util function dot_to_object and corresponding unit test

examples/training/train_textcat.py

@@ -45,7 +45,7 @@ def main(config_path, output_dir=None, n_iter=20, n_texts=2000, init_tok2vec=Non
     print(f"Loading nlp model from {config_path}")
     nlp_config = Config().from_disk(config_path)
     print(f"config: {nlp_config}")
-    nlp, _ = util.load_model_from_config(nlp_config)
+    nlp, _ = util.load_model_from_config(nlp_config, auto_fill=True)
 
     # ensure the nlp object was defined with a textcat component
     if "textcat" not in nlp.pipe_names:

spacy/cli/debug_model.py

@@ -8,6 +8,7 @@ import typer
 from ._util import Arg, Opt, debug_cli, show_validation_error, parse_config_overrides
 from .. import util
 from ..lang.en import English
+from ..util import dot_to_object
 
 
 @debug_cli.command("model")
@@ -60,16 +61,7 @@ def debug_model_cli(
         msg.info(f"Fixing random seed: {seed}")
         fix_random_seed(seed)
 
-    component = config
-    parts = section.split(".")
-    for item in parts:
-        try:
-            component = component[item]
-        except KeyError:
-            msg.fail(
-                f"The section '{section}' is not a valid section in the provided config.",
-                exits=1,
-            )
+    component = dot_to_object(config, section)
     if hasattr(component, "model"):
         model = component.model
     else:

spacy/errors.py

@@ -592,7 +592,7 @@ class Errors:
             "for the `nlp` pipeline with components {names}.")
     E993 = ("The config for 'nlp' needs to include a key 'lang' specifying "
             "the code of the language to initialize it with (for example "
-            "'en' for English).\n\n{config}")
+            "'en' for English) - this can't be 'None'.\n\n{config}")
     E996 = ("Could not parse {file}: {msg}")
     E997 = ("Tokenizer special cases are not allowed to modify the text. "
             "This would map '{chunk}' to '{orth}' given token attributes "

spacy/tests/test_util.py

@@ -2,7 +2,13 @@ import pytest
 
 from .util import get_random_doc
 
-from spacy.util import minibatch_by_words
+from spacy import util
+from spacy.util import minibatch_by_words, dot_to_object
+from thinc.api import Config, Optimizer
+
+from ..lang.en import English
+from ..lang.nl import Dutch
+from ..language import DEFAULT_CONFIG_PATH
 
 
 @pytest.mark.parametrize(
@@ -56,3 +62,50 @@ def test_util_minibatch_oversize(doc_sizes, expected_batches):
         minibatch_by_words(docs, size=batch_size, tolerance=tol, discard_oversize=False)
     )
     assert [len(batch) for batch in batches] == expected_batches
+
+
+def test_util_dot_section():
+    cfg_string = """
+    [nlp]
+    lang = "en"
+    pipeline = ["textcat"]
+    load_vocab_data = false
+
+    [components]
+
+    [components.textcat]
+    factory = "textcat"
+
+    [components.textcat.model]
+    @architectures = "spacy.TextCatBOW.v1"
+    exclusive_classes = true
+    ngram_size = 1
+    no_output_layer = false
+    """
+    nlp_config = Config().from_str(cfg_string)
+    en_nlp, en_config = util.load_model_from_config(nlp_config, auto_fill=True)
+    print(en_config)
+
+    default_config = Config().from_disk(DEFAULT_CONFIG_PATH)
+    default_config["nlp"]["lang"] = "nl"
+    nl_nlp, nl_config = util.load_model_from_config(default_config, auto_fill=True)
+
+    # Test that creation went OK
+    assert isinstance(en_nlp, English)
+    assert isinstance(nl_nlp, Dutch)
+    assert nl_nlp.pipe_names == []
+    assert en_nlp.pipe_names == ["textcat"]
+    assert en_nlp.get_pipe("textcat").model.attrs["multi_label"] == False   # not exclusive_classes
+
+    # Test that default values got overwritten
+    assert not en_config["nlp"]["load_vocab_data"]
+    assert nl_config["nlp"]["load_vocab_data"]  # default value True
+
+    # Test proper functioning of 'dot_to_object'
+    with pytest.raises(KeyError):
+        obj = dot_to_object(en_config, "nlp.pipeline.tagger")
+    with pytest.raises(KeyError):
+        obj = dot_to_object(en_config, "nlp.unknownattribute")
+    assert not dot_to_object(en_config, "nlp.load_vocab_data")
+    assert dot_to_object(nl_config, "nlp.load_vocab_data")
+    assert isinstance(dot_to_object(nl_config, "training.optimizer"), Optimizer)

spacy/util.py

@@ -258,7 +258,7 @@ def load_model_from_config(
     if "nlp" not in config:
         raise ValueError(Errors.E985.format(config=config))
     nlp_config = config["nlp"]
-    if "lang" not in nlp_config:
+    if "lang" not in nlp_config or nlp_config["lang"] is None:
         raise ValueError(Errors.E993.format(config=nlp_config))
     # This will automatically handle all codes registered via the languages
     # registry, including custom subclasses provided via entry points
@@ -1107,6 +1107,26 @@ def dict_to_dot(obj: Dict[str, dict]) -> Dict[str, Any]:
     return {".".join(key): value for key, value in walk_dict(obj)}
 
 
+def dot_to_object(config: Config, section: str):
+    """Convert dot notation of a "section" to a specific part of the Config.
+    e.g. "training.optimizer" would return the Optimizer object.
+    Throws an error if the section is not defined in this config.
+
+    config (Config): The config.
+    section (str): The dot notation of the section in the config.
+    RETURNS: The object denoted by the section
+    """
+    component = config
+    parts = section.split(".")
+    for item in parts:
+        try:
+            component = component[item]
+        except (KeyError, TypeError) as e:
+            msg = f"The section '{section}' is not a valid section in the provided config."
+            raise KeyError(msg)
+    return component
+
+
 def walk_dict(
     node: Dict[str, Any], parent: List[str] = []
 ) -> Iterator[Tuple[List[str], Any]]: