Add profiler

Closes #16

README.md

@@ -112,13 +112,6 @@ 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

@@ -3,7 +3,6 @@
 """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

@@ -44,19 +44,6 @@ 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"
@@ -88,12 +75,7 @@ def main() -> None:
         else:
             text = sys.stdin.read()
 
-        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))
+        print(json.dumps(model.confidence(text, args.max_ngram_length)))
     elif args.subparser_name == "check":
         with open(args.model, "rb") as f:
             model = gptc.deserialize(f)

gptc/classifier.py

@@ -1,68 +0,0 @@
-# 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

@@ -2,7 +2,65 @@
 
 import gptc.tokenizer
 import gptc.model
-from typing import Iterable, Mapping, List, Dict, Union
+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
 
 
 def compile(
@@ -27,49 +85,8 @@ def compile(
         A compiled GPTC model.
 
     """
-
-    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
-
+    word_counts, category_lengths, names = _count_words(
+        raw_model, max_ngram_length, hash_algorithm
+    )
+    model = _get_weights(min_count, word_counts, category_lengths, names)
     return gptc.model.Model(model, names, max_ngram_length, hash_algorithm)

profiler.py

@@ -0,0 +1,16 @@
+# 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)")