class_docstr: Principal Components Analysis
class_docstr_tok: ["Principal","Components","Analysis"]
docstr: :param model_id: Destination id for this model; auto-generated if not specified.
         Defaults to ``None``.
  :type model_id: Union[None, str, H2OEstimator], optional
  :param training_frame: Id of the training data frame.
         Defaults to ``None``.
  :type training_frame: Union[None, str, H2OFrame], optional
  :param validation_frame: Id of the validation data frame.
         Defaults to ``None``.
  :type validation_frame: Union[None, str, H2OFrame], optional
  :param ignored_columns: Names of columns to ignore for training.
         Defaults to ``None``.
  :type ignored_columns: List[str], optional
  :param ignore_const_cols: Ignore constant columns.
         Defaults to ``True``.
  :type ignore_const_cols: bool
  :param score_each_iteration: Whether to score during each iteration of model training.
         Defaults to ``False``.
  :type score_each_iteration: bool
  :param transform: Transformation of training data
         Defaults to ``"none"``.
  :type transform: Literal["none", "standardize", "normalize", "demean", "descale"]
  :param pca_method: Specify the algorithm to use for computing the principal components: GramSVD - uses a
         distributed computation of the Gram matrix, followed by a local SVD; Power - computes the SVD using the
         power iteration method (experimental); Randomized - uses randomized subspace iteration method; GLRM -
         fits a generalized low-rank model with L2 loss function and no regularization and solves for the SVD
         using local matrix algebra (experimental)
         Defaults to ``"gram_s_v_d"``.
  :type pca_method: Literal["gram_s_v_d", "power", "randomized", "glrm"]
  :param pca_impl: Specify the implementation to use for computing PCA (via SVD or EVD): MTJ_EVD_DENSEMATRIX -
         eigenvalue decompositions for dense matrix using MTJ; MTJ_EVD_SYMMMATRIX - eigenvalue decompositions for
         symmetric matrix using MTJ; MTJ_SVD_DENSEMATRIX - singular-value decompositions for dense matrix using
         MTJ; JAMA - eigenvalue decompositions for dense matrix using JAMA. References: JAMA -
         http://math.nist.gov/javanumerics/jama/; MTJ - https://github.com/fommil/matrix-toolkits-java/
         Defaults to ``None``.
  :type pca_impl: Literal["mtj_evd_densematrix", "mtj_evd_symmmatrix", "mtj_svd_densematrix", "jama"], optional
  :param k: Rank of matrix approximation
         Defaults to ``1``.
  :type k: int
  :param max_iterations: Maximum training iterations
         Defaults to ``1000``.
  :type max_iterations: int
  :param use_all_factor_levels: Whether first factor level is included in each categorical expansion
         Defaults to ``False``.
  :type use_all_factor_levels: bool
  :param compute_metrics: Whether to compute metrics on the training data
         Defaults to ``True``.
  :type compute_metrics: bool
  :param impute_missing: Whether to impute missing entries with the column mean
         Defaults to ``False``.
  :type impute_missing: bool
  :param seed: RNG seed for initialization
         Defaults to ``-1``.
  :type seed: int
  :param max_runtime_secs: Maximum allowed runtime in seconds for model training. Use 0 to disable.
         Defaults to ``0.0``.
  :type max_runtime_secs: float
  :param export_checkpoints_dir: Automatically export generated models to this directory.
         Defaults to ``None``.
