class_docstr: PCA (Principal Component Analysis) is a fundamental dimensionality
  reduction technique used to combine features in X in linear combinations
  such that each new component captures the most information or variance of
  the data. N_components is usually small, say at 3, where it can be used for
  data visualization, data compression and exploratory analysis.
  
  cuML's multi-node multi-gpu (MNMG) PCA expects a dask-cuDF object as input
  and provides 2 algorithms, Full and Jacobi. Full (default) uses a full
  eigendecomposition then selects the top K eigenvectors. The Jacobi
  algorithm can be much faster as it iteratively tries to correct the top K
  eigenvectors, but might be less accurate.
  
  Examples
  --------
  .. code-block:: python
  
      >>> from dask_cuda import LocalCUDACluster
      >>> from dask.distributed import Client, wait
      >>> import cupy as cp
      >>> from cuml.dask.decomposition import PCA
      >>> from cuml.dask.datasets import make_blobs
  
      >>> cluster = LocalCUDACluster(threads_per_worker=1)
      >>> client = Client(cluster)
  
      >>> nrows = 6
      >>> ncols = 3
      >>> n_parts = 2
  
      >>> X_cudf, _ = make_blobs(n_samples=nrows, n_features=ncols,
      ...                        centers=1, n_parts=n_parts,
      ...                        cluster_std=0.01, random_state=10,
      ...                        dtype=cp.float32)
  
      >>> blobs = X_cudf.compute()
      >>> print(blobs) # doctest: +SKIP
      [[8.688037  3.122401  1.2581943]
      [8.705028  3.1070278 1.2705998]
      [8.70239   3.1102846 1.2716919]
      [8.695665  3.1042147 1.2635932]
      [8.681095  3.0980906 1.2745825]
      [8.705454  3.100002  1.2657361]]
  
      >>> cumlModel = PCA(n_components = 1, whiten=False)
      >>> XT = cumlModel.fit_transform(X_cudf)
      >>> print(XT.compute()) # doctest: +SKIP
      [[-1.7516235e-02]
      [ 7.8094802e-03]
      [ 4.2757220e-03]
      [-6.7228684e-05]
      [-5.0618490e-03]
      [ 1.0557819e-02]]
      >>> client.close()
      >>> cluster.close()
  
  Parameters
  ----------
  handle : cuml.Handle
      Specifies the cuml.handle that holds internal CUDA state for
      computations in this model. Most importantly, this specifies the CUDA
      stream that will be used for the model's computations, so users can
      run different models concurrently in different streams by creating
      handles in several streams.
      If it is None, a new one is created.
  n_components : int (default = 1)
      The number of top K singular vectors / values you want.
      Must be <= number(columns).
  svd_solver : 'full', 'jacobi', 'auto'
      'full': Run exact full SVD and select the components by postprocessing
      'jacobi': Iteratively compute SVD of the covariance matrix
      'auto': For compatibility with Scikit-learn. Alias for 'jacobi'.
  verbose : int or boolean, default=False
      Sets logging level. It must be one of `cuml.common.logger.level_*`.
      See :ref:`verbosity-levels` for more info.
  whiten : boolean (default = False)
      If True, de-correlates the components. This is done by dividing them by
      the corresponding singular values then multiplying by sqrt(n_samples).
      Whitening allows each component to have unit variance and removes
      multi-collinearity. It might be beneficial for downstream
      tasks like LinearRegression where correlated features cause problems.
  
  
  Attributes
  ----------
  components_ : array
      The top K components (VT.T[:,:n_components]) in U, S, VT = svd(X)
  explained_variance_ : array
      How much each component explains the variance in the data given by S**2
  explained_variance_ratio_ : array
      How much in % the variance is explained given by S**2/sum(S**2)
  singular_values_ : array
      The top K singular values. Remember all singular values >= 0
  mean_ : array
      The column wise mean of X. Used to mean - center the data first.
  noise_variance_ : float
      From Bishop 1999's Textbook. Used in later tasks like calculating the
      estimated covariance of X.
  
  Notes
  -----
  PCA considers linear combinations of features, specifically those that
  maximize global variance structure. This means PCA is fantastic for global
  structure analyses, but weak for local relationships. Consider UMAP or
  T-SNE for a locally important embedding.
  
  **Applications of PCA**
  
      PCA is used extensively in practice for data visualization and data
      compression. It has been used to visualize extremely large word
      embeddings like Word2Vec and GloVe in 2 or 3 dimensions, large
      datasets of everyday objects and images, and used to distinguish
      between cancerous cells from healthy cells.
  
  For additional docs, see `scikitlearn's PCA
  <http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html>`_.
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docstr: Fit the model with X and apply the dimensionality reduction on X.
  
  Parameters
  ----------
  X : dask cuDF
  
  Returns
  -------
  X_new : dask cuDF
docstr_tok: ["Fit","the","model","with","X","and","apply","the","dimensionality","reduction","on","X.","Parameters","--","--","--","--","--","X",":","dask","cuDF","Returns","--","--","--","-","X_new",":","dask","cuDF"]
code: def fit_transform(self, X):
      """
      Fit the model with X and apply the dimensionality reduction on X.
  
      Parameters
      ----------
      X : dask cuDF
  
      Returns
      -------
      X_new : dask cuDF
      """
      return self.fit(X).transform(X)
  
file_path: cuml/python/cuml/dask/decomposition/pca.py
imports: from cuml.dask.decomposition.base import BaseDecomposition
  from cuml.dask.decomposition.base import DecompositionSyncFitMixin
  from cuml.dask.common.base import mnmg_import
  from cuml.dask.common.base import DelayedTransformMixin
  from cuml.dask.common.base import DelayedInverseTransformMixin
label_desc: Use image node_id 3 for calling the PCA obj's underlying member method code with example usage: obj.fit_transform(X) and returns: self