Simplify config

alphadia/constants/default.yaml

@@ -60,9 +60,6 @@ library_prediction:
 
 # define custom alphabase modifications not part of unimod or alphabase
 # also used for decoy channels
-# TODO make this a list
-#  - name: Dimethyl:d12@K
-#    composition: H(-2)2H(8)13C(2)
 custom_modifications:
   # Dimethyl @K channel decoy
   - name: Dimethyl:d12@K
@@ -130,9 +127,6 @@ calibration:
   # the maximum number of times an automatic optimizer can be skipped before it is considered to have converged
   max_skips: 1
 
-  # TODO: remove this parameter
-  final_full_calibration: False
-
   # TODO: remove this parameter
   norm_rt_mode: 'linear'
 
@@ -201,27 +195,27 @@ library_multiplexing:
   # channels can be either a number or a string
   # for every channel, the library gets copied and the modifications are translated according to the mapping
   # the following example shows how to multiplex mTRAQ to three sample channels and a decoy channel
-  # TODO make this a list
-  #    - channel_name: 0
-  #      channel_modifications:
-  #        mTRAQ@K: mTRAQ@K
-  #        mTRAQ@Any_N-term: mTRAQ@Any_N-term
-  multiplex_mapping: {}
-    #0:
-    #  mTRAQ@K: mTRAQ@K
-    #  mTRAQ@Any_N-term: mTRAQ@Any_N-term
-
-    #4:
-    #  mTRAQ@K: mTRAQ:13C(3)15N(1)@K
-    #  mTRAQ@Any_N-term: mTRAQ:13C(3)15N(1)@Any_N-term
+  multiplex_mapping: []
+#    - channel_name: 0
+#      modifications:
+#        mTRAQ@K: mTRAQ@K
+#        mTRAQ@Any_N-term: mTRAQ@Any_N-term
+#
+#    - channel_name: 4
+#      modifications:
+#        mTRAQ@K: mTRAQ:13C(3)15N(1)@K
+#        mTRAQ@Any_N-term: mTRAQ:13C(3)15N(1)@Any_N-term
+#
+#    - channel_name: 8
+#      modifications:
+#        mTRAQ@K: mTRAQ:13C(6)15N(2)@K
+#        mTRAQ@Any_N-term: mTRAQ:13C(6)15N(2)@Any_N-term
+#
+#    - channel_name: 12
+#      modifications:
+#        mTRAQ@K: mTRAQ:d12@K
+#        mTRAQ@Any_N-term: mTRAQ:d12@Any_N-term
 
-    #8:
-    #  mTRAQ@K: mTRAQ:13C(6)15N(2)@K
-    #  mTRAQ@Any_N-term: mTRAQ:13C(6)15N(2)@Any_N-term
-
-    #12:
-    #  mTRAQ@K: mTRAQ:d12@K
-    #  mTRAQ@Any_N-term: mTRAQ:d12@Any_N-term
 
 
 
@@ -388,58 +382,6 @@ transfer_learning:
   instrument: 'Lumos'
 
 
-# configuration for the calibration manager
-# the config has to start with the calibration keyword and consists of a list of calibration groups.
-# each group consists of datapoints which have multiple properties.
-# This can be for example precursors (mz, rt ...), fragments (mz, ...), quadrupole (transfer_efficiency)
-calibration_manager: # TODO move to a separate file or hard-code
-  - name: fragment
-    estimators:
-      - name: mz
-        model: LOESSRegression
-        model_args:
-          n_kernels: 2
-        input_columns:
-          - mz_library
-        target_columns:
-          - mz_observed
-        output_columns:
-          - mz_calibrated
-        transform_deviation: 1e6
-  - name: precursor
-    estimators:
-        - name: mz
-          model: LOESSRegression
-          model_args:
-            n_kernels: 2
-          input_columns:
-            - mz_library
-          target_columns:
-            - mz_observed
-          output_columns:
-            - mz_calibrated
-          transform_deviation: 1e6
-        - name: rt
-          model: LOESSRegression
-          model_args:
-            n_kernels: 6
-          input_columns:
-            - rt_library
-          target_columns:
-            - rt_observed
-          output_columns:
-            - rt_calibrated
-        - name: mobility
-          model: LOESSRegression
-          model_args:
-            n_kernels: 2
-          input_columns:
-            - mobility_library
-          target_columns:
-            - mobility_observed
-          output_columns:
-            - mobility_calibrated
-
 # scope of default yaml should be one search step
 multistep_search:
   transfer_step_enabled: False

alphadia/libtransform.py

@@ -617,12 +617,20 @@ def forward(self, input: SpecLibBase) -> SpecLibBase:
 
 
 class MultiplexLibrary(ProcessingStep):
-    def __init__(self, multiplex_mapping: dict, input_channel: str | int | None = None):
+    def __init__(self, multiplex_mapping: list, input_channel: str | int | None = None):
         """Initialize the MultiplexLibrary step."""
 
