Remove Classifier objects

Closes #16
This commit is contained in:
Samuel Sloniker 2023-04-16 14:27:07 -07:00
parent 75fdb5ba3c
commit 56550ca457
Signed by: kj7rrv
GPG Key ID: 1BB4029E66285A62
4 changed files with 1 additions and 95 deletions

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@ -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

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@ -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

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@ -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)

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@ -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