class_docstr: Detector that detects outlier point with principal component analysis.
  
  This detector performs principal component analysis (PCA) to the
  multivariate time series (every time point is treated as a point in high-
  dimensional space), measures reconstruction error at every time point, and
  identifies a time point as anomalous when the recontruction error is beyond
  anomalously large.
  
  This detector is internally implemented as a `Pipeline` object. Advanced
  users may learn more details by checking attribute `pipe_`.
  
  Parameters
  ----------
  k: int, optional
      Number of principal components to use. Default: 1.
  
  c: float, optional
      Factor used to determine the bound of normal range based on historical
      interquartile range. Default: 5.0.
  
  Attributes
  ----------
  pipe_: adtk.pipe.Pipenet
      Internal pipenet object.
class_docstr_tok: ["Detector","that","detects","outlier","point","with","principal","component","analysis",".","This","detector","performs","principal","component","analysis","(","PCA",")","to","the","multivariate","time","series","(","every","time","point","is","treated","as","a","point","in","high-","dimensional","space",")",",","measures","reconstruction","error","at","every","time","point",",","and","identifies","a","time","point","as","anomalous","when","the","recontruction","error","is","beyond","anomalously","large",".","This","detector","is","internally","implemented","as","a","`","Pipeline","`","object",".","Advanced","users","may","learn","more","details","by","checking","attribute","`","pipe_","`",".","Parameters","--","--","--","--","--","k",":","int",",","optional","Number","of","principal","components","to","use",".","Default",":","1.","c",":","float",",","optional","Factor","used","to","determine","the","bound","of","normal","range","based","on","historical","interquartile","range",".","Default",":","5.0",".","Attributes","--","--","--","--","--","pipe_",":","adtk.pipe.Pipenet","Internal","pipenet","object","."]
code: def _param_names(self) -> Tuple[str, ...]:
      return ("k", "c")
  
imports: from collections import Counter
  from typing import Any, Callable, Dict, Optional, Tuple
  import pandas
  from .._detector_base import _TrainableMultivariateDetector
  from ..aggregator import AndAggregator
  from ..detector import InterQuartileRangeAD, ThresholdAD
  from ..pipe import Pipeline, Pipenet
  from ..transformer import CustomizedTransformer1D, PcaReconstructionError, RegressionResidual
label_desc: Use image node_id 2 for calling the PcaAD obj's underlying member method code with example usage: obj._param_names() and returns: str,  str