-        self._multiplex_mapping = multiplex_mapping
+        self._multiplex_mapping = self._create_multiplex_mapping(multiplex_mapping)
         self._input_channel = input_channel
 
+    @staticmethod
+    def _create_multiplex_mapping(multiplex_mapping: list) -> dict:
+        """Create a dictionary from the multiplex mapping list."""
+        mapping = {}
+        for list_item in multiplex_mapping:
+            mapping[list_item["channel_name"]] = list_item["modifications"]
+        return mapping
+
     def validate(self, input: str) -> bool:
         """Validate the input object. It is expected that the input is a path to a file which exists."""
         valid = True

alphadia/search_step.py

@@ -95,7 +95,7 @@ def _init_config(
 
         config_updates = []
 
-        if user_config is not None:
+        if user_config:
             logger.info("loading additional config provided via CLI")
             # load update config from dict
             if isinstance(user_config, dict):
@@ -108,15 +108,15 @@ def _init_config(
                     "'config' parameter must be of type 'dict' or 'Config'"
                 )
 
-        if cli_config is not None:
+        if cli_config:
             logger.info("loading additional config provided via CLI parameters")
             cli_config_update = Config(
                 cli_config, experiment_name=USER_DEFINED_CLI_PARAM
             )
             config_updates.append(cli_config_update)
 
         # this needs to be last
-        if extra_config is not None:
+        if extra_config:
             extra_config_update = Config(extra_config, experiment_name=MULTISTEP_SEARCH)
             # need to overwrite user-defined output folder here to have correct value in config dump
             extra_config[ConfigKeys.OUTPUT_DIRECTORY] = output_folder

alphadia/workflow/base.py

@@ -111,7 +111,6 @@ def load(
 
         # initialize the calibration manager
         self._calibration_manager = manager.CalibrationManager(
-            self.config["calibration_manager"],
             path=os.path.join(self.path, self.CALIBRATION_MANAGER_PKL_NAME),
             load_from_file=self.config["general"]["reuse_calibration"],
             reporter=self.reporter,

alphadia/workflow/manager.py

@@ -26,6 +26,58 @@
 
 # TODO move all managers to dedicated modules
 
+# configuration for the calibration manager
+# the config has to start with the calibration keyword and consists of a list of calibration groups.
+# each group consists of datapoints which have multiple properties.
+# This can be for example precursors (mz, rt ...), fragments (mz, ...), quadrupole (transfer_efficiency)
+# TODO simplify this structure and the config loading
+CALIBRATION_MANAGER_CONFIG = [
+    {
+        "estimators": [
+            {
+                "input_columns": ["mz_library"],
+                "model": "LOESSRegression",
+                "model_args": {"n_kernels": 2},
+                "name": "mz",
+                "output_columns": ["mz_calibrated"],
+                "target_columns": ["mz_observed"],
+                "transform_deviation": "1e6",
+            }
+        ],
+        "name": "fragment",
+    },
+    {
+        "estimators": [
+            {
+                "input_columns": ["mz_library"],
+                "model": "LOESSRegression",
+                "model_args": {"n_kernels": 2},
+                "name": "mz",
+                "output_columns": ["mz_calibrated"],
+                "target_columns": ["mz_observed"],
+                "transform_deviation": "1e6",
+            },
+            {
+                "input_columns": ["rt_library"],
+                "model": "LOESSRegression",
+                "model_args": {"n_kernels": 6},
+                "name": "rt",
+                "output_columns": ["rt_calibrated"],
+                "target_columns": ["rt_observed"],
+            },
+            {
+                "input_columns": ["mobility_library"],
+                "model": "LOESSRegression",
+                "model_args": {"n_kernels": 2},
+                "name": "mobility",
+                "output_columns": ["mobility_calibrated"],
+                "target_columns": ["mobility_observed"],
+            },
+        ],
+        "name": "precursor",
+    },
+]
+
 
 class BaseManager:
     def __init__(
@@ -151,7 +203,6 @@ def fit_predict(self):
 class CalibrationManager(BaseManager):
     def __init__(
         self,
-        config: None | dict = None,
         path: None | str = None,
         load_from_file: bool = True,
         **kwargs,
@@ -162,10 +213,6 @@ def __init__(
 
         Parameters
         ----------
-
-        config : typing.Union[None, dict], default=None
-            Calibration config dict. If None, the default config is used.
-
         path : str, default=None
             Path where the current parameter set is saved to and loaded from.
 
@@ -181,7 +228,7 @@ def __init__(
 
         if not self.is_loaded_from_file:
             self.estimator_groups = []
-            self.load_config(config)
+            self.load_config(CALIBRATION_MANAGER_CONFIG)
 
     @property
     def estimator_groups(self):

tests/unit_tests/test_libtransform.py

@@ -101,11 +101,14 @@ def test_multiplex_library():
     test_lib.calc_precursor_mz()
     test_lib.calc_fragment_mz_df()
 
-    test_multiplex_mapping = {
-        0: {"mTRAQ@K": "mTRAQ@K"},
-        "magic_chanel": {"mTRAQ@K": "mTRAQ:13C(3)15N(1)@K"},
-        1337: {"mTRAQ@K": "mTRAQ:13C(6)15N(2)@K"},
-    }
+    test_multiplex_mapping = [
+        {"channel_name": 0, "modifications": {"mTRAQ@K": "mTRAQ@K"}},
+        {
+            "channel_name": "magic_channel",
+            "modifications": {"mTRAQ@K": "mTRAQ:13C(3)15N(1)@K"},
+        },
+        {"channel_name": 1337, "modifications": {"mTRAQ@K": "mTRAQ:13C(6)15N(2)@K"}},
+    ]
 
     # when
     multiplexer = libtransform.MultiplexLibrary(test_multiplex_mapping)
@@ -116,7 +119,7 @@ def test_multiplex_library():
     assert result_lib.precursor_df["charge"].nunique() == 2
     assert result_lib.precursor_df["frag_stop_idx"].nunique() == 6
 
-    for channel in [0, 1337, "magic_chanel"]:
+    for channel in [0, 1337, "magic_channel"]:
         assert (
             result_lib.precursor_df[
                 result_lib.precursor_df["channel"] == channel

tests/unit_tests/test_workflow.py

@@ -3,6 +3,7 @@
 import tempfile
 from copy import deepcopy
 from pathlib import Path
+from unittest.mock import patch
 
 import numpy as np
 import pandas as pd
@@ -48,7 +49,7 @@ def test_base_manager_load():
     os.remove(my_base_manager.path)
 
 
-TEST_CONFIG = [
+TEST_CALIBRATION_MANAGER_CONFIG = [
     {
         "name": "precursor",
         "estimators": [
@@ -90,9 +91,13 @@ def test_base_manager_load():
 def test_calibration_manager_init():
     # initialize the calibration manager
     temp_path = os.path.join(tempfile.tempdir, "calibration_manager.pkl")
-    calibration_manager = manager.CalibrationManager(
-        TEST_CONFIG, path=temp_path, load_from_file=False
-    )
+    with patch(
+        "alphadia.workflow.manager.CALIBRATION_MANAGER_CONFIG",
+        TEST_CALIBRATION_MANAGER_CONFIG,
+    ):
+        calibration_manager = manager.CalibrationManager(
+            path=temp_path, load_from_file=False
+        )
 
     assert calibration_manager.path == temp_path
     assert calibration_manager.is_loaded_from_file is False
@@ -158,9 +163,13 @@ def calibration_testdata():
 
 def test_calibration_manager_fit_predict():
     temp_path = os.path.join(tempfile.tempdir, "calibration_manager.pkl")
-    calibration_manager = manager.CalibrationManager(
-        TEST_CONFIG, path=temp_path, load_from_file=False
-    )
+    with patch(
+        "alphadia.workflow.manager.CALIBRATION_MANAGER_CONFIG",
+        TEST_CALIBRATION_MANAGER_CONFIG,
+    ):
+        calibration_manager = manager.CalibrationManager(
+            path=temp_path, load_from_file=False
+        )
 
     test_df = calibration_testdata()
 
@@ -182,9 +191,13 @@ def test_calibration_manager_fit_predict():
 
 def test_calibration_manager_save_load():
     temp_path = os.path.join(tempfile.tempdir, "calibration_manager.pkl")
-    calibration_manager = manager.CalibrationManager(
-        TEST_CONFIG, path=temp_path, load_from_file=False
-    )
+    with patch(
+        "alphadia.workflow.manager.CALIBRATION_MANAGER_CONFIG",
+        TEST_CALIBRATION_MANAGER_CONFIG,
+    ):
+        calibration_manager = manager.CalibrationManager(
+            path=temp_path, load_from_file=False
+        )
 
     test_df = calibration_testdata()
     calibration_manager.fit(test_df, "precursor", plot=False)
@@ -195,9 +208,13 @@ def test_calibration_manager_save_load():
 
     calibration_manager.save()
 
-    calibration_manager_loaded = manager.CalibrationManager(
-        TEST_CONFIG, path=temp_path, load_from_file=True
-    )
+    with patch(
+        "alphadia.workflow.manager.CALIBRATION_MANAGER_CONFIG",
+        TEST_CALIBRATION_MANAGER_CONFIG,
+    ):
+        calibration_manager_loaded = manager.CalibrationManager(
+            path=temp_path, load_from_file=True
+        )
     assert calibration_manager_loaded.is_fitted is True
     assert calibration_manager_loaded.is_loaded_from_file is True
 
@@ -433,7 +450,6 @@ def create_workflow_instance():
         ]
     )
     workflow._calibration_manager = manager.CalibrationManager(
-        workflow.config["calibration_manager"],
         path=os.path.join(workflow.path, workflow.CALIBRATION_MANAGER_PKL_NAME),
         load_from_file=workflow.config["general"]["reuse_calibration"],
         reporter=workflow.reporter,