20 changed files with 272 additions and 1546 deletions
--- a/GPL-3.0
+++ b/GPL-3.0
@ -1,674 +0,0 @@
                    GNU GENERAL PUBLIC LICENSE
                       Version 3, 29 June 2007
 Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
 Everyone is permitted to copy and distribute verbatim copies
 of this license document, but changing it is not allowed.
                            Preamble
  The GNU General Public License is a free, copyleft license for
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                       TERMS AND CONDITIONS
  0. Definitions.
  "This License" refers to version 3 of the GNU General Public License.
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  Notwithstanding any other provision of this License, you have
 permission to link or combine any covered work with a work licensed
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                     END OF TERMS AND CONDITIONS
            How to Apply These Terms to Your New Programs
  If you develop a new program, and you want it to be of the greatest
 possible use to the public, the best way to achieve this is to make it
 free software which everyone can redistribute and change under these terms.
  To do so, attach the following notices to the program.  It is safest
 to attach them to the start of each source file to most effectively
 state the exclusion of warranty; and each file should have at least
 the "copyright" line and a pointer to where the full notice is found.
    <one line to give the program's name and a brief idea of what it does.>
    Copyright (C) <year>  <name of author>
    This program is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.
    This program is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU General Public License for more details.
    You should have received a copy of the GNU General Public License
    along with this program.  If not, see <https://www.gnu.org/licenses/>.
 Also add information on how to contact you by electronic and paper mail.
  If the program does terminal interaction, make it output a short
 notice like this when it starts in an interactive mode:
    <program>  Copyright (C) <year>  <name of author>
    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
    This is free software, and you are welcome to redistribute it
    under certain conditions; type `show c' for details.
 The hypothetical commands `show w' and `show c' should show the appropriate
 parts of the General Public License.  Of course, your program's commands
 might be different; for a GUI interface, you would use an "about box".
  You should also get your employer (if you work as a programmer) or school,
 if any, to sign a "copyright disclaimer" for the program, if necessary.
 For more information on this, and how to apply and follow the GNU GPL, see
 <https://www.gnu.org/licenses/>.
  The GNU General Public License does not permit incorporating your program
 into proprietary programs.  If your program is a subroutine library, you
 may consider it more useful to permit linking proprietary applications with
 the library.  If this is what you want to do, use the GNU Lesser General
 Public License instead of this License.  But first, please read
 <https://www.gnu.org/licenses/why-not-lgpl.html>.
--- a/28
+++ b/28
@ -1,13 +1,21 @@
-Copyright (c) 2020-2022 Samuel L Sloniker
+MIT License
-This program is free software: you can redistribute it and/or modify it under
+Copyright (c) 2020-2021 Samuel L Sloniker KJ7RRV
 the terms of the GNU General Public License as published by the Free Software
 Foundation, either version 3 of the License, or (at your option) any later
 version.
-This program is distributed in the hope that it will be useful, but WITHOUT ANY
+Permission is hereby granted, free of charge, to any person obtaining a copy
-WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
+of this software and associated documentation files (the "Software"), to deal
-PARTICULAR PURPOSE. See the GNU General Public License for more details.
+in the Software without restriction, including without limitation the rights
 to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 copies of the Software, and to permit persons to whom the Software is
 furnished to do so, subject to the following conditions:
-You should have received a copy of the GNU General Public License along with
+The above copyright notice and this permission notice shall be included in all
-this program. If not, see <https://www.gnu.org/licenses/>. 
+copies or substantial portions of the Software.
 THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 SOFTWARE.
--- a/README.md
+++ b/README.md
@ -1,145 +1,44 @@
 # GPTC
 General-purpose text classifier in Python
 GPTC provides both a CLI tool and a Python library.
