keep Add Var feature

.gitlab-ci.yml

@@ -23,7 +23,7 @@ stages:
 
 checking_mr:
   stage: check_mr
-  image: python:3.7-slim
+  image: python:3.8-slim
   before_script:
     - pip install --upgrade pip setuptools wheel
     - pip install python-gitlab
@@ -35,7 +35,7 @@ checking_mr:
 
 linter:
   stage: linting
-  image: python:3.7-slim
+  image: python:3.8-slim
   script:
     - mkdir coverage_files/
     - pip install flake8
@@ -59,7 +59,7 @@ yaml_linter:
 
 doc_string_check:
   stage: linting
-  image: python:3.7-slim
+  image: python:3.8-slim
   script:
     - pip install darglint
     - darglint --list-errors
@@ -69,6 +69,19 @@ doc_string_check:
     - if: $CI_PIPELINE_SOURCE == "merge_request_event"
   allow_failure: True
 
+
+pylint:
+  stage: linting
+  image: python:3.8-slim
+  before_script:
+    - pip install pylint==2.12.2
+  script:
+    - pylint ./umami
+  rules:
+    - if: $CI_COMMIT_BRANCH != ''
+    - if: $CI_PIPELINE_SOURCE == "merge_request_event"
+  allow_failure: True
+
 include:
   - 'pipelines/.unit_test-gitlab-ci.yaml'
   - 'pipelines/.docker-gitlab-ci.yaml'

.pre-commit-config.yaml

@@ -14,6 +14,7 @@ repos:
         language: system
         entry: black
         types: [python]
+        args: ["--experimental-string-processing"]
       - id: flake8
         name: flake8
         stages: [commit]

docker/umami/Dockerfile

-FROM UPDATED_IN_GITLAB_CI_JOB
+FROM ${BASE_IMAGE}
 
 COPY . /umami
 WORKDIR /umami

docker/umamibase/Dockerfile

-FROM UPDATED_IN_GITLAB_CI_JOB
+FROM ${BASE_IMAGE}
 
 ## ensure locale is set during build
 ENV LANG C.UTF-8

pipelines/.coverage-gitlab-ci.yaml

@@ -4,7 +4,7 @@
 
 test_coverage:
   stage: coverage_test_stage
-  image: python:3.7-slim
+  image: python:3.8-slim
   script:
     - pip install --upgrade pip setuptools wheel
     - pip install pytest==6.2.4
@@ -46,7 +46,7 @@ coverage_html_report:
 
 test_coverage_post_report:
   stage: publish
-  image: python:3.7-slim
+  image: python:3.8-slim
   needs: ["test_coverage"]
   before_script:
     - pip install --upgrade pip setuptools wheel

pipelines/.docker-gitlab-ci.yaml

@@ -4,10 +4,21 @@
 
 .image_build_template: &image_build_template
   script:
-    # The script gets overwritten in jobs tagged docker-image-build
-    - ignore
-  tags:
-    - docker-image-build
+    # Prepare Kaniko configuration file
+    - echo "{\"auths\":{\"$CI_REGISTRY\":{\"username\":\"$CI_REGISTRY_USER\",\"password\":\"$CI_REGISTRY_PASSWORD\"}}}" > /kaniko/.docker/config.json
+    # Build and push the image from the Dockerfile at the root of the project.
+    - /kaniko/executor --context $CI_PROJECT_DIR
+      --dockerfile ${DOCKER_FILE}
+      --build-arg ${BASE}
+      --destination ${IMAGE_DESTINATION}
+    # Print the full registry path of the pushed image
+    - echo "Image pushed successfully to ${IMAGE_DESTINATION}"
+
+  image:
+    # We recommend using the CERN version of the Kaniko image: gitlab-registry.cern.ch/ci-tools/docker-image-builder
+    name: gitlab-registry.cern.ch/ci-tools/docker-image-builder
+    entrypoint: [""]
+
   retry: 2
 