  :type export_checkpoints_dir: str, optional
docstr_tok: [":","param","model_id",":","Destination","id","for","this","model",";","auto-generated","if","not","specified",".","Defaults","to","``","None","``",".",":","type","model_id",":","Union","[","None",",","str",",","H2OEstimator","]",",","optional",":","param","training_frame",":","Id","of","the","training","data","frame",".","Defaults","to","``","None","``",".",":","type","training_frame",":","Union","[","None",",","str",",","H2OFrame","]",",","optional",":","param","validation_frame",":","Id","of","the","validation","data","frame",".","Defaults","to","``","None","``",".",":","type","validation_frame",":","Union","[","None",",","str",",","H2OFrame","]",",","optional",":","param","ignored_columns",":","Names","of","columns","to","ignore","for","training",".","Defaults","to","``","None","``",".",":","type","ignored_columns",":","List","[","str","]",",","optional",":","param","ignore_const_cols",":","Ignore","constant","columns",".","Defaults","to","``","True","``",".",":","type","ignore_const_cols",":","bool",":","param","score_each_iteration",":","Whether","to","score","during","each","iteration","of","model","training",".","Defaults","to","``","False","``",".",":","type","score_each_iteration",":","bool",":","param","transform",":","Transformation","of","training","data","Defaults","to","``","``","none","''","``",".",":","type","transform",":","Literal","[","``","none","''",",","``","standardize","''",",","``","normalize","''",",","``","demean","''",",","``","descale","''","]",":","param","pca_method",":","Specify","the","algorithm","to","use","for","computing","the","principal","components",":","GramSVD","-","uses","a","distributed","computation","of","the","Gram","matrix",",","followed","by","a","local","SVD",";","Power","-","computes","the","SVD","using","the","power","iteration","method","(","experimental",")",";","Randomized","-","uses","randomized","subspace","iteration","method",";","GLRM","-","fits","a","generalized","low-rank","model","with","L2","loss","function","and","no","regularization","and","solves","for","the","SVD","using","local","matrix","algebra","(","experimental",")","Defaults","to","``","``","gram_s_v_d","''","``",".",":","type","pca_method",":","Literal","[","``","gram_s_v_d","''",",","``","power","''",",","``","randomized","''",",","``","glrm","''","]",":","param","pca_impl",":","Specify","the","implementation","to","use","for","computing","PCA","(","via","SVD","or","EVD",")",":","MTJ_EVD_DENSEMATRIX","-","eigenvalue","decompositions","for","dense","matrix","using","MTJ",";","MTJ_EVD_SYMMMATRIX","-","eigenvalue","decompositions","for","symmetric","matrix","using","MTJ",";","MTJ_SVD_DENSEMATRIX","-","singular-value","decompositions","for","dense","matrix","using","MTJ",";","JAMA","-","eigenvalue","decompositions","for","dense","matrix","using","JAMA",".","References",":","JAMA","-","http",":","\/\/math.nist.gov\/javanumerics\/jama\/",";","MTJ","-","https",":","\/\/github.com\/fommil\/matrix-toolkits-java\/","Defaults","to","``","None","``",".",":","type","pca_impl",":","Literal","[","``","mtj_evd_densematrix","''",",","``","mtj_evd_symmmatrix","''",",","``","mtj_svd_densematrix","''",",","``","jama","''","]",",","optional",":","param","k",":","Rank","of","matrix","approximation","Defaults","to","``","1","``",".",":","type","k",":","int",":","param","max_iterations",":","Maximum","training","iterations","Defaults","to","``","1000","``",".",":","type","max_iterations",":","int",":","param","use_all_factor_levels",":","Whether","first","factor","level","is","included","in","each","categorical","expansion","Defaults","to","``","False","``",".",":","type","use_all_factor_levels",":","bool",":","param","compute_metrics",":","Whether","to","compute","metrics","on","the","training","data","Defaults","to","``","True","``",".",":","type","compute_metrics",":","bool",":","param","impute_missing",":","Whether","to","impute","missing","entries","with","the","column","mean","Defaults","to","``","False","``",".",":","type","impute_missing",":","bool",":","param","seed",":","RNG","seed","for","initialization","Defaults","to","``","-1","``",".",":","type","seed",":","int",":","param","max_runtime_secs",":","Maximum","allowed","runtime","in","seconds","for","model","training",".","Use","0","to","disable",".","Defaults","to","``","0.0","``",".",":","type","max_runtime_secs",":","float",":","param","export_checkpoints_dir",":","Automatically","export","generated","models","to","this","directory",".","Defaults","to","``","None","``",".",":","type","export_checkpoints_dir",":","str",",","optional"]
code: def __init__(
      self,
      model_id=None,  # type: Optional[Union[None, str, H2OEstimator]]
      training_frame=None,  # type: Optional[Union[None, str, H2OFrame]]
      validation_frame=None,  # type: Optional[Union[None, str, H2OFrame]]
      ignored_columns=None,  # type: Optional[List[str]]
      ignore_const_cols=True,  # type: bool
      score_each_iteration=False,  # type: bool
      transform="none",  # type: Literal["none", "standardize", "normalize", "demean", "descale"]
      pca_method="gram_s_v_d",  # type: Literal["gram_s_v_d", "power", "randomized", "glrm"]
      pca_impl=None,  # type: Optional[Literal["mtj_evd_densematrix", "mtj_evd_symmmatrix", "mtj_svd_densematrix", "jama"]]
      k=1,  # type: int
      max_iterations=1000,  # type: int
      use_all_factor_levels=False,  # type: bool
      compute_metrics=True,  # type: bool
      impute_missing=False,  # type: bool
      seed=-1,  # type: int
      max_runtime_secs=0.0,  # type: float
      export_checkpoints_dir=None,  # type: Optional[str]
  ):
      """
      :param model_id: Destination id for this model; auto-generated if not specified.
             Defaults to ``None``.
