Fix type annotations

gptc/model.py

@@ -3,11 +3,79 @@
 import gptc.tokenizer
 from gptc.exceptions import InvalidModelError
 import gptc.weighting
-from typing import Iterable, Mapping, List, Dict, Union, cast, BinaryIO
+from typing import (
+    Iterable,
+    Mapping,
+    List,
+    Dict,
+    Union,
+    cast,
+    BinaryIO,
+    Any,
+    Tuple,
+    TypedDict,
+)
 import json
 import collections
 
 
+class ExplanationEntry(TypedDict):
+    weight: float
+    probabilities: Dict[str, float]
+    count: int
+
+
+Explanation = Dict[
+    str,
+    ExplanationEntry,
+]
+
+Log = List[Tuple[str, float, List[float]]]
+
+
+def convert_log(log: Log, names: List[str]) -> Explanation:
+    explanation: Explanation = {}
+    for word2, weight, word_probs in log:
+        if word2 in explanation:
+            explanation[word2]["count"] += 1
+        else:
+            explanation[word2] = {
+                "weight": weight,
+                "probabilities": {
+                    name: word_probs[index] for index, name in enumerate(names)
+                },
+                "count": 1,
+            }
+    return explanation
+
+
+class Confidences(collections.UserDict[str, float]):
+    def __init__(
+        self,
+        probs: Dict[str, float],
+        model: Model,
+        text: str,
+        max_ngram_length: int,
+    ):
+        collections.UserDict.__init__(self, probs)
+        self.model = model
+        self.text = text
+        self.max_ngram_length = max_ngram_length
+
+
+class TransparentConfidences(Confidences):
+    def __init__(
+        self,
+        probs: Dict[str, float],
+        explanation: Explanation,
+        model: Model,
+        text: str,
+        max_ngram_length: int,
+    ):
+        Confidences.__init__(self, probs, model, text, max_ngram_length)
+        self.explanation = explanation
+
+
 class Model:
     def __init__(
         self,
@@ -23,7 +91,7 @@ class Model:
 
     def confidence(
         self, text: str, max_ngram_length: int, transparent: bool = False
-    ) -> Dict[str, float]:
+    ) -> Confidences:
         """Classify text with confidence.
 
         Parameters
@@ -56,7 +124,7 @@ class Model:
 
         if transparent:
             token_map = {tokens[i]: raw_tokens[i] for i in range(len(tokens))}
-            log = []
+            log: Log = []
 
         numbered_probs: Dict[int, float] = {}
 
@@ -71,7 +139,13 @@ class Model:
                 )
 
                 if transparent:
-                    log.append([token_map[word], weight, unweighted_numbers])
+                    log.append(
+                        (
+                            token_map[word],
+                            weight,
+                            unweighted_numbers,
+                        )
+                    )
 
                 for category, value in enumerate(weighted_numbers):
                     try:
@@ -88,19 +162,7 @@ class Model:
         }
 
         if transparent:
-            explanation = {}
+            explanation = convert_log(log, self.names)
-            for word, weight, word_probs in log:
-                if word in explanation:
-                    explanation[word]["count"] += 1
-                else:
-                    explanation[word] = {
-                        "weight": weight,
-                        "probabilities": {
-                            name: word_probs[index]
-                            for index, name in enumerate(self.names)
-                        },
-                        "count": 1,
-                    }
 
             return TransparentConfidences(
                 probs, explanation, self, text, max_ngram_length
@@ -141,20 +203,6 @@ class Model:
             )
 
 
-class Confidences(collections.UserDict):
-    def __init__(self, probs, model, text, max_ngram_length):
-        collections.UserDict.__init__(self, probs)
-        self.model = model
-        self.text = text
-        self.max_ngram_length = max_ngram_length
-
-
-class TransparentConfidences(Confidences):
-    def __init__(self, probs, explanation, model, text, max_ngram_length):
-        Confidences.__init__(self, probs, model, text, max_ngram_length)
-        self.explanation = explanation
-
-
 def deserialize(encoded_model: BinaryIO) -> Model:
     prefix = encoded_model.read(14)
     if prefix != b"GPTC model v6\n":

gptc/weighting.py

@@ -39,7 +39,7 @@ def _standard_deviation(numbers: Sequence[float]) -> float:
     return math.sqrt(_mean(squared_deviations))
 
 
-def weight(numbers: Sequence[float]) -> List[float]:
+def weight(numbers: Sequence[float]) -> Tuple[float, List[float]]:
     standard_deviation = _standard_deviation(numbers)
     weight = standard_deviation * 2
     weighted_numbers = [i * weight for i in numbers]