Integrate and simplify pipe analysis

spacy/language.py

@@ -18,7 +18,7 @@ from timeit import default_timer as timer
 
 from .tokens.underscore import Underscore
 from .vocab import Vocab, create_vocab
-from .pipe_analysis import analyze_pipes, analyze_all_pipes, validate_attrs
+from .pipe_analysis import validate_attrs, print_summary
 from .gold import Example
 from .scorer import Scorer
 from .util import create_default_optimizer, registry
@@ -37,8 +37,6 @@ from . import util
 from . import about
 
 
-# TODO: integrate pipeline analyis
-ENABLE_PIPELINE_ANALYSIS = False
 # This is the base config will all settings (training etc.)
 DEFAULT_CONFIG_PATH = Path(__file__).parent / "default_config.cfg"
 DEFAULT_CONFIG = Config().from_disk(DEFAULT_CONFIG_PATH)
@@ -522,6 +520,24 @@ class Language:
             return add_component(func)
         return add_component
 
+    def analyze_pipes(
+        self,
+        *,
+        keys: List[str] = ["assigns", "requires", "scores", "retokenizes"],
+        pretty: bool = True,
+        no_print: bool = False,
+    ) -> Optional[Dict[str, Any]]:
+        """Analyze the current pipeline components, print a summary of what
+        they assign or require and check that all requirements are met.
+
+        keys (List[str]): The meta values to display in the table. Corresponds
+            to values in FactoryMeta, defined by @Language.factory decorator.
+        pretty (bool): Pretty-print the results with colors and icons.
+        no_print (bool): Don't print anything and return structured dict instead.
+        RETURNS (dict): The data, if no_print is set to True.
+        """
+        return print_summary(self, keys=keys, pretty=pretty, no_print=no_print)
+
     def get_pipe(self, name: str) -> Callable[[Doc], Doc]:
         """Get a pipeline component for a given component name.
 
@@ -666,8 +682,6 @@ class Language:
         pipe_index = self._get_pipe_index(before, after, first, last)
         self._pipe_meta[name] = self.get_factory_meta(factory_name)
         self.pipeline.insert(pipe_index, (name, pipe_component))
-        if ENABLE_PIPELINE_ANALYSIS:
-            analyze_pipes(self, name, pipe_index)
         return pipe_component
 
     def _get_pipe_index(
@@ -758,8 +772,6 @@ class Language:
             self.add_pipe(factory_name, name=name)
         else:
             self.add_pipe(factory_name, name=name, before=pipe_index)
-        if ENABLE_PIPELINE_ANALYSIS:
-            analyze_all_pipes(self)
 
     def rename_pipe(self, old_name: str, new_name: str) -> None:
         """Rename a pipeline component.
@@ -793,8 +805,6 @@ class Language:
         # because factory may be used for something else
         self._pipe_meta.pop(name)
         self._pipe_configs.pop(name)
-        if ENABLE_PIPELINE_ANALYSIS:
-            analyze_all_pipes(self)
         return removed
 
     def __call__(

spacy/pipe_analysis.py

@@ -42,19 +42,6 @@ def analyze_pipes(
     return problems
 
 
-def analyze_all_pipes(nlp: "Language", warn: bool = True) -> Dict[str, List[str]]:
-    """Analyze all pipes in the pipeline in order.
-
-    nlp (Language): The current nlp object.
-    warn (bool): Show user warning if problem is found.
-    RETURNS (Dict[str, List[str]]): The problems found, keyed by component name.
-    """
-    problems = {}
-    for i, name in enumerate(nlp.pipe_names):
-        problems[name] = analyze_pipes(nlp, name, i, warn=warn)
-    return problems
-
-
 def validate_attrs(values: Iterable[str]) -> Iterable[str]:
     """Validate component attributes provided to "assigns", "requires" etc.
     Raises error for invalid attributes and formatting. Doesn't check if
@@ -133,27 +120,35 @@ def get_requires_for_attr(nlp: "Language", attr: str) -> List[str]:
 
 
 def print_summary(
-    nlp: "Language", pretty: bool = True, no_print: bool = False
+    nlp: "Language",
+    *,
+    keys: List[str] = ["requires", "assigns", "scores", "retokenizes"],
+    pretty: bool = True,
+    no_print: bool = False,
 ) -> Optional[Dict[str, Union[List[str], Dict[str, List[str]]]]]:
     """Print a formatted summary for the current nlp object's pipeline. Shows
     a table with the pipeline components and why they assign and require, as
     well as any problems if available.
 
