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import os
import re
from pathlib import Path
from typing import Any, List, TypeVar, Union
from numpy import ndarray
from sklearn.utils.validation import _check_feature_names_in # type: ignore
T = TypeVar("T", bound=Any)
ArrayLike = Union[ndarray, List[T]]
PathLike = Union[str, Path]
def _csv_filename_to_pkl_filename(csv_filename: PathLike) -> PathLike:
if os.path.splitext(csv_filename)[1] == ".pkl":
return csv_filename
# Assume that the csv filename is of the form "foo.csv"
assert str(csv_filename).endswith(".csv")
dirname = str(os.path.dirname(csv_filename))
basename = str(os.path.basename(csv_filename))
base = str(os.path.splitext(basename)[0])
pkl_basename = base + ".pkl"
return os.path.join(dirname, pkl_basename)
_regexp_im = re.compile(r"\b(\d+\.\d+)im\b")
_regexp_im_sci = re.compile(r"\b(\d+\.\d+)[eEfF]([+-]?\d+)im\b")
_regexp_sci = re.compile(r"\b(\d+\.\d+)[eEfF]([+-]?\d+)\b")
_apply_regexp_im = lambda x: _regexp_im.sub(r"\1j", x)
_apply_regexp_im_sci = lambda x: _regexp_im_sci.sub(r"\1e\2j", x)
_apply_regexp_sci = lambda x: _regexp_sci.sub(r"\1e\2", x)
def _preprocess_julia_floats(s: str) -> str:
if isinstance(s, str):
s = _apply_regexp_im(s)
s = _apply_regexp_im_sci(s)
s = _apply_regexp_sci(s)
return s
def _safe_check_feature_names_in(self, variable_names, generate_names=True):
"""_check_feature_names_in with compat for old versions."""
try:
return _check_feature_names_in(
self, variable_names, generate_names=generate_names
)
except TypeError:
return _check_feature_names_in(self, variable_names)
def _subscriptify(i: int) -> str:
"""Converts integer to subscript text form.
For example, 123 -> "₁₂₃".
"""
return "".join([chr(0x2080 + int(c)) for c in str(i)])
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