 
@@ -15,67 +26,67 @@ build_umamibase_cpu:
   <<: *image_build_template
   stage: image_build_umamibase
   variables:
+    BASE: 'BASE_IMAGE=tensorflow/tensorflow:$TFTAG'
     DOCKER_FILE: docker/umamibase/Dockerfile
-    FROM: tensorflow/tensorflow:$TFTAG
-    TO: '${CI_REGISTRY}/${CI_PROJECT_NAMESPACE}/umami/umamibase:latest'
+    IMAGE_DESTINATION: '${CI_REGISTRY}/${CI_PROJECT_NAMESPACE}/umami/umamibase:latest'
   rules:
     - if: $CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH
     - if: $CI_COMMIT_TAG
       variables:
-        TO: '${CI_REGISTRY}/${CI_PROJECT_NAMESPACE}/umami/umamibase:$CI_COMMIT_REF_SLUG'
+        IMAGE_DESTINATION: '${CI_REGISTRY}/${CI_PROJECT_NAMESPACE}/umami/umamibase:$CI_COMMIT_REF_SLUG'
 
 build_umamibase_gpu:
   <<: *image_build_template
   stage: builds
   variables:
+    BASE: 'BASE_IMAGE=tensorflow/tensorflow:$TFTAG-gpu'
     DOCKER_FILE: docker/umamibase/Dockerfile
-    FROM: tensorflow/tensorflow:$TFTAG-gpu
-    TO: '${CI_REGISTRY}/${CI_PROJECT_NAMESPACE}/umami/umamibase:latest-gpu'
+    IMAGE_DESTINATION: '${CI_REGISTRY}/${CI_PROJECT_NAMESPACE}/umami/umamibase:latest-gpu'
   rules:
     - if: $CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH
     - if: $CI_COMMIT_TAG
       variables:
-        TO: '${CI_REGISTRY}/${CI_PROJECT_NAMESPACE}/umami/umamibase:$CI_COMMIT_REF_SLUG-gpu'
+        IMAGE_DESTINATION: '${CI_REGISTRY}/${CI_PROJECT_NAMESPACE}/umami/umamibase:$CI_COMMIT_REF_SLUG-gpu'
 
 build_umamibase_gpu_pytorch:
   <<: *image_build_template
   stage: builds
   variables:
+    BASE: 'BASE_IMAGE=pytorch/pytorch:$TORCHTAG'
     DOCKER_FILE: docker/umamibase/Dockerfile
-    FROM: pytorch/pytorch:$TORCHTAG
-    TO: '${CI_REGISTRY}/${CI_PROJECT_NAMESPACE}/umami/umamibase:latest-pytorch-gpu'
+    IMAGE_DESTINATION: '${CI_REGISTRY}/${CI_PROJECT_NAMESPACE}/umami/umamibase:latest-pytorch-gpu'
   rules:
     - if: $CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH
 
 
 # Umami images: use base image as a foundation to speed up build process
 build_umami_cpu:
-  <<: *image_build_template
-  stage: image_build_umami
-  variables:
-    DOCKER_FILE: docker/umami/Dockerfile
-    FROM: '${CI_REGISTRY}/${CI_PROJECT_NAMESPACE}/umami/umamibase:latest'
-    TO: $CI_REGISTRY_IMAGE:latest
-  rules:
-    - if: $CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH
-    - if: $CI_COMMIT_TAG
-      variables:
-        FROM: '${CI_REGISTRY}/${CI_PROJECT_NAMESPACE}/umami/umamibase:$CI_COMMIT_REF_SLUG'
-        TO: $CI_REGISTRY_IMAGE:$CI_COMMIT_REF_SLUG
+    <<: *image_build_template
+    stage: image_build_umami
+    variables:
+      BASE: 'BASE_IMAGE=${CI_REGISTRY}/${CI_PROJECT_NAMESPACE}/umami/umamibase:latest'
+      DOCKER_FILE: docker/umami/Dockerfile
+      IMAGE_DESTINATION: '${CI_REGISTRY_IMAGE}:latest'
+    rules:
+      - if: $CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH
+      - if: $CI_COMMIT_TAG
+        variables:
+          BASE: 'BASE_IMAGE=${CI_REGISTRY}/${CI_PROJECT_NAMESPACE}/umami/umamibase:$CI_COMMIT_REF_SLUG'
+          IMAGE_DESTINATION: $CI_REGISTRY_IMAGE:$CI_COMMIT_REF_SLUG
 
 build_umami_gpu:
-  <<: *image_build_template
-  stage: image_build_umami
-  variables:
-    DOCKER_FILE: docker/umami/Dockerfile
-    FROM: '${CI_REGISTRY}/${CI_PROJECT_NAMESPACE}/umami/umamibase:latest-gpu'
-    TO: $CI_REGISTRY_IMAGE:latest-gpu
-  rules:
-    - if: $CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH
-    - if: $CI_COMMIT_TAG
-      variables:
-        FROM: '${CI_REGISTRY}/${CI_PROJECT_NAMESPACE}/umami/umamibase:$CI_COMMIT_REF_SLUG-gpu'
-        TO: $CI_REGISTRY_IMAGE:$CI_COMMIT_REF_SLUG-gpu
+    <<: *image_build_template
+    stage: image_build_umami
+    variables:
+      BASE: 'BASE_IMAGE=${CI_REGISTRY}/${CI_PROJECT_NAMESPACE}/umami/umamibase:latest-gpu'
+      DOCKER_FILE: docker/umami/Dockerfile
+      IMAGE_DESTINATION: '${CI_REGISTRY_IMAGE}:latest-gpu'
+    rules:
+      - if: $CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH
+      - if: $CI_COMMIT_TAG
+        variables:
+          BASE: 'BASE_IMAGE=${CI_REGISTRY}/${CI_PROJECT_NAMESPACE}/umami/umamibase:$CI_COMMIT_REF_SLUG-gpu'
+          IMAGE_DESTINATION: '$CI_REGISTRY_IMAGE:$CI_COMMIT_REF_SLUG-gpu'
 
 
 # ----------------------------------------------------------------------------
@@ -92,9 +103,9 @@ build_umamibase_cpu_MR:
   <<: *image_build_template
   stage: image_build_umamibase
   variables:
+    BASE: 'BASE_IMAGE=tensorflow/tensorflow:$TFTAG'
     DOCKER_FILE: docker/umamibase/Dockerfile
-    FROM: tensorflow/tensorflow:$TFTAG
-    TO: '${CI_REGISTRY}/${CI_PROJECT_NAMESPACE}/umami/temporary_images:${CI_MERGE_REQUEST_IID}-base'
+    IMAGE_DESTINATION: '${CI_REGISTRY}/${CI_PROJECT_NAMESPACE}/umami/temporary_images:${CI_MERGE_REQUEST_IID}-base'
   rules:
     - if: $CI_PIPELINE_SOURCE == "merge_request_event" && $CI_PROJECT_PATH=="atlas-flavor-tagging-tools/algorithms/umami"
 
@@ -103,9 +114,9 @@ build_umamibase_gpu_MR:
   <<: *image_build_template
   stage: image_build_umamibase
   variables:
+    BASE: 'BASE_IMAGE=tensorflow/tensorflow:$TFTAG-gpu'
     DOCKER_FILE: docker/umamibase/Dockerfile
-    FROM: tensorflow/tensorflow:$TFTAG-gpu
-    TO: '${CI_REGISTRY}/${CI_PROJECT_NAMESPACE}/umami/temporary_images:${CI_MERGE_REQUEST_IID}-gpu-base'
+    IMAGE_DESTINATION: '${CI_REGISTRY}/${CI_PROJECT_NAMESPACE}/umami/temporary_images:${CI_MERGE_REQUEST_IID}-gpu-base'
   rules:
     - if: $CI_PIPELINE_SOURCE == "merge_request_event" && $CI_PROJECT_PATH=="atlas-flavor-tagging-tools/algorithms/umami"
       when: manual

pipelines/.integration_test-gitlab-ci.yaml

@@ -148,6 +148,21 @@ test_train_dips:
       - test_dips_model/
       - coverage_files/
 
+test_train_dips_tfrecords:
+  <<: *test_template
+  stage: integration_test_tagger
+  needs: ["test_preprocessing_dips"]
+  dependencies:
+   - test_preprocessing_dips
+  script:
+    - pytest --cov=./ --cov-report= ./umami/tests/integration/test_train_dips.py -v -s --junitxml=report.xml -k "test_tfrecords_train_dips"
+    - cp .coverage ./coverage_files/.coverage.test_train_dips_tfrecords
+  artifacts:
+    <<: *artifact_template
+    paths:
+      - test_dips_model_tfrecords/
+      - coverage_files/
+
 test_train_dips_cond_att:
   <<: *test_template
   stage: integration_test_tagger
@@ -205,7 +220,7 @@ test_train_umami_tfrecords:
   artifacts:
     <<: *artifact_template
     paths:
-      - test_umami_model/
+      - test_umami_model_tfrecords/
       - coverage_files/
 
