Make more of compilation numeric

README.md

@@ -112,6 +112,13 @@ GPTC.
 
 See `models/unpacked/` for an example of the format.
 
+### `gptc.Classifier(model, max_ngram_length=1)`
+
+`Classifier` objects are deprecated starting with GPTC 3.1.0, and will be
+removed in 4.0.0. See [the README from
+3.0.2](https://git.kj7rrv.com/kj7rrv/gptc/src/tag/v3.0.1/README.md) if you need
+documentation.
+
 ## Ngrams
 
 GPTC optionally supports using ngrams to improve classification accuracy. They

gptc/__init__.py

@@ -3,6 +3,7 @@
 """General-Purpose Text Classifier"""
 
 from gptc.compiler import compile as compile
+from gptc.classifier import Classifier as Classifier
 from gptc.pack import pack as pack
 from gptc.model import Model as Model, deserialize as deserialize
 from gptc.tokenizer import normalize as normalize

gptc/__main__.py

@@ -44,6 +44,19 @@ def main() -> None:
         type=int,
         default=1,
     )
+    group = classify_parser.add_mutually_exclusive_group()
+    group.add_argument(
+        "-j",
+        "--json",
+        help="output confidence dict as JSON (default)",
+        action="store_true",
+    )
+    group.add_argument(
+        "-c",
+        "--category",
+        help="output most likely category or `None`",
+        action="store_true",
+    )
 
     check_parser = subparsers.add_parser(
         "check", help="check one word or ngram in model"
@@ -75,7 +88,12 @@ def main() -> None:
         else:
             text = sys.stdin.read()
 
-        print(json.dumps(model.confidence(text, args.max_ngram_length)))
+        if args.category:
+            classifier = gptc.Classifier(model, args.max_ngram_length)
+            print(classifier.classify(text))
+        else:
+            probabilities = model.confidence(text, args.max_ngram_length)
+            print(json.dumps(probabilities))
     elif args.subparser_name == "check":
         with open(args.model, "rb") as f:
             model = gptc.deserialize(f)

gptc/classifier.py

@@ -0,0 +1,68 @@
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+import gptc.model
+from typing import Dict, Union
+
+
+class Classifier:
+    """A text classifier.
+
+    Parameters
+    ----------
+    model : dict
+        A compiled GPTC model.
+
+    max_ngram_length : int
+        The maximum ngram length to use when tokenizing input. If this is
+        greater than the value used when the model was compiled, it will be
+        silently lowered to that value.
+
+    Attributes
+    ----------
+    model : dict
+        The model used.
+
+    """
+
+    def __init__(self, model: gptc.model.Model, max_ngram_length: int = 1):
+        self.model = model
+        model_ngrams = model.max_ngram_length
+        self.max_ngram_length = min(max_ngram_length, model_ngrams)
+
+    def confidence(self, text: str) -> Dict[str, float]:
+        """Classify text with confidence.
+
+        Parameters
+        ----------
+        text : str
+            The text to classify
+
+        Returns
+        -------
+        dict
+            {category:probability, category:probability...} or {} if no words
+            matching any categories in the model were found
+
+        """
+        return self.model.confidence(text, self.max_ngram_length)
+
+    def classify(self, text: str) -> Union[str, None]:
+        """Classify text.
+
+        Parameters
+        ----------
+        text : str
+            The text to classify
+
+        Returns
+        -------
+        str or None
+            The most likely category, or None if no words matching any
+            category in the model were found.
+
+        """
+        probs: Dict[str, float] = self.confidence(text)
+        try:
+            return sorted(probs.items(), key=lambda x: x[1])[-1][0]
+        except IndexError:
+            return None

gptc/compiler.py

@@ -2,65 +2,7 @@
 
 import gptc.tokenizer
 import gptc.model
-from typing import Iterable, Mapping, List, Dict, Union, Tuple
-
-
-def _count_words(
-    raw_model: Iterable[Mapping[str, str]],
-    max_ngram_length: int,
-    hash_algorithm: str,
-) -> Tuple[Dict[int, Dict[str, int]], Dict[str, int], List[str]]:
-    word_counts: Dict[int, Dict[str, int]] = {}
-    category_lengths: Dict[str, int] = {}
-    names: List[str] = []
-
-    for portion in raw_model:
-        text = gptc.tokenizer.hash(
-            gptc.tokenizer.tokenize(portion["text"], max_ngram_length),
-            hash_algorithm,
-        )
-        category = portion["category"]
-
-        if not category in names:
-            names.append(category)
-
-        category_lengths[category] = category_lengths.get(category, 0) + len(
-            text
-        )
-
-        for word in text:
-            if word in word_counts:
-                try:
-                    word_counts[word][category] += 1
-                except KeyError:
-                    word_counts[word][category] = 1
-            else:
-                word_counts[word] = {category: 1}
-
-    return word_counts, category_lengths, names
-
-
-def _get_weights(
-    min_count: int,
-    word_counts: Dict[int, Dict[str, int]],
-    category_lengths: Dict[str, int],
-    names: List[str],
-) -> Dict[int, List[int]]:
-    model: Dict[int, List[int]] = {}
-    for word, counts in word_counts.items():
-        if sum(counts.values()) >= min_count:
-            weights = {
-                category: value / category_lengths[category]
-                for category, value in counts.items()
-            }
-            total = sum(weights.values())
-            new_weights: List[int] = []
-            for category in names:
-                new_weights.append(
-                    round((weights.get(category, 0) / total) * 65535)
-                )
-            model[word] = new_weights
-    return model
+from typing import Iterable, Mapping, List, Dict, Union
 
 
 def compile(
@@ -85,8 +27,49 @@ def compile(
         A compiled GPTC model.
 
     """
-    word_counts, category_lengths, names = _count_words(
-        raw_model, max_ngram_length, hash_algorithm
-    )
-    model = _get_weights(min_count, word_counts, category_lengths, names)
+
+    word_counts: Dict[int, Dict[int, int]] = {}
+    category_lengths: Dict[int, int] = {}
+    names: List[str] = []
+
+    for portion in raw_model:
+        text = gptc.tokenizer.hash(
+            gptc.tokenizer.tokenize(portion["text"], max_ngram_length),
+            hash_algorithm,
+        )
+        category_name = portion["category"]
+
+        if not category_name in names:
+            names.append(category_name)
+
+        category = names.index(category_name)
+
+        category_lengths[category] = category_lengths.get(category, 0) + len(
+            text
+        )
+
+        for word in text:
+            if word in word_counts:
+                try:
+                    word_counts[word][category] += 1
+                except KeyError:
+                    word_counts[word][category] = 1
+            else:
+                word_counts[word] = {category: 1}
+
+    model: Dict[int, List[int]] = {}
+    for word, counts in word_counts.items():
+        if sum(counts.values()) >= min_count:
+            weights = {
+                category: value / category_lengths[category]
+                for category, value in counts.items()
+            }
+            total = sum(weights.values())
+            new_weights: List[int] = []
+            for category in range(len(names)):
+                new_weights.append(
+                    round((weights.get(category, 0) / total) * 65535)
+                )
+            model[word] = new_weights
+
     return gptc.model.Model(model, names, max_ngram_length, hash_algorithm)

profiler.py

@@ -1,16 +0,0 @@
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-import cProfile
-import gptc
-import json
-import sys
-
-max_ngram_length = 10
-
-with open("models/raw.json") as f:
-    raw_model = json.load(f)
-
-with open("models/benchmark_text.txt") as f:
-    text = f.read()
-
-cProfile.run("gptc.compile(raw_model, max_ngram_length)")