      :type model_id: Union[None, str, H2OEstimator], optional
      :param training_frame: Id of the training data frame.
             Defaults to ``None``.
      :type training_frame: Union[None, str, H2OFrame], optional
      :param validation_frame: Id of the validation data frame.
             Defaults to ``None``.
      :type validation_frame: Union[None, str, H2OFrame], optional
      :param ignored_columns: Names of columns to ignore for training.
             Defaults to ``None``.
      :type ignored_columns: List[str], optional
      :param ignore_const_cols: Ignore constant columns.
             Defaults to ``True``.
      :type ignore_const_cols: bool
      :param score_each_iteration: Whether to score during each iteration of model training.
             Defaults to ``False``.
      :type score_each_iteration: bool
      :param transform: Transformation of training data
             Defaults to ``"none"``.
      :type transform: Literal["none", "standardize", "normalize", "demean", "descale"]
      :param pca_method: Specify the algorithm to use for computing the principal components: GramSVD - uses a
             distributed computation of the Gram matrix, followed by a local SVD; Power - computes the SVD using the
             power iteration method (experimental); Randomized - uses randomized subspace iteration method; GLRM -
             fits a generalized low-rank model with L2 loss function and no regularization and solves for the SVD
             using local matrix algebra (experimental)
             Defaults to ``"gram_s_v_d"``.
      :type pca_method: Literal["gram_s_v_d", "power", "randomized", "glrm"]
      :param pca_impl: Specify the implementation to use for computing PCA (via SVD or EVD): MTJ_EVD_DENSEMATRIX -
             eigenvalue decompositions for dense matrix using MTJ; MTJ_EVD_SYMMMATRIX - eigenvalue decompositions for
             symmetric matrix using MTJ; MTJ_SVD_DENSEMATRIX - singular-value decompositions for dense matrix using
             MTJ; JAMA - eigenvalue decompositions for dense matrix using JAMA. References: JAMA -
             http://math.nist.gov/javanumerics/jama/; MTJ - https://github.com/fommil/matrix-toolkits-java/
             Defaults to ``None``.
      :type pca_impl: Literal["mtj_evd_densematrix", "mtj_evd_symmmatrix", "mtj_svd_densematrix", "jama"], optional
      :param k: Rank of matrix approximation
             Defaults to ``1``.
      :type k: int
      :param max_iterations: Maximum training iterations
             Defaults to ``1000``.
      :type max_iterations: int
      :param use_all_factor_levels: Whether first factor level is included in each categorical expansion
             Defaults to ``False``.
      :type use_all_factor_levels: bool
      :param compute_metrics: Whether to compute metrics on the training data
             Defaults to ``True``.
      :type compute_metrics: bool
      :param impute_missing: Whether to impute missing entries with the column mean
             Defaults to ``False``.
      :type impute_missing: bool
      :param seed: RNG seed for initialization
             Defaults to ``-1``.
      :type seed: int
      :param max_runtime_secs: Maximum allowed runtime in seconds for model training. Use 0 to disable.
             Defaults to ``0.0``.
      :type max_runtime_secs: float
      :param export_checkpoints_dir: Automatically export generated models to this directory.
             Defaults to ``None``.
      :type export_checkpoints_dir: str, optional
      """
      super(H2OPrincipalComponentAnalysisEstimator, self).__init__()
      self._parms = {}
      self._id = self._parms["model_id"] = model_id
      self.training_frame = training_frame
      self.validation_frame = validation_frame
      self.ignored_columns = ignored_columns
      self.ignore_const_cols = ignore_const_cols
      self.score_each_iteration = score_each_iteration
      self.transform = transform
      self.pca_method = pca_method
      self.pca_impl = pca_impl
      self.k = k
      self.max_iterations = max_iterations
      self.use_all_factor_levels = use_all_factor_levels
      self.compute_metrics = compute_metrics
      self.impute_missing = impute_missing
      self.seed = seed
      self.max_runtime_secs = max_runtime_secs
      self.export_checkpoints_dir = export_checkpoints_dir
  
imports: from h2o.estimators.estimator_base import H2OEstimator
  from h2o.exceptions import H2OValueError
  from h2o.frame import H2OFrame
  from h2o.utils.typechecks import assert_is_type, Enum, numeric
label_desc: Use image node_id 1 to create a new H2OPrincipalComponentAnalysisEstimator object from inherited base classes: H2OEstimator with example: obj = H2OPrincipalComponentAnalysisEstimator(model_id, training_frame, validation_frame, ignored_columns, ignore_const_cols, score_each_iteration, transform, pca_method, pca_impl, k, max_iterations, use_all_factor_levels, compute_metrics, impute_missing, seed, max_runtime_secs, export_checkpoints_dir)