Clean up code

gptc/__init__.py

@@ -2,12 +2,11 @@
 
 """General-Purpose Text Classifier"""
 
-from gptc.compiler import compile as compile
+from gptc.pack import pack
-from gptc.pack import pack as pack
+from gptc.model import Model, deserialize
-from gptc.model import Model as Model, deserialize as deserialize
+from gptc.tokenizer import normalize
-from gptc.tokenizer import normalize as normalize
 from gptc.exceptions import (
-    GPTCError as GPTCError,
+    GPTCError,
-    ModelError as ModelError,
+    ModelError,
-    InvalidModelError as InvalidModelError,
+    InvalidModelError,
 )

gptc/__main__.py

@@ -59,16 +59,16 @@ def main() -> None:
     args = parser.parse_args()
 
     if args.subparser_name == "compile":
-        with open(args.model, "r") as f:
+        with open(args.model, "r", encoding="utf-8") as input_file:
-            model = json.load(f)
+            model = json.load(input_file)
 
-        with open(args.out, "wb+") as f:
+        with open(args.out, "wb+") as output_file:
-            gptc.compile(
+            gptc.Model.compile(
                 model, args.max_ngram_length, args.min_count
-            ).serialize(f)
+            ).serialize(output_file)
     elif args.subparser_name == "classify":
-        with open(args.model, "rb") as f:
+        with open(args.model, "rb") as model_file:
-            model = gptc.deserialize(f)
+            model = gptc.deserialize(model_file)
 
         if sys.stdin.isatty():
             text = input("Text to analyse: ")
@@ -77,8 +77,8 @@ def main() -> None:
 
         print(json.dumps(model.confidence(text, args.max_ngram_length)))
     elif args.subparser_name == "check":
-        with open(args.model, "rb") as f:
+        with open(args.model, "rb") as model_file:
-            model = gptc.deserialize(f)
+            model = gptc.deserialize(model_file)
         print(json.dumps(model.get(args.token)))
     else:
         print(json.dumps(gptc.pack(args.model, True)[0]))

gptc/compiler.py

@@ -1,8 +1,8 @@
 # SPDX-License-Identifier: GPL-3.0-or-later
 
+from typing import Iterable, Mapping, List, Dict, Tuple
 import gptc.tokenizer
 import gptc.model
-from typing import Iterable, Mapping, List, Dict, Union, Tuple
 
 
 def _count_words(
@@ -15,7 +15,7 @@ def _count_words(
     names: List[str] = []
 
     for portion in raw_model:
-        text = gptc.tokenizer.hash(
+        text = gptc.tokenizer.hash_list(
             gptc.tokenizer.tokenize(portion["text"], max_ngram_length),
             hash_algorithm,
         )
@@ -63,7 +63,7 @@ def _get_weights(
     return model
 
 
-def compile(
+def compile_(
     raw_model: Iterable[Mapping[str, str]],
     max_ngram_length: int = 1,
     min_count: int = 1,

gptc/model.py

@@ -1,22 +1,18 @@
 # SPDX-License-Identifier: GPL-3.0-or-later
 
-import gptc.tokenizer
-from gptc.exceptions import InvalidModelError
-import gptc.weighting
 from typing import (
-    Iterable,
-    Mapping,
     List,
     Dict,
-    Union,
     cast,
     BinaryIO,
-    Any,
     Tuple,
     TypedDict,
 )
 import json
-import collections
+import gptc.tokenizer
+from gptc.exceptions import InvalidModelError
+import gptc.weighting
+import gptc.compiler
 
 
 class ExplanationEntry(TypedDict):
@@ -33,6 +29,21 @@ Explanation = Dict[
 Log = List[Tuple[str, float, List[float]]]
 
 
+class Confidences(dict[str, float]):
+    def __init__(self, probs: Dict[str, float]):
+        dict.__init__(self, probs)
+
+
+class TransparentConfidences(Confidences):
+    def __init__(
+        self,
+        probs: Dict[str, float],
+        explanation: Explanation,
+    ):
+        self.explanation = explanation
+        Confidences.__init__(self, probs)
+
+
 def convert_log(log: Log, names: List[str]) -> Explanation:
     explanation: Explanation = {}
     for word2, weight, word_probs in log:
@@ -49,33 +60,6 @@ def convert_log(log: Log, names: List[str]) -> Explanation:
     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,
@@ -117,7 +101,7 @@ class Model:
             text, min(max_ngram_length, self.max_ngram_length)
         )
 
-        tokens = gptc.tokenizer.hash(
+        tokens = gptc.tokenizer.hash_list(
             raw_tokens,
             self.hash_algorithm,
         )
@@ -163,12 +147,9 @@ class Model:
 
         if transparent:
             explanation = convert_log(log, self.names)
+            return TransparentConfidences(probs, explanation)
 
-            return TransparentConfidences(
+        return Confidences(probs)
-                probs, explanation, self, text, max_ngram_length
-            )
-        else:
-            return Confidences(probs, self, text, max_ngram_length)
 
     def get(self, token: str) -> Dict[str, float]:
         try:
@@ -202,6 +183,8 @@ class Model:
                 + b"".join([weight.to_bytes(2, "big") for weight in weights])
             )
 