 test_plot_input_vars:

pyproject.toml

 [tool.black]
-line-length = 79
-target-version = ['py37']
+line-length = 88
+target-version = ['py38']
 include = '\.pyi?$'
 exclude = '''
 /(

requirements.txt

@@ -21,6 +21,7 @@ partd==1.2.0
 Pillow==8.2.0
 pre-commit==2.12.1
 pydot==1.4.2
+pylint==2.12.2
 pytest-cov==2.12.0
 pytest==6.2.4
 ruamel.yaml==0.17.10
@@ -35,3 +36,4 @@ xarray==0.16.2
 xhistogram==0.1.3
 yamllint==1.26.2
 zarr==2.8.1
+python-gitlab

setup.cfg

@@ -3,12 +3,18 @@ multi_line_output=3
 include_trailing_comma=True
 force_grid_wrap=0
 use_parentheses=True
-line_length=79
+line_length=88
 
 [flake8]
-ignore = E203, E266, E501, W503
-max-line-length = 79
+ignore = E203, E266, W503
+max-line-length = 88
 select = B,C,E,F,W,T4
 
 [darglint]
-ignore=DAR203
\ No newline at end of file
+ignore=DAR203
+
+[pylint.FORMAT]
+max-line-length = 88
+
+[pylint.'MESSAGES CONTROL']
+disable = missing-docstring,invalid-name,unspecified-encoding,wrong-import-order,logging-fstring-interpolation,no-name-in-module,too-many-arguments,too-many-locals,too-many-lines,no-member,too-many-statements,too-many-branches
\ No newline at end of file

umami/configuration/Configuration.py

@@ -8,11 +8,16 @@ from umami.tools import yaml_loader
 
 
 class Configuration(object):
-    """This is a global configuration to allow certain settings which are hardcoded so far."""
+    """
+    This is a global configuration to allow certain settings which are
+    hardcoded so far.
+    """
 
     def __init__(self, yaml_config=None):
         super(Configuration, self).__init__()
-        self.yaml_config = f"{pathlib.Path(__file__).parent.absolute()}/../configs/global_config.yaml"
+        self.yaml_config = (
+            f"{pathlib.Path(__file__).parent.absolute()}/../configs/global_config.yaml"
+        )
         self.LoadConfigFile()
         self.logger = self.SetLoggingLevel()
         self.SetTFDebugLevel()
@@ -37,9 +42,7 @@ class Configuration(object):
                 self.logger.debug(f"Setting {item} to {self.config[item]}.")
                 setattr(self, item, self.config[item])
             else:
-                raise KeyError(
-                    f"You need to specify {item} in your" " config file!"
-                )
+                raise KeyError(f"You need to specify {item} in your config file!")
 
     def SetMPLPlottingBackend(self):
         """Setting the plotting backend of matplotlib."""
@@ -53,9 +56,7 @@ class Configuration(object):
     def SetTFDebugLevel(self):
         """Setting the Debug level of tensorflow.
         For reference see https://stackoverflow.com/questions/35869137/avoid-tensorflow-print-on-standard-error"""  # noqa
-        self.logger.debug(
-            f"Setting TFDebugLevel to {self.config['TFDebugLevel']}"
-        )
+        self.logger.debug(f"Setting TFDebugLevel to {self.config['TFDebugLevel']}")
         os.environ["TF_CPP_MIN_LOG_LEVEL"] = str(self.config["TFDebugLevel"])
 
     def SetLoggingLevel(self):
@@ -73,7 +74,8 @@ class Configuration(object):
             logger.setLevel(log_levels[self.config["DebugLevel"]])
         else:
             logging.error(
-                f"The 'DebugLevel' option {self.config['DebugLevel']} set in the global config is not valid."
+                f"The 'DebugLevel' option {self.config['DebugLevel']} set in"
+                " the global config is not valid."
             )
         ch = logging.StreamHandler()
         ch.setLevel(log_levels[self.config["DebugLevel"]])
@@ -95,7 +97,9 @@ class CustomFormatter(logging.Formatter):
     red = "\x1b[31;21m"
     bold_red = "\x1b[31;1m"
     reset = "\x1b[0m"
-    debugformat = "%(asctime)s - %(levelname)s:%(name)s: %(message)s (%(filename)s:%(lineno)d)"
+    debugformat = (
+        "%(asctime)s - %(levelname)s:%(name)s: %(message)s (%(filename)s:%(lineno)d)"
+    )
     format = "%(levelname)s:%(name)s: %(message)s"
 