 ## Installation
    pip install gptc
 ## CLI Tool
 ### Classifying text
-    gptc classify [-n <max_ngram_length>] <compiled model file>
+    `python -m gptc <modelfile>`
-This will prompt for a string and classify it, then print (in JSON) a dict of
+This will prompt for a string and classify it, outputting the category on
-the format `{category: probability, category:probability, ...}` to stdout. (For
+stdout (or "None" if it cannot determine anything).
 information about `-n <max_ngram_length>`, see section "Ngrams.")
-### Checking individual words or ngrams
+Alternatively, if you need confidence data, use:
-    gptc check <compiled model file> <token or ngram>
+    `python -m gptc -j <modelfile>`
-This is very similar to `gptc classify`, except it takes the input as an
+This will print (in JSON) a dict of the format `{category: probability,
-argument, and it treats the input as a single token or ngram.
+category:probability, ...}` to stdout.
 ### Compiling models
-    gptc compile [-n <max_ngram_length>] [-c <min_count>] <raw model file> <compiled model file>
+    gptc <raw model file> -c|--compile <compiled model file>
 This will write the compiled model encoded in binary format to `<compiled model
 file>`.
 If `-c` is specified, words and ngrams used less than `min_count` times will be
 excluded from the compiled model.
 ### Packing models
    gptc pack <dir>
 This will print the raw model in JSON to stdout. See `models/unpacked/` for an
 example of the format. Any exceptions will be printed to stderr.
 ## Library
-
+### `gptc.Classifier(model)`
-### `Model.serialize(file)`
+Create a `Classifier` object using the given *compiled* model (as a dict, not
-
+JSON).
-Write binary data representing the model to `file`.
+#### `Classifier.confidence(text)`
 ### `Model.deserialize(encoded_model)`
 Deserialize a `Model` from a file containing data from `Model.serialize()`.
 ### `Model.confidence(text, max_ngram_length)`
 Classify `text`. Returns a dict of the format `{category: probability,
 category:probability, ...}`
-
+#### `Classifier.classify(text)`
-Note that this may not include values for all categories. If there are no
+Classify `text`. Returns the category into which the text is placed (as a
-common words between the input and the training data (likely, for example, with
+string), or `None` when it cannot classify the text.
-input in a different language from the training data), an empty dict will be
+## `gptc.compile(raw_model)`
 returned.
 For information about `max_ngram_length`, see section "Ngrams."
 ### `Model.get(token)`
 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.
 ### `Model.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).
+dict).
 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:
    (raw_model, [(exception,),(exception,)...])
 Note that the exceptions are contained in single-item tuples. This is to allow
 more information to be provided without breaking the API in future versions of
 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 5.0.0. 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
 are disabled by default (maximum length set to 1) for performance reasons.
 Enabling them significantly increases the time required both for compilation
 and classification. The effect seems more significant for compilation than for
 classification. Compiled models are also much larger when ngrams are enabled.
 Larger maximum ngram lengths will result in slower performance and larger
 files. It is a good idea to experiment with different values and use the
 highest one at which GPTC is fast enough and models are small enough for your
 needs.
 Once a model is compiled at a certain maximum ngram length, it cannot be used
 for classification with a higher value. If you instantiate a `Classifier` with
 a model compiled with a lower `max_ngram_length`, the value will be silently
 reduced to the one used when compiling the model.
 ## Model format
-
+This section explains the raw model format, which is how you should create and
-This section explains the raw model format, which is how models are created and
+edit models.
 edited.
 Raw models are formatted as a list of dicts. See below for the format:
@ -150,30 +49,13 @@ Raw models are formatted as a list of dicts. See below for the format:
        }
    ]
-GPTC handles raw models as `list`s of `dict`s of `str`s (`List[Dict[str,
+GPTC handles models as Python `list`s of `dict`s of `str`s (for raw models) or
-str]]`), and they can be stored in any way these Python objects can be.
+`dict`s of `str`s and `float`s (for compiled models), and they can be stored
-However, it is recommended to store them in JSON format for compatibility with
+in any way these Python objects can be. However, it is recommended to store
-the command-line tool.
+them in JSON format for compatibility with the command-line tool.
