163 lines
6.3 KiB
Python
163 lines
6.3 KiB
Python
######################## BEGIN LICENSE BLOCK ########################
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# The Original Code is Mozilla Universal charset detector code.
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#
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# The Initial Developer of the Original Code is
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# Netscape Communications Corporation.
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# Portions created by the Initial Developer are Copyright (C) 2001
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# the Initial Developer. All Rights Reserved.
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#
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# Contributor(s):
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# Mark Pilgrim - port to Python
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# Shy Shalom - original C code
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#
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# This library is free software; you can redistribute it and/or
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# modify it under the terms of the GNU Lesser General Public
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# License as published by the Free Software Foundation; either
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# version 2.1 of the License, or (at your option) any later version.
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#
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# This library is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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# Lesser General Public License for more details.
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#
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# You should have received a copy of the GNU Lesser General Public
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# License along with this library; if not, write to the Free Software
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# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
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# 02110-1301 USA
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######################### END LICENSE BLOCK #########################
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from typing import Dict, List, NamedTuple, Optional, Union
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from .charsetprober import CharSetProber
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from .enums import CharacterCategory, ProbingState, SequenceLikelihood
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class SingleByteCharSetModel(NamedTuple):
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charset_name: str
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language: str
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char_to_order_map: Dict[int, int]
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language_model: Dict[int, Dict[int, int]]
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typical_positive_ratio: float
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keep_ascii_letters: bool
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alphabet: str
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class SingleByteCharSetProber(CharSetProber):
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SAMPLE_SIZE = 64
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SB_ENOUGH_REL_THRESHOLD = 1024 # 0.25 * SAMPLE_SIZE^2
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POSITIVE_SHORTCUT_THRESHOLD = 0.95
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NEGATIVE_SHORTCUT_THRESHOLD = 0.05
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def __init__(
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self,
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model: SingleByteCharSetModel,
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is_reversed: bool = False,
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name_prober: Optional[CharSetProber] = None,
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) -> None:
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super().__init__()
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self._model = model
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# TRUE if we need to reverse every pair in the model lookup
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self._reversed = is_reversed
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# Optional auxiliary prober for name decision
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self._name_prober = name_prober
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self._last_order = 255
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self._seq_counters: List[int] = []
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self._total_seqs = 0
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self._total_char = 0
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self._control_char = 0
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self._freq_char = 0
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self.reset()
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def reset(self) -> None:
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super().reset()
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# char order of last character
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self._last_order = 255
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self._seq_counters = [0] * SequenceLikelihood.get_num_categories()
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self._total_seqs = 0
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self._total_char = 0
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self._control_char = 0
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# characters that fall in our sampling range
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self._freq_char = 0
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@property
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def charset_name(self) -> Optional[str]:
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if self._name_prober:
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return self._name_prober.charset_name
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return self._model.charset_name
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@property
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def language(self) -> Optional[str]:
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if self._name_prober:
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return self._name_prober.language
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return self._model.language
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def feed(self, byte_str: Union[bytes, bytearray]) -> ProbingState:
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# TODO: Make filter_international_words keep things in self.alphabet
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if not self._model.keep_ascii_letters:
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byte_str = self.filter_international_words(byte_str)
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else:
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byte_str = self.remove_xml_tags(byte_str)
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if not byte_str:
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return self.state
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char_to_order_map = self._model.char_to_order_map
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language_model = self._model.language_model
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for char in byte_str:
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order = char_to_order_map.get(char, CharacterCategory.UNDEFINED)
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# XXX: This was SYMBOL_CAT_ORDER before, with a value of 250, but
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# CharacterCategory.SYMBOL is actually 253, so we use CONTROL
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# to make it closer to the original intent. The only difference
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# is whether or not we count digits and control characters for
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# _total_char purposes.
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if order < CharacterCategory.CONTROL:
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self._total_char += 1
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if order < self.SAMPLE_SIZE:
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self._freq_char += 1
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if self._last_order < self.SAMPLE_SIZE:
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self._total_seqs += 1
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if not self._reversed:
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lm_cat = language_model[self._last_order][order]
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else:
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lm_cat = language_model[order][self._last_order]
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self._seq_counters[lm_cat] += 1
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self._last_order = order
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charset_name = self._model.charset_name
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if self.state == ProbingState.DETECTING:
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if self._total_seqs > self.SB_ENOUGH_REL_THRESHOLD:
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confidence = self.get_confidence()
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if confidence > self.POSITIVE_SHORTCUT_THRESHOLD:
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self.logger.debug(
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"%s confidence = %s, we have a winner", charset_name, confidence
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)
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self._state = ProbingState.FOUND_IT
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elif confidence < self.NEGATIVE_SHORTCUT_THRESHOLD:
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self.logger.debug(
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"%s confidence = %s, below negative shortcut threshold %s",
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charset_name,
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confidence,
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self.NEGATIVE_SHORTCUT_THRESHOLD,
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)
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self._state = ProbingState.NOT_ME
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return self.state
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def get_confidence(self) -> float:
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r = 0.01
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if self._total_seqs > 0:
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r = (
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(
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self._seq_counters[SequenceLikelihood.POSITIVE]
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+ 0.25 * self._seq_counters[SequenceLikelihood.LIKELY]
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)
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/ self._total_seqs
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/ self._model.typical_positive_ratio
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)
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# The more control characters (proportionnaly to the size
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# of the text), the less confident we become in the current
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# charset.
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r = r * (self._total_char - self._control_char) / self._total_char
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r = r * self._freq_char / self._total_char
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if r >= 1.0:
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r = 0.99
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return r
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