format and add type def + docstring for split_sentences

recon/corrections.py

@@ -2,7 +2,8 @@
 
 import copy
 from collections import defaultdict
-from typing import DefaultDict, Dict, List
+from typing import Any, DefaultDict, Dict, List
+
 import spacy
 from spacy.tokens import Doc as SpacyDoc, Span as SpacySpan
 
@@ -118,19 +119,31 @@ spacy_pre = SpacyPreProcessor(nlp)
 
 
 @operation("recon.v1.split_sentences", pre=[spacy_pre])
-def split_sentences(example, preprocessed_outputs = {}):    
+def split_sentences(example: Example, preprocessed_outputs: Dict[str, Any] = {}) -> List[Example]:
+    """Split a single example into multiple examples by splitting the text into 
+    multiple sentences and resetting entity and token offsets based on offsets 
+    relative to sentence boundaries
+
+    Args:
+        example (Example): Input Example
+        preprocessed_outputs (Dict[str, Any], optional): Outputs of preprocessors.
+
+    Returns:
+        List[Example]: List of split examples. 
+            Could be list of 1 if the example is just one sentence.
+    """
     doc = preprocessed_outputs["recon.v1.spacy"]
-    
+
     new_examples = []
     ents = []
     for ent in example.spans:
         span = doc.char_span(ent.start, ent.end, label=ent.label)
         if not span:
             token = None
-            text = doc.text[ent.start:ent.end]
+            text = doc.text[ent.start : ent.end]
             for t in doc:
                 if t.text == text:
-                    token = t 
+                    token = t
             if token:
                 span = SpacySpan(doc, token.i, token.i + 1, label=ent.label)
         ents.append(span)
@@ -141,20 +154,21 @@ def split_sentences(example, preprocessed_outputs = {}):
         sent_doc = sent.as_doc()
         new_example = Example(
             text=sent_doc.text,
-            spans=[Span(
-                text=e.text,
-                start=e.start_char,
-                end=e.end_char,
-                token_start=e.start,
-                token_end=e.end,
-                label=e.label_
-            ) for e in sent_doc.ents],
-            tokens=[Token(
-                text=t.text,
-                start=t.idx,
-                end=t.idx + len(t.text),
-                id=i
-            ) for i, t in enumerate(sent_doc)]
+            spans=[
+                Span(
+                    text=e.text,
+                    start=e.start_char,
+                    end=e.end_char,
+                    token_start=e.start,
+                    token_end=e.end,
+                    label=e.label_,
+                )
+                for e in sent_doc.ents
+            ],
+            tokens=[
+                Token(text=t.text, start=t.idx, end=t.idx + len(t.text), id=i)
+                for i, t in enumerate(sent_doc)
+            ],
         )
         new_examples.append(new_example)
     return new_examples

recon/dataset.py

@@ -66,7 +66,7 @@ class Dataset:
         data: List[Example] = [],
         operations: List[OperationState] = None,
         example_store: ExampleStore = None,
-        verbose: bool = False
+        verbose: bool = False,
     ):
         self.name = name
         self.data = data
@@ -151,10 +151,10 @@ class Dataset:
         Args:
             operations (List[Union[str, OperationState]]): List of operations
         """
-        
+
         msg = Printer(no_print=self.verbose == False)
         msg.text(f"Applying pipeline of operations inplace to the dataset: {self.name}")
-        
+
         for op in operations:
             op_name = op.name if isinstance(op, OperationState) else op
             msg.text(f"|_ {op_name}")

recon/operations.py

@@ -116,7 +116,7 @@ class Operation:
         """
         verbose = True
         msg = Printer(no_print=verbose == False)
-    
+
         initial_state = kwargs.pop("initial_state") if "initial_state" in kwargs else None
         verbose = kwargs.pop("verbose") if "verbose" in kwargs else None
         if not initial_state:
@@ -150,7 +150,9 @@ class Operation:
             dataset.example_store.add(new_example)
 
         new_data = []
-        for orig_example_hash, example, preprocessed_outputs in op_iter(dataset.data, self.pre, verbose=verbose):
+        for orig_example_hash, example, preprocessed_outputs in op_iter(
+            dataset.data, self.pre, verbose=verbose
+        ):
             if preprocessed_outputs:
                 res = self.op(example, *args, preprocessed_outputs=preprocessed_outputs, **kwargs)
             else: