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Update docs [ci skip]
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Ines Montani
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@ -598,6 +598,65 @@ contains the information about the component and its default provided by the
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| `name` | str | The pipeline component name. |
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| **RETURNS** | [`FactoryMeta`](#factorymeta) | The factory meta. |
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## Language.analyze_pipes {#analyze_pipes tag="method" new="3"}
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Analyze the current pipeline components and show a summary of the attributes
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they assign and require, and the scores they set. The data is based on the
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information provided in the [`@Language.component`](/api/language#component) and
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[`@Language.factory`](/api/language#factory) decorator. If requirements aren't
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met, e.g. if a component specifies a required property that is not set by a
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previous component, a warning is shown.
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<Infobox variant="warning" title="Important note">
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The pipeline analysis is static and does **not actually run the components**.
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This means that it relies on the information provided by the components
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themselves. If a custom component declares that it assigns an attribute but it
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doesn't, the pipeline analysis won't catch that.
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</Infobox>
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> #### Example
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>
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> ```python
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> nlp = spacy.blank("en")
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> nlp.add_pipe("tagger")
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> nlp.add_pipe("entity_linker")
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> nlp.analyze_pipes()
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> ```
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<Accordion title="Example output" spaced>
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```
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============================= Pipeline Overview =============================
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# Component Assigns Requires Scores Retokenizes
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- ------------- --------------- -------------- --------- -----------
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0 tagger token.tag tag_acc False
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pos_acc
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lemma_acc
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1 entity_linker token.ent_kb_id doc.ents False
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doc.sents
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token.ent_iob
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token.ent_type
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================================ Problems (4) ================================
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⚠ 'entity_linker' requirements not met: doc.ents, doc.sents,
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token.ent_iob, token.ent_type
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```
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</Accordion>
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| Name | Type | Description |
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| -------------- | ----------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| _keyword-only_ | | |
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| `keys` | `List[str]` | The values to display in the table. Corresponds to attributes of the [`FactoryMeta`](/api/language#factorymeta). Defaults to `["assigns", "requires", "scores", "retokenizes"]`. |
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| `pretty` | bool | Pretty-print the results with colors and icons. Defaults to `True`. |
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| `no_print` | bool | Don't print anything and return a structured dict instead. Defaults to `False`. |
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| **RETURNS** | dict | Optional dict, if `no_print` is set to `True`. |
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## Language.meta {#meta tag="property"}
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Custom meta data for the Language class. If a model is loaded, contains meta
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@ -311,6 +311,61 @@ nlp.rename_pipe("ner", "entityrecognizer")
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nlp.replace_pipe("tagger", my_custom_tagger)
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```
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### Analyzing pipeline components {#analysis new="3"}
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The [`nlp.analyze_pipes`](/api/language#analyze_pipes) method analyzes the
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components in the current pipeline and outputs information about them, like the
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attributes they set on the [`Doc`](/api/doc) and [`Token`](/api/token), whether
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they retokenize the `Doc` and which scores they produce during training. It will
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also show warnings if components require values that aren't set by previous
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component – for instance, if the entity linker is used but no component that
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runs before it sets named entities.
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```python
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nlp = spacy.blank("en")
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nlp.add_pipe("tagger")
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nlp.add_pipe("entity_linker") # this is a problem, because it needs entities
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nlp.analyze_pipes()
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```
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```
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### Example output
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============================= Pipeline Overview =============================
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# Component Assigns Requires Scores Retokenizes
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- ------------- --------------- -------------- --------- -----------
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0 tagger token.tag tag_acc False
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pos_acc
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lemma_acc
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1 entity_linker token.ent_kb_id doc.ents False
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doc.sents
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token.ent_iob
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token.ent_type
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================================ Problems (4) ================================
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⚠ 'entity_linker' requirements not met: doc.ents, doc.sents,
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token.ent_iob, token.ent_type
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```
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If you prefer a structured dictionary containing the component information and
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the problems, you can set `no_print=True`. This will return the data instead of
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printing it.
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```
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result = nlp.analyze_pipes(no_print=True)
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```
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<Infobox variant="warning" title="Important note">
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The pipeline analysis is static and does **not actually run the components**.
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This means that it relies on the information provided by the components
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themselves. If a custom component declares that it assigns an attribute but it
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doesn't, the pipeline analysis won't catch that.
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</Infobox>
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## Creating custom pipeline components {#custom-components}
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A pipeline component is a function that receives a `Doc` object, modifies it and
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