+    compile = staticmethod(gptc.compiler.compile_)
+
 
 def deserialize(encoded_model: BinaryIO) -> Model:
     prefix = encoded_model.read(14)
@@ -213,26 +196,27 @@ def deserialize(encoded_model: BinaryIO) -> Model:
         byte = encoded_model.read(1)
         if byte == b"\n":
             break
-        elif byte == b"":
+
+        if byte == b"":
             raise InvalidModelError()
-        else:
+
         config_json += byte
 
     try:
         config = json.loads(config_json.decode("utf-8"))
-    except (UnicodeDecodeError, json.JSONDecodeError):
+    except (UnicodeDecodeError, json.JSONDecodeError) as exc:
-        raise InvalidModelError()
+        raise InvalidModelError() from exc
 
     try:
         names = config["names"]
         max_ngram_length = config["max_ngram_length"]
         hash_algorithm = config["hash_algorithm"]
-    except KeyError:
+    except KeyError as exc:
-        raise InvalidModelError()
+        raise InvalidModelError() from exc
 
     if not (
         isinstance(names, list) and isinstance(max_ngram_length, int)
-    ) or not all([isinstance(name, str) for name in names]):
+    ) or not all(isinstance(name, str) for name in names):
         raise InvalidModelError()
 
     weight_code_length = 6 + 2 * len(names)
@@ -243,7 +227,7 @@ def deserialize(encoded_model: BinaryIO) -> Model:
         code = encoded_model.read(weight_code_length)
         if not code:
             break
-        elif len(code) != weight_code_length:
+        if len(code) != weight_code_length:
             raise InvalidModelError()
 
         weights[int.from_bytes(code[:6], "big")] = [

gptc/pack.py

@@ -7,7 +7,7 @@ from typing import List, Dict, Tuple
 
 def pack(
     directory: str, print_exceptions: bool = False
-) -> Tuple[List[Dict[str, str]], List[Tuple[Exception]]]:
+) -> Tuple[List[Dict[str, str]], List[Tuple[OSError]]]:
     paths = os.listdir(directory)
     texts: Dict[str, List[str]] = {}
     exceptions = []
@@ -17,16 +17,18 @@ def pack(
         try:
             for file in os.listdir(os.path.join(directory, path)):
                 try:
-                    with open(os.path.join(directory, path, file)) as f:
+                    with open(
-                        texts[path].append(f.read())
+                        os.path.join(directory, path, file), encoding="utf-8"
-                except Exception as e:
+                    ) as input_file:
-                    exceptions.append((e,))
+                        texts[path].append(input_file.read())
+                except OSError as error:
+                    exceptions.append((error,))
                     if print_exceptions:
-                        print(e, file=sys.stderr)
+                        print(error, file=sys.stderr)
-        except Exception as e:
+        except OSError as error:
-            exceptions.append((e,))
+            exceptions.append((error,))
             if print_exceptions:
-                print(e, file=sys.stderr)
+                print(error, file=sys.stderr)
 
     raw_model = []
 

gptc/tokenizer.py

@@ -1,9 +1,9 @@
 # SPDX-License-Identifier: GPL-3.0-or-later
 
-from typing import List, Union, Callable, Any, cast
+import unicodedata
+from typing import List, cast
 import hashlib
 import emoji
-import unicodedata
 
 
 def tokenize(text: str, max_ngram_length: int = 1) -> List[str]:
@@ -37,7 +37,7 @@ def tokenize(text: str, max_ngram_length: int = 1) -> List[str]:
 
     if max_ngram_length == 1:
         return tokens
-    else:
+
     ngrams = []
     for ngram_length in range(1, max_ngram_length + 1):
         for index in range(len(tokens) + 1 - ngram_length):
@@ -69,7 +69,7 @@ def _get_hash_function(hash_algorithm: str) -> type:
         "sha3_384",
     }:
         return cast(type, getattr(hashlib, hash_algorithm))
-    else:
+
     raise ValueError("not a valid hash function: " + hash_algorithm)
 
 
@@ -77,7 +77,7 @@ def hash_single(token: str, hash_algorithm: str) -> int:
     return _hash_single(token, _get_hash_function(hash_algorithm))
 
 
-def hash(tokens: List[str], hash_algorithm: str) -> List[int]:
+def hash_list(tokens: List[str], hash_algorithm: str) -> List[int]:
     hash_function = _get_hash_function(hash_algorithm)
     return [_hash_single(token, hash_function) for token in tokens]
 

gptc/weighting.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: GPL-3.0-or-later
 
 import math
-from typing import Sequence, Union, Tuple, List
+from typing import Sequence, Tuple, List
 
 
 def _mean(numbers: Sequence[float]) -> float:
@@ -41,6 +41,6 @@ def _standard_deviation(numbers: Sequence[float]) -> float:
 
 def weight(numbers: Sequence[float]) -> Tuple[float, List[float]]:
     standard_deviation = _standard_deviation(numbers)
-    weight = standard_deviation * 2
+    weight_assigned = standard_deviation * 2
-    weighted_numbers = [i * weight for i in numbers]
+    weighted_numbers = [i * weight_assigned for i in numbers]
-    return weight, weighted_numbers
+    return weight_assigned, weighted_numbers