 ## Emoji
 GPTC treats individual emoji as words.
 ## Example model
 An example model, which is designed to distinguish between texts written by
 Mark Twain and those written by William Shakespeare, is available in `models`.
 The raw model is in `models/raw.json`; the compiled model is in
 `models/compiled.json`.
 The example model was compiled with `max_ngram_length=10`.
 ## Benchmark
 A benchmark script is available for comparing performance of GPTC between
 different Python versions. To use it, run `benchmark.py` with all of the Python
 installations you want to test. It tests both compilation and classification.
 It uses the default Twain/Shakespeare model for both, and for classification it
 uses [Mark Antony's "Friends, Romans, countrymen"
 speech](https://en.wikipedia.org/wiki/Friends,_Romans,_countrymen,_lend_me_your_ears)
 from Shakespeare's *Julius Caesar*.
--- a/benchmark.py
+++ b/benchmark.py
@ -1,56 +0,0 @@
 # SPDX-License-Identifier: GPL-3.0-or-later
 import timeit
 import gptc
 import json
 import sys
 max_ngram_length = 10
 compile_iterations = 100
 classify_iterations = 10000
 with open("models/raw.json") as f:
    raw_model = json.load(f)
 with open("models/benchmark_text.txt") as f:
    text = f.read()
 print("Benchmarking GPTC on Python", sys.version)
 print("Maximum ngram length:", max_ngram_length)
 print(
    "Average compilation time over",
    compile_iterations,
    "iterations:",
    round(
        1000000
        * timeit.timeit(
            "gptc.Model.compile(raw_model, max_ngram_length)",
            number=compile_iterations,
            globals=globals(),
        )
        / compile_iterations
    ),
    "microseconds",
 )
 classifier = gptc.Classifier(
    gptc.compile(raw_model, max_ngram_length), max_ngram_length
 )
 print(
    "Average classification time over",
    classify_iterations,
    "iterations:",
    round(
        1000000
        * timeit.timeit(
            "classifier.classify(text)",
            number=classify_iterations,
            globals=globals(),
        )
        / classify_iterations
    ),
    "microseconds",
 )
 print("--- benchmark complete ---")
--- a/gptc/init.py
+++ b/gptc/init.py
@ -1,12 +1,5 @@
 # SPDX-License-Identifier: GPL-3.0-or-later
 """General-Purpose Text Classifier"""
-from gptc.pack import pack
+from gptc.compiler import compile
-from gptc.model import Model
+from gptc.classifier import Classifier
-from gptc.tokenizer import normalize
+from gptc.exceptions import *
 from gptc.exceptions import (
    GPTCError,
    ModelError,
    InvalidModelError,
 )
--- a/gptc/main.py
+++ b/gptc/main.py
@ -1,88 +1,27 @@
 #!/usr/bin/env python3
 # SPDX-License-Identifier: GPL-3.0-or-later
 import argparse
 import json
 import sys
 import gptc
-
+parser = argparse.ArgumentParser(description="General Purpose Text Classifier")
-def main() -> None:
+parser.add_argument('model', help='model to use')
-    parser = argparse.ArgumentParser(
+parser.add_argument('-c', '--compile', help='compile raw model model to outfile', metavar='outfile')
-        description="General Purpose Text Classifier", prog="gptc"
+parser.add_argument('-j', '--confidence', help='output confidence dict in json', action='store_true')
    )
    subparsers = parser.add_subparsers(dest="subparser_name", required=True)
    compile_parser = subparsers.add_parser(
        "compile", help="compile a raw model"
    )
    compile_parser.add_argument("model", help="raw model to compile")
    compile_parser.add_argument(
        "out", help="name of file to write compiled model to"
    )
    compile_parser.add_argument(
        "--max-ngram-length",
        "-n",
        help="maximum ngram length",
        type=int,
        default=1,
    )
    compile_parser.add_argument(
        "--min-count",
        "-c",
        help="minimum use count for word/ngram to be included in model",
        type=int,
        default=1,
    )
    classify_parser = subparsers.add_parser("classify", help="classify text")
    classify_parser.add_argument("model", help="compiled model to use")
    classify_parser.add_argument(
        "--max-ngram-length",
        "-n",
        help="maximum ngram length",
        type=int,
        default=1,
    )
    check_parser = subparsers.add_parser(
        "check", help="check one word or ngram in model"
    )
    check_parser.add_argument("model", help="compiled model to use")
    check_parser.add_argument("token", help="token or ngram to check")
    pack_parser = subparsers.add_parser(
        "pack", help="pack a model from a directory"
    )
    pack_parser.add_argument("model", help="directory containing model")
 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:
+    raw_model = json.load(f)
-            model = json.load(input_file)
+if args.compile:
-
+    with open(args.compile, 'w+') as f:
-        with open(args.out, "wb+") as output_file:
+        json.dump(gptc.compile(raw_model), f)
-            gptc.Model.compile(
+else:
-                model, args.max_ngram_length, args.min_count
+    classifier = gptc.Classifier(raw_model)
            ).serialize(output_file)
    elif args.subparser_name == "classify":
        with open(args.model, "rb") as model_file:
            model = gptc.Model.deserialize(model_file)
    if sys.stdin.isatty():
-            text = input("Text to analyse: ")
+        text = input('Text to analyse: ')
    else:
        text = sys.stdin.read()
-
+    if args.confidence:
-        print(json.dumps(model.confidence(text, args.max_ngram_length)))
+        print(json.dumps(classifier.confidence(text)))
    elif args.subparser_name == "check":
        with open(args.model, "rb") as model_file:
            model = gptc.Model.deserialize(model_file)
        print(json.dumps(model.get(args.token)))
    else:
-        print(json.dumps(gptc.pack(args.model, True)[0]))
+        print(classifier.classify(text))
 if __name__ == "__main__":
    main()
--- a/gptc/classifier.py
+++ b/gptc/classifier.py
@ -0,0 +1,90 @@
 import gptc.tokenizer, gptc.compiler, gptc.exceptions
 import warnings
 class Classifier:
    """A text classifier.
    Parameters
    ----------
    model : dict
        A compiled GPTC model.
    Attributes
    ----------
    model : dict
        The model used.
    """
    def __init__(self, model):
        try:
            model_version = model['__version__']
        except:
            model_version = 1
        if model_version == 3:
            self.model = model
        else:
            # The model is an unsupported version
            try:
                raw_model = model['__raw__']
            except:
                raise gptc.exceptions.UnsupportedModelError('this model is unsupported and does not contain a raw model for recompiling')
            warnings.warn("model needed to be recompiled on-the-fly; please re-compile it and use the new compiled model in the future")
            self.model = gptc.compiler.compile(raw_model)
    def confidence(self, text):
        """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
        """
        model = self.model
        text = gptc.tokenizer.tokenize(text)
        probs = {}
        for word in text:
            try:
                for category, value in enumerate(model[word]):
                    try:
                        probs[category] += value
                    except KeyError:
                        probs[category] = value
            except KeyError:
                pass
        probs = {model['__names__'][category]: value/65535 for category, value in probs.items()}
        total = sum(probs.values())
        probs = {category: value/total for category, value in probs.items()}
        return probs
    def classify(self, text):
        """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 = self.confidence(text)
        try:
            return sorted(probs.items(), key=lambda x: x[1])[-1][0]
        except IndexError:
            return None
--- a/gptc/compiler.py
+++ b/gptc/compiler.py
@ -0,0 +1,62 @@
 import gptc.tokenizer
 def compile(raw_model):
    """Compile a raw model.
    Parameters
    ----------
    raw_model : list of dict
        A raw GPTC model.
    Returns
    -------
    dict
        A compiled GPTC model.
    """
    categories = {}
    for portion in raw_model:
        text = gptc.tokenizer.tokenize(portion['text'])
        category = portion['category']
        try:
            categories[category] += text
        except KeyError:
            categories[category] = text
    categories_by_count = {}
    names = []
    for category, text in categories.items():
        if not category in names:
            names.append(category)
        categories_by_count[category] = {}
        for word in text:
            try:
                categories_by_count[category][word] += 1/len(categories[category])
            except KeyError:
                categories_by_count[category][word] = 1/len(categories[category])
    word_weights = {}
    for category, words in categories_by_count.items():
        for word, value in words.items():
            try:
                word_weights[word][category] = value
            except KeyError:
                word_weights[word] = {category:value}
    model = {}
    for word, weights in word_weights.items():
        total = sum(weights.values())
        model[word] = []
        for category in names:
            model[word].append(round((weights.get(category, 0)/total)*65535))
    model['__names__'] = names
    model['__version__'] = 3
    model['__raw__'] = raw_model
    return model
--- a/gptc/exceptions.py
+++ b/gptc/exceptions.py
@ -1,13 +1,8 @@
 # SPDX-License-Identifier: GPL-3.0-or-later
 class GPTCError(BaseException):
    pass
 class ModelError(GPTCError):
    pass
-
+class UnsupportedModelError(ModelError):
 class InvalidModelError(ModelError):
    pass
--- a/gptc/model.py
+++ b/gptc/model.py
@ -1,322 +0,0 @@
 # SPDX-License-Identifier: GPL-3.0-or-later
 from typing import (
        Iterable,
        Mapping,
    List,
    Dict,
    cast,
    BinaryIO,
    Tuple,
    TypedDict,
 )
 import json
 import gptc.tokenizer
 from gptc.exceptions import InvalidModelError
 import gptc.weighting
 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_list(
            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
 class ExplanationEntry(TypedDict):
    weight: float
    probabilities: Dict[str, float]
    count: int
 Explanation = Dict[
    str,
    ExplanationEntry,
 ]
 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:
        if word2 in explanation:
            explanation[word2]["count"] += 1
        else:
            explanation[word2] = {
                "weight": weight,
                "probabilities": {
                    name: word_probs[index] for index, name in enumerate(names)
                },
                "count": 1,
            }
    return explanation
 class Model:
    def __init__(
        self,
        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, transparent: bool = False
    ) -> Confidences:
        """Classify text with confidence.
        Parameters
        ----------
        text : str
            The text to classify
        max_ngram_length : int
            The maximum ngram length to use in classifying
        Returns
        -------
        dict
            {category:probability, category:probability...} or {} if no words
            matching any categories in the model were found
        """
        model = self.weights
        max_ngram_length = min(self.max_ngram_length, max_ngram_length)
        raw_tokens = gptc.tokenizer.tokenize(
            text, min(max_ngram_length, self.max_ngram_length)
        )
        tokens = gptc.tokenizer.hash_list(
            raw_tokens,
            self.hash_algorithm,
        )
        if transparent:
            token_map = {tokens[i]: raw_tokens[i] for i in range(len(tokens))}
            log: Log = []
        numbered_probs: Dict[int, float] = {}
        for word in tokens:
            try:
                unweighted_numbers = [
                    i / 65535 for i in cast(List[float], model[word])
                ]
                weight, weighted_numbers = gptc.weighting.weight(
                    unweighted_numbers
                )
                if transparent:
                    log.append(
                        (
                            token_map[word],
                            weight,
                            unweighted_numbers,
                        )
                    )
                for category, value in enumerate(weighted_numbers):
                    try:
                        numbered_probs[category] += value
                    except KeyError:
                        numbered_probs[category] = value
            except KeyError:
                pass
        total = sum(numbered_probs.values())
        probs: Dict[str, float] = {
            self.names[category]: value / total
            for category, value in numbered_probs.items()
        }
        if transparent:
            explanation = convert_log(log, self.names)
            return TransparentConfidences(probs, explanation)
        return Confidences(probs)
    def get(self, token: str) -> Dict[str, float]:
        try:
            weights = self.weights[
                gptc.tokenizer.hash_single(
                    gptc.tokenizer.normalize(token), self.hash_algorithm
                )
            ]
        except KeyError:
            return {}
        return {
            category: weights[index] / 65535
            for index, category in enumerate(self.names)
        }
    def serialize(self, file: BinaryIO) -> None:
        file.write(b"GPTC model v6\n")
        file.write(
            json.dumps(
                {
                    "names": self.names,
                    "max_ngram_length": self.max_ngram_length,
                    "hash_algorithm": self.hash_algorithm,
                }
            ).encode("utf-8")
            + b"\n"
        )
        for word, weights in self.weights.items():
            file.write(
                word.to_bytes(6, "big")
                + b"".join([weight.to_bytes(2, "big") for weight in weights])
            )
    @staticmethod
    def compile(
        raw_model: Iterable[Mapping[str, str]],
        max_ngram_length: int = 1,
        min_count: int = 1,
        hash_algorithm: str = "sha256",
        ) -> 'Model':
        """Compile a raw model.
