Allow hash algorithm selection

Closes #9

README.md

@@ -69,7 +69,7 @@ Return a confidence dict for the given token or ngram. This function is very
 similar to `Model.confidence()`, except it treats the input as a single token
 or ngram.
 
-### `gptc.compile(raw_model, max_ngram_length=1, min_count=1)`
+### `gptc.compile(raw_model, max_ngram_length=1, min_count=1, hash_algorithm="sha256")`
 
 Compile a raw model (as a list, not JSON) and return the compiled model (as a
 `gptc.Model` object).
@@ -79,6 +79,26 @@ For information about `max_ngram_length`, see section "Ngrams."
 Words or ngrams used less than `min_count` times throughout the input text are
 excluded from the model.
 
+The hash algorithm should be left as the default, which may change with a minor
+version update, but it can be changed by the application if needed. It is
+stored in the model, so changing the algorithm does not affect compatibility.
+The following algorithms are supported:
+
+* `md5`
+* `sha1`
+* `sha224`
+* `sha256`
+* `sha384`
+* `sha512`
+* `sha3_224`
+* `sha3_384`
+* `sha3_256`
+* `sha3_512`
+* `shake_128`
+* `shake_256`
+* `blake2b`
+* `blake2s`
+
 ### `gptc.pack(directory, print_exceptions=False)`
 
 Pack the model in `directory` and return a tuple of the format:

gptc/compiler.py

@@ -9,6 +9,7 @@ def compile(
     raw_model: Iterable[Mapping[str, str]],
     max_ngram_length: int = 1,
     min_count: int = 1,
+    hash_algorithm: str = "sha256",
 ) -> gptc.model.Model:
     """Compile a raw model.
 
@@ -33,7 +34,8 @@ def compile(
 
     for portion in raw_model:
         text = gptc.tokenizer.hash(
-            gptc.tokenizer.tokenize(portion["text"], max_ngram_length)
+            gptc.tokenizer.tokenize(portion["text"], max_ngram_length),
+            hash_algorithm,
         )
         category = portion["category"]
 
@@ -68,4 +70,4 @@ def compile(
                 )
             model[word] = new_weights
 
-    return gptc.model.Model(model, names, max_ngram_length)
+    return gptc.model.Model(model, names, max_ngram_length, hash_algorithm)

gptc/model.py

@@ -13,10 +13,12 @@ class Model:
         weights: Dict[int, List[int]],
         names: List[str],
         max_ngram_length: int,
+        hash_algorithm: str,
     ):
         self.weights = weights
         self.names = names
         self.max_ngram_length = max_ngram_length
+        self.hash_algorithm = hash_algorithm
 
     def confidence(self, text: str, max_ngram_length: int) -> Dict[str, float]:
         """Classify text with confidence.
@@ -42,7 +44,8 @@ class Model:
         tokens = gptc.tokenizer.hash(
             gptc.tokenizer.tokenize(
                 text, min(max_ngram_length, self.max_ngram_length)
-            )
+            ),
+            self.hash_algorithm,
         )
         numbered_probs: Dict[int, float] = {}
         for word in tokens:
@@ -83,6 +86,7 @@ class Model:
                 {
                     "names": self.names,
                     "max_ngram_length": self.max_ngram_length,
+                    "hash_algorithm": self.hash_algorithm,
                 }
             ).encode("utf-8")
             + b"\n"
@@ -117,6 +121,7 @@ def deserialize(encoded_model: BinaryIO) -> Model:
     try:
         names = config["names"]
         max_ngram_length = config["max_ngram_length"]
+        hash_algorithm = config["hash_algorithm"]
     except KeyError:
         raise InvalidModelError()
 
@@ -141,4 +146,4 @@ def deserialize(encoded_model: BinaryIO) -> Model:
             for value in [code[x : x + 2] for x in range(6, len(code), 2)]
         ]
 
-    return Model(weights, names, max_ngram_length)
+    return Model(weights, names, max_ngram_length, hash_algorithm)

gptc/tokenizer.py

@@ -1,6 +1,6 @@
 # SPDX-License-Identifier: GPL-3.0-or-later
 
-from typing import List, Union
+from typing import List, Union, Callable
 import hashlib
 import emoji
 import unicodedata
@@ -45,14 +45,33 @@ def tokenize(text: str, max_ngram_length: int = 1) -> List[str]:
         return ngrams
 
 
-def hash_single(token: str) -> int:
+def hash_single(token: str, hash_function: Callable) -> int:
     return int.from_bytes(
-        hashlib.sha256(token.encode("utf-8")).digest()[:6], "big"
+        hash_function(token.encode("utf-8")).digest()[:6], "big"
     )
 
 
-def hash(tokens: List[str]) -> List[int]:
-    return [hash_single(token) for token in tokens]
+def hash(tokens: List[str], hash_algorithm: str) -> List[int]:
+    if hash_algorithm in {
+        "sha224",
+        "md5",
+        "sha512",
+        "sha3_256",
+        "blake2s",
+        "sha3_224",
+        "sha1",
+        "sha256",
+        "sha384",
+        "shake_256",
+        "blake2b",
+        "sha3_512",
+        "shake_128",
+        "sha3_384",
+    }:
+        hash_function = getattr(hashlib, hash_algorithm)
+        return [hash_single(token, hash_function) for token in tokens]
+    else:
+        raise ValueError("not a valid hash function: " + hash_algorithm)
 
 
 def normalize(text: str) -> str: