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# -*- coding: utf-8 -*-
"""
Detect the dialect using the data consistency measure.
Author: Gertjan van den Burg
"""
from dataclasses import dataclass
from functools import lru_cache
from typing import Dict
from typing import Iterable
from typing import List
from typing import Optional
from . import field_size_limit
from .break_ties import tie_breaker
from .cparser_util import parse_string
from .detect_pattern import pattern_score
from .detect_type import DEFAULT_EPS_TYPE
from .detect_type import TypeDetector
from .dialect import SimpleDialect
from .potential_dialects import get_dialects
@dataclass
class ConsistencyScore:
"""Container to track the consistency score calculation
Parameters
----------
P : float
The pattern score
T : Optional[float]
The type score. Can be None if not computed for speed.
Q : Optional[float]
The consistency score. Can be None if not computed for speed.
"""
P: float
T: Optional[float]
Q: Optional[float]
class ConsistencyDetector:
"""Detect the dialect with the data consistency measure
This class uses the data consistency measure to detect the dialect. See the
paper for details.
Parameters
----------
skip : bool
Skip computation of the type score for dialects with a low pattern
score.
verbose : bool
Print out the dialects considered and their scores.
cache_capacity: int
The size of the cache for type detection. Caching the type detection
result greatly speeds up the computation of the consistency measure.
The size of the cache can be changed to trade off memory use and speed.
"""
def __init__(
self,
skip: bool = True,
verbose: bool = False,
cache_capacity: int = 100_000,
) -> None:
self._skip = skip
self._verbose = verbose
self._type_detector = TypeDetector()
self._cache_capacity = cache_capacity
# NOTE: A bit ugly but allows setting the cache size dynamically
@lru_cache(cache_capacity)
def cached_is_known_type(cell: str, is_quoted: bool) -> bool:
return self._type_detector.is_known_type(cell, is_quoted)
self._cached_is_known_type = cached_is_known_type
def detect(
self, data: str, delimiters: Optional[List[str]] = None
) -> Optional[SimpleDialect]:
"""Detect the dialect using the consistency measure
Parameters
----------
data : str
The data of the file as a string
delimiters : iterable
List of delimiters to consider. If None, the :func:`get_delimiters`
function is used to automatically detect this (as described in the
paper).
Returns
-------
dialect : SimpleDialect
The detected dialect. If no dialect could be detected, returns None.
"""
self._cached_is_known_type.cache_clear()
# TODO: probably some optimization there too
dialects = get_dialects(data, delimiters=delimiters)
# TODO: This is not thread-safe and this object can simply own a Parser
# for each dialect and set the limit directly there (we can also cache
# the best parsing result)
old_limit = field_size_limit(len(data) + 1)
scores = self.compute_consistency_scores(data, dialects)
best_dialects = ConsistencyDetector.get_best_dialects(scores)
result: Optional[SimpleDialect] = None
if len(best_dialects) == 1:
result = best_dialects[0]
else:
result = tie_breaker(data, best_dialects)
field_size_limit(old_limit)
return result
def compute_consistency_scores(
self, data: str, dialects: List[SimpleDialect]
) -> Dict[SimpleDialect, ConsistencyScore]:
"""Compute the consistency score for each dialect
This function computes the consistency score for each dialect. This is
done by first computing the pattern score for a dialect. If the class
is instantiated with ``skip`` set to False, it also computes the type
score for each dialect. If ``skip`` is True (the default), the type
score is only computed if the pattern score is larger or equal to the
current best combined score.
Parameters
----------
data : str
The data of the file as a string
dialects : Iterable[SimpleDialect]
An iterable of delimiters to consider.
Returns
-------
scores : Dict[SimpleDialect, ConsistencyScore]
A map with a :class:`ConsistencyScore` object for each dialect
provided as input.
"""
scores: Dict[SimpleDialect, ConsistencyScore] = {}
incumbent_score = -float("inf")
for dialect in sorted(dialects):
P = pattern_score(data, dialect)
if P < incumbent_score and self._skip:
scores[dialect] = ConsistencyScore(P, None, None)
if self._verbose:
print("%15r:\tP = %15.6f\tskip." % (dialect, P))
continue
T = self.compute_type_score(data, dialect)
Q = P * T
incumbent_score = max(incumbent_score, Q)
scores[dialect] = ConsistencyScore(P, T, Q)
if self._verbose:
print(
"%15r:\tP = %15.6f\tT = %15.6f\tQ = %15.6f"
% (dialect, P, T, Q)
)
return scores
@staticmethod
def get_best_dialects(
scores: Dict[SimpleDialect, ConsistencyScore]
) -> List[SimpleDialect]:
"""Identify the dialects with the highest consistency score"""
Qscores = [score.Q for score in scores.values()]
Qmax = -float("inf")
for q in Qscores:
if q is None:
continue
Qmax = max(Qmax, q)
return [d for d, score in scores.items() if score.Q == Qmax]
def compute_type_score(
self, data: str, dialect: SimpleDialect, eps: float = DEFAULT_EPS_TYPE
) -> float:
"""Compute the type score"""
total = known = 0
for row in parse_string(data, dialect, return_quoted=True):
assert all(isinstance(cell, tuple) for cell in row)
for cell, is_quoted in row:
total += 1
known += self._cached_is_known_type(cell, is_quoted=is_quoted)
if not total:
return eps
return max(eps, known / total)
def detect_dialect_consistency(
data: str,
delimiters: Optional[Iterable[str]] = None,
skip: bool = True,
verbose: bool = False,
) -> Optional[SimpleDialect]:
"""Helper function that wraps ConsistencyDetector"""
# Mostly kept for backwards compatibility
consistency_detector = ConsistencyDetector(skip=skip, verbose=verbose)
if delimiters is not None:
delimiters = list(delimiters)
return consistency_detector.detect(data, delimiters=delimiters)
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