        Parameters
        ----------
        raw_model : list of dict
            A raw GPTC model.
        max_ngram_length : int
            Maximum ngram lenght to compile with.
        Returns
        -------
        dict
            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)
        return Model(model, names, max_ngram_length, hash_algorithm)
    @staticmethod
    def deserialize(encoded_model: BinaryIO) -> "Model":
        prefix = encoded_model.read(14)
        if prefix != b"GPTC model v6\n":
            raise InvalidModelError()
        config_json = b""
        while True:
            byte = encoded_model.read(1)
            if byte == b"\n":
                break
            if byte == b"":
                raise InvalidModelError()
            config_json += byte
        try:
            config = json.loads(config_json.decode("utf-8"))
        except (UnicodeDecodeError, json.JSONDecodeError) as exc:
            raise InvalidModelError() from exc
        try:
            names = config["names"]
            max_ngram_length = config["max_ngram_length"]
            hash_algorithm = config["hash_algorithm"]
        except KeyError as exc:
            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):
            raise InvalidModelError()
        weight_code_length = 6 + 2 * len(names)
        weights: Dict[int, List[int]] = {}
        while True:
            code = encoded_model.read(weight_code_length)
            if not code:
                break
            if len(code) != weight_code_length:
                raise InvalidModelError()
            weights[int.from_bytes(code[:6], "big")] = [
                int.from_bytes(value, "big")
                for value in [code[x : x + 2] for x in range(6, len(code), 2)]
            ]
        return Model(weights, names, max_ngram_length, hash_algorithm)
--- a/gptc/pack.py
+++ b/gptc/pack.py
@ -1,38 +0,0 @@
 # SPDX-License-Identifier: GPL-3.0-or-later
 import sys
 import os
 from typing import List, Dict, Tuple
 def pack(
    directory: str, print_exceptions: bool = False
 ) -> Tuple[List[Dict[str, str]], List[Tuple[OSError]]]:
    paths = os.listdir(directory)
    texts: Dict[str, List[str]] = {}
    exceptions = []
    for path in paths:
        texts[path] = []
        try:
            for file in os.listdir(os.path.join(directory, path)):
                try:
                    with open(
                        os.path.join(directory, path, file), encoding="utf-8"
                    ) as input_file:
                        texts[path].append(input_file.read())
                except OSError as error:
                    exceptions.append((error,))
                    if print_exceptions:
                        print(error, file=sys.stderr)
        except OSError as error:
            exceptions.append((error,))
            if print_exceptions:
                print(error, file=sys.stderr)
    raw_model = []
    for category, cat_texts in texts.items():
        raw_model += [{"category": category, "text": i} for i in cat_texts]
    return raw_model, exceptions
--- a/gptc/tokenizer.py
+++ b/gptc/tokenizer.py
@ -1,86 +1,11 @@
-# SPDX-License-Identifier: GPL-3.0-or-later
+def tokenize(text):
    """Convert a string to a list of lemmas."""