     nlp (Language): The nlp object.
+    keys (List[str]): The meta keys to show in the table.
     pretty (bool): Pretty-print the results (color etc).
     no_print (bool): Don't print anything, just return the data.
     RETURNS (dict): A dict with "overview" and "problems".
     """
     msg = Printer(pretty=pretty, no_print=no_print)
-    overview = []
+    overview = {}
     problems = {}
     for i, name in enumerate(nlp.pipe_names):
         meta = nlp.get_pipe_meta(name)
-        overview.append((i, name, meta.requires, meta.assigns, meta.retokenizes))
+        overview[name] = {"i": i, "name": name}
+        for key in keys:
+            overview[name][key] = getattr(meta, key, None)
         problems[name] = analyze_pipes(nlp, name, i, warn=False)
     msg.divider("Pipeline Overview")
-    header = ("#", "Component", "Requires", "Assigns", "Retokenizes")
-    msg.table(overview, header=header, divider=True, multiline=True)
+    header = ["#", "Component", *[key.capitalize() for key in keys]]
+    body = [[info for info in entry.values()] for entry in overview.values()]
+    msg.table(body, header=header, divider=True, multiline=True)
     n_problems = sum(len(p) for p in problems.values())
     if any(p for p in problems.values()):
         msg.divider(f"Problems ({n_problems})")

spacy/tests/pipeline/test_analysis.py

@@ -1,15 +1,12 @@
 import spacy.language
 from spacy.language import Language
-from spacy.pipe_analysis import print_summary, validate_attrs
 from spacy.pipe_analysis import get_assigns_for_attr, get_requires_for_attr
-from spacy.pipe_analysis import count_pipeline_interdependencies
+from spacy.pipe_analysis import validate_attrs, count_pipeline_interdependencies
 from mock import Mock
 import pytest
 
 
 def test_component_decorator_assigns():
-    spacy.language.ENABLE_PIPELINE_ANALYSIS = True
-
     @Language.component("c1", assigns=["token.tag", "doc.tensor"])
     def test_component1(doc):
         return doc
@@ -32,8 +29,9 @@ def test_component_decorator_assigns():
 
     nlp = Language()
     nlp.add_pipe("c1")
-    with pytest.warns(UserWarning):
-        nlp.add_pipe("c2")
+    nlp.add_pipe("c2")
+    problems = nlp.analyze_pipes(no_print=True)["problems"]
+    assert problems["c2"] == ["token.pos"]
     nlp.add_pipe("c3")
     assert get_assigns_for_attr(nlp, "doc.tensor") == ["c1", "c2"]
     nlp.add_pipe("c1", name="c4")
@@ -45,7 +43,6 @@ def test_component_decorator_assigns():
     assert nlp.pipe_factories["c4"] == "c1"
     assert get_assigns_for_attr(nlp, "doc.tensor") == ["c1", "c2", "c4"]
     assert get_requires_for_attr(nlp, "token.pos") == ["c2"]
-    assert print_summary(nlp, no_print=True)
     assert nlp("hello world")
 
 
@@ -112,11 +109,12 @@ def test_analysis_validate_attrs_remove_pipe():
 
     nlp = Language()
     nlp.add_pipe("pipe_analysis_c6")
-    with pytest.warns(UserWarning):
-        nlp.add_pipe("pipe_analysis_c7")
-    with pytest.warns(None) as record:
-        nlp.remove_pipe("pipe_analysis_c7")
-    assert not record.list
+    nlp.add_pipe("pipe_analysis_c7")
+    problems = nlp.analyze_pipes(no_print=True)["problems"]
+    assert problems["pipe_analysis_c7"] == ["token.pos"]
+    nlp.remove_pipe("pipe_analysis_c7")
+    problems = nlp.analyze_pipes(no_print=True)["problems"]
+    assert all(p == [] for p in problems.values())
 
 
 def test_pipe_interdependencies():