Merge pull request #171 from wilhelm-lab/patch/retry_mechanism

Patch/retry mechanism

.cookietemple.yml

@@ -15,5 +15,5 @@ full_name: Victor Giurcoiu
 email: victor.giurcoiu@tum.de
 project_name: oktoberfest
 project_short_description: Public repo oktoberfest
-version: 0.5.2
+version: 0.5.3
 license: MIT

.github/release-drafter.yml

@@ -1,5 +1,5 @@
-name-template: "0.5.2 🌈" # <<COOKIETEMPLE_FORCE_BUMP>>
-tag-template: 0.5.2 # <<COOKIETEMPLE_FORCE_BUMP>>
+name-template: "0.5.3 🌈" # <<COOKIETEMPLE_FORCE_BUMP>>
+tag-template: 0.5.3 # <<COOKIETEMPLE_FORCE_BUMP>>
 exclude-labels:
     - "skip-changelog"
 

.github/workflows/sync_project.yml

@@ -25,9 +25,9 @@ jobs:
 
             - uses: oleksiyrudenko/gha-git-credentials@v2.1
               with:
-                  name: "victorgiurcoiu"
-                  email: "victor.giurcoiu@tum.de"
-                  actor: "victorgiurcoiu"
+                  name: "Mario Picciani"
+                  email: "mario.picciani@tum.de"
+                  actor: "picciama"
                   token: "${{ secrets.CT_SYNC_TOKEN}}"
 
             - name: Sync project

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2022, Victor Giurcoiu
+Copyright (c) 2023, Wilhelmlab at Technical University of Munich
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

cookietemple.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.5.2
+current_version = 0.5.3
 
 [bumpversion_files_whitelisted]
 init_file = oktoberfest/__init__.py

docs/conf.py

@@ -54,9 +54,9 @@
 # the built documents.
 #
 # The short X.Y version.
-version = "0.5.2"
+version = "0.5.3"
 # The full version, including alpha/beta/rc tags.
-release = "0.5.2"
+release = "0.5.3"
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.

oktoberfest/__init__.py

@@ -5,7 +5,7 @@
 __author__ = """The Oktoberfest development team (Wilhelmlab at Technical University of Munich)"""
 __copyright__ = f"Copyright {datetime.now():%Y}, Wilhelmlab at Technical University of Munich"
 __license__ = "MIT"
-__version__ = "0.5.2"
+__version__ = "0.5.3"
 
 import logging.handlers
 import sys

oktoberfest/predict/koina.py

@@ -361,7 +361,7 @@ def __async_callback(
         self,
         infer_results: Dict[int, Union[InferResult, InferenceServerException]],
         request_id: int,
-        result: InferResult,
+        result: Optional[InferResult],
         error: Optional[InferenceServerException],
     ):
         """
@@ -408,7 +408,13 @@ def __async_predict_batch(
         batch_outputs = self.__get_batch_outputs(self.model_outputs.keys())
         batch_inputs = self.__get_batch_inputs(data)
 
-        for _ in range(retries):
+        for i in range(retries):
+            if i > 0:  # need to yield first, before doing sth, but only after first time
+                yield
+                if isinstance(infer_results.get(request_id), InferResult):
+                    break
+                del infer_results[request_id]  # avoid race condition in case inference is slower than tqdm loop
+
             self.client.async_infer(
                 model_name=self.model_name,
                 request_id=str(request_id),
@@ -417,9 +423,6 @@ def __async_predict_batch(
                 outputs=batch_outputs,
                 client_timeout=timeout,
             )
-            yield
-            if isinstance(infer_results.get(request_id), InferResult):
-                break
 
     def predict(
         self,
@@ -492,23 +495,21 @@ def __predict_async(
         n_tasks = i + 1
         with tqdm(total=n_tasks, desc="Getting predictions", disable=disable_progress_bar) as pbar:
             unfinished_tasks = [i for i in range(n_tasks)]
-            while pbar.n != n_tasks:
+            while pbar.n < n_tasks:
                 time.sleep(0.2)
                 new_unfinished_tasks = []
                 for j in unfinished_tasks:
                     result = infer_results.get(j)
                     if result is None:
                         new_unfinished_tasks.append(j)
-                        continue
-                    if isinstance(result, InferenceServerException):
+                    elif isinstance(result, InferResult):
+                        pbar.n += 1
+                    else:  # unexpected result / exception -> try again
                         try:
-                            new_unfinished_tasks.append(j)
                             next(tasks[j])
+                            new_unfinished_tasks.append(j)
                         except StopIteration:
                             pbar.n += 1
-                        continue
-                    if isinstance(result, InferResult):
-                        pbar.n += 1
 
                 unfinished_tasks = new_unfinished_tasks
                 pbar.refresh()

oktoberfest/preprocessing/preprocessing.py

@@ -247,6 +247,7 @@ def list_spectra(input_dir: Union[str, Path], file_format: str) -> List[Path]:
     :param file_format: Format of spectra files that match the file extension (case-insensitive), can be "mzML", "RAW" or "pkl".
     :raises NotADirectoryError: if the specified input directory does not exist
     :raises ValueError: if the specified file format is not supported
+    :raises AssertionError: if no files in the provided input directory match the provided file format
     :return: A list of paths to all spectra files found in the given directory
     """
     if isinstance(input_dir, str):
@@ -264,6 +265,12 @@ def list_spectra(input_dir: Union[str, Path], file_format: str) -> List[Path]:
     else:
         raise NotADirectoryError(f"{input_dir} does not exist.")
 
+    if not raw_files:
+        raise AssertionError(
+            f"There are no spectra files with the extension {file_format.lower()} in the provided input_dir {input_dir}. "
+            "Please check."
+        )
+
     return raw_files
 
 

pyproject.toml

@@ -1,8 +1,8 @@
 [tool.poetry]
 name = "oktoberfest"
-version = "0.5.2"  # <<COOKIETEMPLE_FORCE_BUMP>>
+version = "0.5.3"  # <<COOKIETEMPLE_FORCE_BUMP>>
 description = "Public repo oktoberfest"
-authors = ["Victor Giurcoiu <victor.giurcoiu@tum.de>"]
+authors = ["Wilhelmlab at Technical University of Munich"]
 license = "MIT"
 readme = "README.rst"
 homepage = "https://github.com/wilhelm-lab/oktoberfest"

tests/unit_tests/test_pp.py

@@ -0,0 +1,28 @@
+import unittest
+from pathlib import Path
+
+from oktoberfest import pp
+
+
+class TestProcessing(unittest.TestCase):
+    """Test class for preprocessing functions."""
+
+    def test_list_spectra(self):
+        """Test listing of spectra with expected user input."""
+        spectra_path = Path(__file__).parent
+        spectra_file = spectra_path / "test.mzml"
+        spectra_file.open("w").close()
+        self.assertEqual([spectra_path / "test.mzml"], pp.list_spectra(spectra_path, file_format="mzml"))
+        spectra_file.unlink()
+
+    def test_list_spectra_with_empty_string_folder(self):
+        """Test listing spectra in a string folder without matching files."""
+        self.assertRaises(AssertionError, pp.list_spectra, str(Path(__file__).parent), "raw")
+
+    def test_list_spectra_with_wrong_folder(self):
+        """Test listing spectra in a folder that does not exist."""
+        self.assertRaises(NotADirectoryError, pp.list_spectra, Path(__file__).parent / "noexist", "raw")
+
+    def test_list_spectra_with_wrong_format(self):
+        """Test listing spectra with a format that isn't allowed."""
+        self.assertRaises(ValueError, pp.list_spectra, Path(__file__).parent, "mzm")

tests/unit_tests/test_predictions.py

@@ -37,3 +37,18 @@ def test_prosit_tmt(self):
         expected_df["PREDICTED_IRT"] = expected_df["PREDICTED_IRT"].astype(library.spectra_data["PREDICTED_IRT"].dtype)
 
         pd.testing.assert_frame_equal(library.spectra_data, expected_df)
+
+    def test_failing_koina(self):
+        """Test koina with input data that does not fit to the model to trigger exception handling."""
+        library = Spectra.from_csv(Path(__file__).parent / "data" / "predictions" / "library_input.csv")
+        input_data = library.spectra_data
+
+        self.assertRaises(
+            Exception,
+            predict,
+            input_data,
+            model_name="Prosit_2020_intensity_HCD",
+            server_url="koina.proteomicsdb.org:443",
+            ssl=True,
+            targets=["intensities", "annotation"],
+        )