    out = [""]
-import unicodedata
+    for char in text.lower():
-from typing import List, cast
+        if char.isalpha() or char == "'":
-import hashlib
+            out[-1] += char
-import emoji
+        elif out[-1] != "":
            out.append("")
-
+    return [string for string in out if string]
 def tokenize(text: str, max_ngram_length: int = 1) -> List[str]:
    text = unicodedata.normalize("NFKD", text).casefold()
    parts = []
    highest_end = 0
    for emoji_part in emoji.emoji_list(text):
        parts += list(text[highest_end : emoji_part["match_start"]])
        parts.append(emoji_part["emoji"])
        highest_end = emoji_part["match_end"]
    parts += list(text[highest_end:])
    converted_text = [part for part in parts if part]
    tokens = [""]
    for char in converted_text:
        if (
            char.isalpha()
            or char.isnumeric()
            or char == "'"
            or (char in ",." and (" " + tokens[-1])[-1].isnumeric())
        ):
            tokens[-1] += char
        elif emoji.is_emoji(char):
            tokens.append(char)
            tokens.append("")
        elif tokens[-1] != "":
            tokens.append("")
    tokens = [string for string in tokens if string]
    if max_ngram_length == 1:
        return tokens
    ngrams = []
    for ngram_length in range(1, max_ngram_length + 1):
        for index in range(len(tokens) + 1 - ngram_length):
            ngrams.append(" ".join(tokens[index : index + ngram_length]))
    return ngrams
 def _hash_single(token: str, hash_function: type) -> int:
    return int.from_bytes(
        hash_function(token.encode("utf-8")).digest()[:6], "big"
    )
 def _get_hash_function(hash_algorithm: str) -> type:
    if hash_algorithm in {
        "sha224",
        "md5",
        "sha512",
        "sha3_256",
        "blake2s",
        "sha3_224",
        "sha1",
        "sha256",
        "sha384",
        "shake_256",
        "blake2b",
        "sha3_512",
        "shake_128",
        "sha3_384",
    }:
        return cast(type, getattr(hashlib, hash_algorithm))
    raise ValueError("not a valid hash function: " + hash_algorithm)
 def hash_single(token: str, hash_algorithm: str) -> int:
    return _hash_single(token, _get_hash_function(hash_algorithm))
 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]
 def normalize(text: str) -> str:
    return " ".join(tokenize(text, 1))
--- a/gptc/weighting.py
+++ b/gptc/weighting.py
@ -1,46 +0,0 @@
 # SPDX-License-Identifier: GPL-3.0-or-later
 import math
 from typing import Sequence, Tuple, List
 def _mean(numbers: Sequence[float]) -> float:
    """Calculate the mean of a group of numbers
    Parameters
    ----------
    numbers : list of int or float
        The numbers to calculate the mean of
    Returns
    -------
    float
        The mean of the numbers
    """
    return sum(numbers) / len(numbers)
 def _standard_deviation(numbers: Sequence[float]) -> float:
    """Calculate the standard deviation of a group of numbers
    Parameters
    ----------
    numbers : list of int or float
        The numbers to calculate the mean of
    Returns
    -------
    float
        The standard deviation of the numbers
    """
    mean = _mean(numbers)
    squared_deviations = [(mean - i) ** 2 for i in numbers]
    return math.sqrt(_mean(squared_deviations))
 def weight(numbers: Sequence[float]) -> Tuple[float, List[float]]:
    standard_deviation = _standard_deviation(numbers)
    weight_assigned = standard_deviation * 2
    weighted_numbers = [i * weight_assigned for i in numbers]
    return weight_assigned, weighted_numbers
--- a/models/benchmark_text.txt
+++ b/models/benchmark_text.txt
@ -1,35 +0,0 @@
 Friends, Romans, countrymen, lend me your ears;
 I come to bury Caesar, not to praise him.