     FORMATS = {

umami/evaluate_model.py

@@ -27,9 +27,7 @@ def GetParser():
     -------
     args: parse_args
     """
-    parser = argparse.ArgumentParser(
-        description="Preprocessing command line options."
-    )
+    parser = argparse.ArgumentParser(description="Preprocessing command line options.")
 
     parser.add_argument(
         "-c",
@@ -75,8 +73,37 @@ def GetParser():
 
 
 def EvaluateModel(
-    args, train_config, preprocess_config, test_file, data_set_name
+    args: object,
+    train_config: object,
+    preprocess_config: object,
+    test_file: str,
+    data_set_name: str,
 ):
+    """
+    Evaluate only the taggers in the files or also the UMAMI tagger.
+
+    Parameters
+    ----------
+    args : object
+        Loaded argparser.
+    train_config : object
+        Loaded train config.
+    preprocess_config : object
+        Loaded preprocessing config.
+    test_file : str
+        Path to the files which are to be tested. Wildcards are supported.
+    data_set_name : str
+        Dataset name for the results files. The results will be saved in
+        dicts. The key will be this dataset name.
+
+    Raises
+    ------
+    ValueError
+        If no epoch is given when evaluating UMAMI.
+    ValueError
+        If the given tagger argument in train config is not a list.
+    """
+
     # Get train parameters
     Eval_params = train_config.Eval_parameters_validation
     class_labels = train_config.NN_structure["class_labels"]
@@ -84,8 +111,7 @@ def EvaluateModel(
     frac_values_comp = Eval_params["frac_values_comp"]
     var_cuts = (
         Eval_params["variable_cuts"][f"{data_set_name}"]
-        if "variable_cuts" in Eval_params
-        and Eval_params["variable_cuts"] is not None
+        if "variable_cuts" in Eval_params and Eval_params["variable_cuts"] is not None
         else None
     )
 
@@ -102,7 +128,8 @@ def EvaluateModel(
     except AttributeError:
         Eval_model_bool = True
 
-    # Set epoch to use for evaluation of trained model or dummy value if tagger scores from derivations should be used
+    # Set epoch to use for evaluation of trained model or dummy value if
+    # tagger scores from derivations should be used
     epoch = args.epoch if Eval_model_bool else 0
 
     # Test if multiple taggers are given or not
@@ -124,9 +151,7 @@ def EvaluateModel(
     # evaluate trained model file (for evaluate_trained_model: True in config)
     if Eval_model_bool:
         if epoch is None:
-            raise ValueError(
-                "You need to give an epoch which is to be evaluated!"
-            )
+            raise ValueError("You need to give an epoch which is to be evaluated!")
 
         # Get model file path
         model_file = utt.GetModelPath(
@@ -239,36 +264,64 @@ def EvaluateModel(
         if tagger_preds != []
         else None,
         frac_values_comp=frac_values_comp,
-        eff_min=0.49
-        if "eff_min" not in Eval_params
-        else Eval_params["eff_min"],
-        eff_max=1.0
-        if "eff_max" not in Eval_params
-        else Eval_params["eff_max"],
+        eff_min=0.49 if "eff_min" not in Eval_params else Eval_params["eff_min"],
+        eff_max=1.0 if "eff_max" not in Eval_params else Eval_params["eff_max"],
     )
 
     df_eff_rej = pd.DataFrame(tagger_rej_dicts)
     del tagger_rej_dicts
 
     df_eff_rej.to_hdf(
-        f"{train_config.model_name}/results/results-rej_per_eff"
-        f"-{epoch}.h5",
+        f"{train_config.model_name}/results/results-rej_per_eff-{epoch}.h5",
         data_set_name,
     )
 
