Add type checks to all functions that need them

gptc/__init__.py

@@ -2,6 +2,10 @@
 
 """General-Purpose Text Classifier"""
 
-from gptc.compiler import compile
+from gptc.compiler import compile as compile
-from gptc.classifier import Classifier
+from gptc.classifier import Classifier as Classifier
-from gptc.exceptions import *
+from gptc.exceptions import (
+    GPTCError as GPTCError,
+    ModelError as ModelError,
+    UnsupportedModelError as UnsupportedModelError,
+)

gptc/__main__.py

@@ -13,9 +13,7 @@ def main() -> None:
     )
     subparsers = parser.add_subparsers(dest="subparser_name", required=True)
 
-    compile_parser = subparsers.add_parser(
+    compile_parser = subparsers.add_parser("compile", help="compile a raw model")
-        "compile", help="compile a raw model"
-    )
     compile_parser.add_argument("model", help="raw model to compile")
     compile_parser.add_argument(
         "--max-ngram-length",

gptc/classifier.py

@@ -27,14 +27,10 @@ class Classifier:
 
     def __init__(self, model: gptc.compiler.MODEL, max_ngram_length: int = 1):
         if model.get("__version__", 0) != 3:
-            raise gptc.exceptions.UnsupportedModelError(
+            raise gptc.exceptions.UnsupportedModelError(f"unsupported model version")
-                f"unsupported model version"
-            )
         self.model = model
         model_ngrams = cast(int, model.get("__ngrams__", 1))
-        self.max_ngram_length = min(
+        self.max_ngram_length = min(max_ngram_length, model_ngrams)
-            max_ngram_length, model_ngrams
-        )
 
     def confidence(self, text: str) -> Dict[str, float]:
         """Classify text with confidence.

gptc/compiler.py

@@ -8,9 +8,7 @@ CONFIG_T = Union[List[str], int, str]
 MODEL = Dict[str, Union[WEIGHTS_T, CONFIG_T]]
 
 
-def compile(
+def compile(raw_model: Iterable[Mapping[str, str]], max_ngram_length: int = 1) -> MODEL:
-    raw_model: Iterable[Mapping[str, str]], max_ngram_length: int = 1
-) -> MODEL:
     """Compile a raw model.
 
     Parameters
@@ -49,13 +47,9 @@ def compile(
         categories_by_count[category] = {}
         for word in text:
             try:
-                categories_by_count[category][word] += 1 / len(
+                categories_by_count[category][word] += 1 / len(categories[category])
-                    categories[category]
-                )
             except KeyError:
-                categories_by_count[category][word] = 1 / len(
+                categories_by_count[category][word] = 1 / len(categories[category])
-                    categories[category]
-                )
     word_weights: Dict[str, Dict[str, float]] = {}
     for category, words in categories_by_count.items():
         for word, value in words.items():
@@ -69,9 +63,7 @@ def compile(
         total = sum(weights.values())
         new_weights: List[int] = []
         for category in names:
-            new_weights.append(
+            new_weights.append(round((weights.get(category, 0) / total) * 65535))
-                round((weights.get(category, 0) / total) * 65535)
-            )
         model[word] = new_weights
 
     model["__names__"] = names

gptc/tokenizer.py

@@ -1,6 +1,7 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
 
 from typing import List, Union
+
 try:
     import emoji
 
@@ -9,7 +10,7 @@ except ImportError:
     has_emoji = False
 
 
-def tokenize(text: str, max_ngram_length: int=1) -> List[str]:
+def tokenize(text: str, max_ngram_length: int = 1) -> List[str]:
     """Convert a string to a list of lemmas."""
     converted_text: Union[str, List[str]] = text.lower()