CLN: Remove unused multiple model functionality, fix #538

> The config allows instantiating multiple models
  for training / prediction but this was never fully implemented

Squash commits:
- Make config just use "model" option, not "models":
  - Remove `comma_separated_list` from converters
  - Change option name 'models' -> 'model' in config/valid.toml
  - Rewrite is_valid_model_name to test a single string,
    not a list of strings
  - Change attribute `models` -> `model` in config/eval.py
  - Change attribute `models` -> `model` in config/learncurve.py
  - Change attribute `models` -> `model` in config/predict.py
  - Change attribute `models` -> `model` in config/train.py
  - Rewrite/rename config.models -> model.config_from_toml_path,
    model.config_from_toml_dict
  - Fix option 'models' -> model = 'str',
    in all .toml files in tests/data_for_tests/configs
- Rewrite `models.from_model_config_map` as `models.get`:
  - Add src/vak/models/_api.py with BUILTIN_MODELS and
    MODEL_NAMES to use for validation in `models.get`
  - Rewrite models/models.py `from_model_config_map` as `models.get`
  - Import get and _api in vak/models/__init__.py
  - Rewrite core/train.py to take model_name and model_config
    then use models.get
  - Fix cli/train.py to pass model_name and model_config into core.train
  - Rewrite core/eval.py to take model_name and model_config
    then use models.get
  - Fix cli/eval.py to pass model_name and model_config into core.eval
  - Rewrite core/learncurve.py to take model_name and model_config
  - Fix cli/learncurve.py to pass model_name and model_config
    into core.learncurve
  - Rewrite core/predict.py to take model_name and model_config
    then use models.get
  - Fix cli/predict.py to pass model_name and model_config
    into core.predict
  - Make 'model' not 'models' required in src/vak/config/parse.py
  - Use models.MODEL_NAMES in src/vak/config/validators.py
  - Use models.MODEL_NAMES in config/model.py
- Fix tests
  - Fix tests to use vak.config.model.config_from_toml_path:
    - tests/test_models/test_windowed_frame_classification_model.py
    - tests/test_core/test_train.py
    - tests/test_core/test_predict.py
    - tests/test_core/test_learncurve.py
    - tests/test_core/test_eval.py
  - Fix test to use 'model' option not 'models' in
    tests/test_config/test_parse.py
  - Fix assert helper function in tests/test_core
    - test_eval.py
    - test_learncurve.py
    - test_predict.py
    - test_prep.py
    - test_train.py
  - Rewrite fixture module with constants we can import
    in test modules to parametrize: tests/fixtures/config.py
  - Add tests/test_config/test_model.py

src/vak/cli/eval.py

@@ -43,7 +43,8 @@ def eval(toml_path):
 
     logger.info("Logging results to {}".format(cfg.eval.output_dir))
 
-    model_config_map = config.models.map_from_path(toml_path, cfg.eval.models)
+    model_name = cfg.eval.model
+    model_config = config.model.config_from_toml_path(toml_path, model_name)
 
     if cfg.eval.csv_path is None:
         raise ValueError(
@@ -53,8 +54,9 @@ def eval(toml_path):
         )
 
     core.eval(
-        cfg.eval.csv_path,
-        model_config_map,
+        model_name=model_name,
+        model_config=model_config,
+        csv_path=cfg.eval.csv_path,
         checkpoint_path=cfg.eval.checkpoint_path,
         labelmap_path=cfg.eval.labelmap_path,
         output_dir=cfg.eval.output_dir,

src/vak/cli/learncurve.py

@@ -50,7 +50,8 @@ def learning_curve(toml_path):
     log_version(logger)
     logger.info("Logging results to {}".format(results_path))
 
-    model_config_map = config.models.map_from_path(toml_path, cfg.learncurve.models)
+    model_name = cfg.learncurve.model
+    model_config = config.model.config_from_toml_path(toml_path, model_name)
 
     if cfg.learncurve.csv_path is None:
         raise ValueError(
@@ -60,7 +61,8 @@ def learning_curve(toml_path):
         )
 
     core.learning_curve(
-        model_config_map,
+        model_name=model_name,
+        model_config=model_config,
         train_set_durs=cfg.learncurve.train_set_durs,
         num_replicates=cfg.learncurve.num_replicates,
         csv_path=cfg.learncurve.csv_path,

src/vak/cli/predict.py

@@ -38,7 +38,8 @@ def predict(toml_path):
     log_version(logger)
     logger.info("Logging results to {}".format(cfg.prep.output_dir))
 
-    model_config_map = config.models.map_from_path(toml_path, cfg.predict.models)
+    model_name = cfg.predict.model
+    model_config = config.model.config_from_toml_path(toml_path, model_name)
 
     if cfg.predict.csv_path is None:
         raise ValueError(
@@ -48,10 +49,11 @@ def predict(toml_path):
         )
 
     core.predict(
+        model_name=model_name,
+        model_config=model_config,
         csv_path=cfg.predict.csv_path,
         checkpoint_path=cfg.predict.checkpoint_path,
         labelmap_path=cfg.predict.labelmap_path,
-        model_config_map=model_config_map,
         window_size=cfg.dataloader.window_size,
         num_workers=cfg.predict.num_workers,
         spect_key=cfg.spect_params.spect_key,

src/vak/cli/train.py

@@ -49,7 +49,8 @@ def train(toml_path):
     log_version(logger)
     logger.info("Logging results to {}".format(results_path))
 
-    model_config_map = config.models.map_from_path(toml_path, cfg.train.models)
+    model_name = cfg.train.model
+    model_config = config.model.config_from_toml_path(toml_path, model_name)
 
     if cfg.train.csv_path is None:
         raise ValueError(
@@ -64,7 +65,8 @@ def train(toml_path):
         labelset, labelmap_path = cfg.prep.labelset, None
 
     core.train(
-        model_config_map=model_config_map,
+        model_name=model_name,
+        model_config=model_config,
         csv_path=cfg.train.csv_path,
         labelset=labelset,
         window_size=cfg.dataloader.window_size,

src/vak/config/__init__.py

@@ -4,7 +4,7 @@
     dataloader,
     eval,
     learncurve,
-    models,
+    model,
     parse,
     predict,
     prep,

src/vak/config/eval.py

@@ -5,7 +5,7 @@
 
 from .validators import is_valid_model_name
 from .. import device
-from ..converters import comma_separated_list, expanded_user_path
+from ..converters import expanded_user_path
 
 
 def convert_post_tfm_kwargs(post_tfm_kwargs: dict) -> dict:
@@ -72,8 +72,8 @@ class EvalConfig:
         Path to location where .csv files with evaluation metrics should be saved.
     labelmap_path : str
         path to 'labelmap.json' file.
-    models : list
-        of model names. e.g., 'models = TweetyNet, GRUNet, ConvNet'
+    model : str
+        Model name, e.g., ``model = "TweetyNet"``
     batch_size : int
         number of samples per batch presented to models during training.
     num_workers : int
@@ -106,9 +106,8 @@ class EvalConfig:
     output_dir = attr.ib(converter=expanded_user_path)
 
     # required, model / dataloader
-    models = attr.ib(
-        converter=comma_separated_list,
-        validator=[instance_of(list), is_valid_model_name],
+    model = attr.ib(
+        validator=[instance_of(str), is_valid_model_name],
     )
     batch_size = attr.ib(converter=int, validator=instance_of(int))
 

src/vak/config/learncurve.py

@@ -14,8 +14,8 @@ class LearncurveConfig(TrainConfig):
 
     Attributes
     ----------
-    models : list
-        of model names. e.g., 'models = TweetyNet, GRUNet, ConvNet'
+    model : str
+        Model name, e.g., ``model = "TweetyNet"``
     csv_path : str
         path to where dataset was saved as a csv.
     num_epochs : int

src/vak/config/model.py

@@ -0,0 +1,88 @@
+from __future__ import annotations
+import pathlib
+
+import toml
+
+from .. import models
+
+
+MODEL_TABLES = [
+        "network",
+        "optimizer",
+        "loss",
+        "metrics",
+    ]
+
+
+def config_from_toml_dict(toml_dict: dict, model_name: str) -> dict:
+    """Get configuration for a model from a .toml configuration file
+    loaded into a ``dict``.
+
+    Parameters
+    ----------
+    toml_dict : dict
+        Configuration from a .toml file, loaded into a dictionary.
+    model_name : str
+        Name of a model, specified as the ``model`` option in a table
+        (such as TRAIN or PREDICT),
+        that should have its own corresponding table
+        specifying its configuration: hyperparameters such as learning rate, etc.
+
+    Returns
+    -------
+    model_config : dict
+        Model configuration in a ``dict``,
+        as loaded from a .toml file,
+        and used by the model method ``from_config``.
+    """
+    if model_name not in models.MODEL_NAMES:
+        raise ValueError(
+            f"Invalid model name: {model_name}.\nValid model names are: {models.MODEL_NAMES}"
+        )
+
+    try:
+        model_config = toml_dict[model_name]
+    except KeyError as e:
+        raise ValueError(
+            f"A config section specifies the model name '{model_name}', "
+            f"but there is no section named '{model_name}' in the config."
+        ) from e
+
+    # check if config declares parameters for required attributes;
+    # if not, just put an empty dict that will get passed as the "kwargs"
+    for attr in MODEL_TABLES:
+        if attr not in model_config:
+            model_config[attr] = {}
+
+    return model_config
+
+
+def config_from_toml_path(toml_path: str | pathlib.Path, model_name: str) -> dict:
+    """Get configuration for a model from a .toml configuration file,
+    given the path to the file.
+
+    Parameters
+    ----------
+    toml_path : str, Path
+        to configuration file in .toml format
+     model_name : str
+        of str, i.e. names of models specified by a section
+        (such as TRAIN or PREDICT) that should each have corresponding sections
+        specifying their configuration: hyperparameters such as learning rate, etc.
+
+    Returns
+    -------
+    model_config : dict
+        Model configuration in a ``dict``,
+        as loaded from a .toml file,
+        and used by the model method ``from_config``.
+    """
+    toml_path = pathlib.Path(toml_path)
+    if not toml_path.is_file():
+        raise FileNotFoundError(
+            f"File not found, or not recognized as a file: {toml_path}"
+        )
+
+    with toml_path.open("r") as fp:
+        config_dict = toml.load(fp)
+    return config_from_toml_dict(config_dict, model_name)

src/vak/config/parse.py

@@ -29,26 +29,26 @@
         "checkpoint_path",
         "labelmap_path",
         "output_dir",
-        "models",
+        "model",
     ],
     "LEARNCURVE": [
-        "models",
+        "model",
         "root_results_dir",
         "train_set_durs",
         "num_replicates",
     ],
     "PREDICT": [
         "checkpoint_path",
         "labelmap_path",
-        "models",
+        "model",
     ],
     "PREP": [
         "data_dir",
         "output_dir",
     ],
     "SPECT_PARAMS": None,
     "TRAIN": [
-        "models",
+        "model",
         "root_results_dir",
     ],
 }