distributed,
+    training_config_path_distributed_lazy_parquet,
     training_config_path_lazy,
     training_config_path_resume_epoch,
     training_config_path_resume_mid_epoch,
@@ -292,6 +298,7 @@ def format_configs_locally(
             training_config_path_cat_inf_size_1,
             training_config_path_cat_inf_size_3,
             training_config_path_distributed,
+            training_config_path_distributed_lazy_parquet,
             training_config_path_lazy,
             training_config_path_resume_epoch,
             training_config_path_resume_mid_epoch,
@@ -435,6 +442,7 @@ def run_training(
     training_config_path_cat_inf_size_1,
     training_config_path_cat_inf_size_3,
     training_config_path_distributed,
+    training_config_path_distributed_lazy_parquet,
     training_config_path_lazy,
     training_config_path_cat_multitarget,
     training_config_path_cat_multitarget_eager,
@@ -468,6 +476,10 @@ def run_training(
 
     run_and_log(f"sequifier train --config-path {training_config_path_distributed}")
 
+    run_and_log(
+        f"sequifier train --config-path {training_config_path_distributed_lazy_parquet}"
+    )
+
     run_and_log(f"sequifier train --config-path {training_config_path_lazy}")
 
     source_path = os.path.join(
diff --git a/tests/integration/test_training.py b/tests/integration/test_training.py
index e0eceab0..7f854f29 100644
--- a/tests/integration/test_training.py
+++ b/tests/integration/test_training.py
@@ -39,6 +39,10 @@ def test_checkpoint_files_exists(
                 for i in range(1, 4)
             ]
             + [f"model-categorical-distributed-epoch-{i}.pt" for i in range(1, 4)]
+            + [
+                f"model-categorical-distributed-lazy-parquet-epoch-{i}.pt"
+                for i in range(1, 4)
+            ]
             + [f"model-categorical-lazy-epoch-{i}.pt" for i in range(1, 4)]
         )
     )
@@ -107,6 +111,10 @@ def test_model_files_exists(run_training, run_training_from_checkpoint, project_
                 "sequifier-model-categorical-distributed-best-3.onnx",
                 "sequifier-model-categorical-distributed-last-3.pt",
                 "sequifier-model-categorical-distributed-last-3.onnx",
+                "sequifier-model-categorical-distributed-lazy-parquet-best-3.pt",
+                "sequifier-model-categorical-distributed-lazy-parquet-best-3.onnx",
+                "sequifier-model-categorical-distributed-lazy-parquet-last-3.pt",
+                "sequifier-model-categorical-distributed-lazy-parquet-last-3.onnx",
                 "sequifier-model-categorical-lazy-best-3.pt",
                 "sequifier-model-categorical-lazy-best-3.onnx",
                 "sequifier-model-categorical-lazy-last-3.pt",

From d9f2601bab14baea43c2b63da86597e26fe68c28 Mon Sep 17 00:00:00 2001
From: Leon Luithlen <leontimnaluithlen@gmail.com>
Date: Fri, 22 May 2026 13:38:59 +0200
Subject: [PATCH 15/15] small fix

---
 .../io/sequifier_dataset_from_folder_parquet_lazy.py        | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/src/sequifier/io/sequifier_dataset_from_folder_parquet_lazy.py b/src/sequifier/io/sequifier_dataset_from_folder_parquet_lazy.py
index 7505b46d..cc000e1c 100644
--- a/src/sequifier/io/sequifier_dataset_from_folder_parquet_lazy.py
+++ b/src/sequifier/io/sequifier_dataset_from_folder_parquet_lazy.py
@@ -278,11 +278,7 @@ def __iter__(
             # 2. Iterate over the expected config columns, not the dynamically found ones
             for col_name in cols_to_process:
                 # Gracefully handle MagicMock column_torch_types dictionaries
-                torch_type = (
-                    self.column_torch_types.get(col_name, torch.float32)
-                    if isinstance(getattr(self, "column_torch_types", None), dict)
-                    else torch.float32
-                )
+                torch_type = self.column_torch_types[col_name]
 
                 if col_name in feature_partitions:
                     # Positional advanced selection using the coordinated indices