     # Save the number of jets in the test file to the h5 file.
     # This is needed to calculate the binomial errors
     with h5py.File(
-        f"{train_config.model_name}/results/"
-        + f"results-rej_per_eff-{epoch}.h5",
+        f"{train_config.model_name}/results/" + f"results-rej_per_eff-{epoch}.h5",
         "a",
     ) as f:
         f.attrs["N_test"] = len(jets)
 
 
 def EvaluateModelDips(
-    args, train_config, preprocess_config, test_file, data_set_name, tagger
+    args: object,
+    train_config: object,
+    preprocess_config: object,
+    test_file: str,
+    data_set_name: str,
+    tagger: str,
 ):
+    """
+    Evaluate the DIPS models.
+
+    Parameters
+    ----------
+    args : object
+        Loaded argparser.
+    train_config : object
+        Loaded train config.
+    preprocess_config : object
+        Loaded preprocessing config.
+    test_file : str
+        Path to the files which are to be tested. Wildcards are supported.
+    data_set_name : str
+        Dataset name for the results files. The results will be saved in
+        dicts. The key will be this dataset name.
+    tagger : str
+        Name of the tagger that is to be evaluated. Can either be dips or
+        dips_cond_att depending which architecture is used.
+
+    Raises
+    ------
+    ValueError
+        If no epoch is given when evaluating.
+    ValueError
+        If the given tagger argument in train config is neither a
+        list nor a string.
+    """
+
     # Check if epochs are set
     if args.epoch is None:
         raise ValueError("You need to give an epoch which is to be evaluated!")
@@ -280,8 +333,7 @@ def EvaluateModelDips(
     frac_values_comp = Eval_params["frac_values_comp"]
     var_cuts = (
         Eval_params["variable_cuts"][f"{data_set_name}"]
-        if "variable_cuts" in Eval_params
-        and Eval_params["variable_cuts"] is not None
+        if "variable_cuts" in Eval_params and Eval_params["variable_cuts"] is not None
         else None
     )
 
@@ -304,9 +356,7 @@ def EvaluateModelDips(
         )
 
     # Get model file path
-    model_file = utt.GetModelPath(
-        model_name=train_config.model_name, epoch=args.epoch
-    )
+    model_file = utt.GetModelPath(model_name=train_config.model_name, epoch=args.epoch)
     logger.info(f"Evaluating {model_file}")
 
     # Check which test files need to be loaded depending on the DIPS version
@@ -428,12 +478,8 @@ def EvaluateModelDips(
         main_class=main_class,
         frac_values={"dips": Eval_params["frac_values"]},
         frac_values_comp=frac_values_comp,
-        eff_min=0.49
-        if "eff_min" not in Eval_params
-        else Eval_params["eff_min"],
-        eff_max=1.0
-        if "eff_max" not in Eval_params
-        else Eval_params["eff_max"],
+        eff_min=0.49 if "eff_min" not in Eval_params else Eval_params["eff_min"],
+        eff_max=1.0 if "eff_max" not in Eval_params else Eval_params["eff_max"],
     )
 
     # Form the dict to a Dataframe and save it
@@ -441,16 +487,14 @@ def EvaluateModelDips(
     del tagger_rej_dicts
 
     df_eff_rej.to_hdf(
-        f"{train_config.model_name}/results/results-rej_per_eff"
-        f"-{args.epoch}.h5",
+        f"{train_config.model_name}/results/results-rej_per_eff-{args.epoch}.h5",
         data_set_name,
     )
 
     # Save the number of jets in the test file to the h5 file.
     # This is needed to calculate the binomial errors
     with h5py.File(
-        f"{train_config.model_name}/results/"
-        + f"results-rej_per_eff-{args.epoch}.h5",
+        f"{train_config.model_name}/results/" + f"results-rej_per_eff-{args.epoch}.h5",
         "a",
     ) as f:
         f.attrs["N_test"] = len(jets)
@@ -481,13 +525,42 @@ def EvaluateModelDips(
 
 
 def EvaluateModelDL1(
-    args,
-    train_config,
-    preprocess_config,
-    test_file,
-    data_set_name,
-    test_file_entry,
+    args: object,
+    train_config: object,
+    preprocess_config: object,
+    test_file: str,
+    data_set_name: str,
+    test_file_entry: str,
 ):
+    """
+    Evaluate the various DL1* models.
+
+    Parameters
+    ----------
+    args : object
+        Loaded argparser.
+    train_config : object
+        Loaded train config.
+    preprocess_config : object
+        Loaded preprocessing config.
+    test_file : str
+        Path to the files which are to be tested. Wildcards are supported.
+    data_set_name : str