 The evil that men do lives after them;
 The good is oft interred with their bones;
 So let it be with Caesar. The noble Brutus
 Hath told you Caesar was ambitious:
 If it were so, it was a grievous fault,
 And grievously hath Caesar answer'd it.
 Here, under leave of Brutus and the rest-
 For Brutus is an honourable man;
 So are they all, all honourable men-
 Come I to speak in Caesar's funeral.
 He was my friend, faithful and just to me:
 But Brutus says he was ambitious;
 And Brutus is an honourable man.
 He hath brought many captives home to Rome
 Whose ransoms did the general coffers fill:
 Did this in Caesar seem ambitious?
 When that the poor have cried, Caesar hath wept:
 Ambition should be made of sterner stuff:
 Yet Brutus says he was ambitious;
 And Brutus is an honourable man.
 You all did see that on the Lupercal
 I thrice presented him a kingly crown,
 Which he did thrice refuse: was this ambition?
 Yet Brutus says he was ambitious;
 And, sure, he is an honourable man.
 I speak not to disprove what Brutus spoke,
 But here I am to speak what I do know.
 You all did love him once, not without cause:
 What cause withholds you then, to mourn for him?
 O judgment! thou art fled to brutish beasts,
 And men have lost their reason. Bear with me;
 My heart is in the coffin there with Caesar,
 And I must pause till it come back to me.
--- a/models/compiled.gptc
+++ b/models/compiled.gptc
--- a/models/compiled.json
+++ b/models/compiled.json
--- a/profiler.py
+++ b/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.Model.compile(raw_model, max_ngram_length)")
--- a/pyproject.toml
+++ b/pyproject.toml
@ -1,28 +0,0 @@
 [build-system]
 requires = ["setuptools>=61.0.0", "wheel"]
 build-backend = "setuptools.build_meta"
 [project]
 name = "gptc"
 version = "4.0.1"
 description = "General-purpose text classifier"
 readme = "README.md"
 authors = [{ name = "Samuel Sloniker", email = "sam@kj7rrv.com"}]
 classifiers = [
    "Programming Language :: Python",
    "Programming Language :: Python :: 3",
    "Development Status :: 5 - Production/Stable",
    "License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)",
    "Operating System :: OS Independent",
 ]
 dependencies = ["emoji"]
 requires-python = ">=3.7"
 [project.urls]
 Homepage = "https://git.kj7rrv.com/kj7rrv/gptc"
 [project.scripts]
 gptc = "gptc.__main__:main"
 [tool.setuptools]
 packages = ["gptc"]
--- a/setup.py
+++ b/setup.py
@ -0,0 +1,17 @@
 import setuptools
 with open("README.md", "r") as fh:
    long_description = fh.read()
 setuptools.setup(
    name="gptc",
    version="1.0.0",
    description="General-purpose text classifier",
    long_description=long_description,
    long_description_content_type="text/markdown",
    packages=setuptools.find_packages(),
    classifiers=[
        "Programming Language :: Python :: 3",
        "Operating System :: OS Independent",
    ],
    python_requires=">=3.6",
 )
--- a/utils/pack.py
+++ b/utils/pack.py
@ -0,0 +1,29 @@
 import sys
 import os
 import json
 if len(sys.argv) != 2:
    print('usage: pack.py <path>', file=sys.stderr)
    exit(1)
 paths = os.listdir(sys.argv[1])
 texts = {}
 for path in paths:
    texts[path] = []
    try:
        for file in os.listdir(os.path.join(sys.argv[1], path)):
            try:
                with open(os.path.join(sys.argv[1], path, file)) as f:
                    texts[path].append(f.read())
            except Exception as e:
                print(e, file=sys.stderr)
    except Exception as e:
        print(e, file=sys.stderr)
 raw_model = []
 for category, cat_texts in texts.items():
    raw_model += [{'category': category, 'text': i} for i in cat_texts]
 print(json.dumps(raw_model))