class_docstr: Metaestimator for feeding Dask Arrays to an estimator blockwise.
  
  This wrapper provides a bridge between Dask objects and estimators
  implementing the ``partial_fit`` API. These *incremental learners* can
  train on batches of data. This fits well with Dask's blocked data
  structures.
  
  .. note::
  
     This meta-estimator is not appropriate for hyperparameter optimization
     on larger-than-memory datasets. For that, see
     :class:`~dask_ml.model_selection.IncrementalSearchCV` or
     :class:`~dask_ml.model_selection.HyperbandSearchCV`.
  
  See the `list of incremental learners`_ in the scikit-learn documentation
  for a list of estimators that implement the ``partial_fit`` API. Note that
  `Incremental` is not limited to just these classes, it will work on any
  estimator implementing ``partial_fit``, including those defined outside of
  scikit-learn itself.
  
  Calling :meth:`Incremental.fit` with a Dask Array will pass each block of
  the Dask array or arrays to ``estimator.partial_fit`` *sequentially*.
  
  Like :class:`ParallelPostFit`, the methods available after fitting (e.g.
  :meth:`Incremental.predict`, etc.) are all parallel and delayed.
  
  The ``estimator_`` attribute is a clone of `estimator` that was actually
  used during the call to ``fit``. All attributes learned during training
  are available on ``Incremental`` directly.
  
  .. _list of incremental learners: https://scikit-learn.org/stable/modules/computing.html#incremental-learning  # noqa
  
  Parameters
  ----------
  estimator : Estimator
      Any object supporting the scikit-learn ``partial_fit`` API.
  
  scoring : string or callable, optional
      A single string (see :ref:`scoring_parameter`) or a callable
      (see :ref:`scoring`) to evaluate the predictions on the test set.
  
      For evaluating multiple metrics, either give a list of (unique)
      strings or a dict with names as keys and callables as values.
  
      NOTE that when using custom scorers, each scorer should return a
      single value. Metric functions returning a list/array of values
      can be wrapped into multiple scorers that return one value each.
  
      See :ref:`multimetric_grid_search` for an example.
  
      .. warning::
  
         If None, the estimator's default scorer (if available) is used.
         Most scikit-learn estimators will convert large Dask arrays to
         a single NumPy array, which may exhaust the memory of your worker.
         You probably want to always specify `scoring`.
  
  random_state : int or numpy.random.RandomState, optional
      Random object that determines how to shuffle blocks.
  
  shuffle_blocks : bool, default True
      Determines whether to call ``partial_fit`` on a randomly selected chunk
      of the Dask arrays (default), or to fit in sequential order. This does
      not control shuffle between blocks or shuffling each block.
  
  predict_meta: pd.Series, pd.DataFrame, np.array deafult: None(infer)
      An empty ``pd.Series``, ``pd.DataFrame``, ``np.array`` that matches the output
      type of the estimators ``predict`` call.
      This meta is necessary for  for some estimators to work with
      ``dask.dataframe`` and ``dask.array``
  
  predict_proba_meta: pd.Series, pd.DataFrame, np.array deafult: None(infer)
      An empty ``pd.Series``, ``pd.DataFrame``, ``np.array`` that matches the output
      type of the estimators ``predict_proba`` call.
      This meta is necessary for  for some estimators to work with
      ``dask.dataframe`` and ``dask.array``
  
  transform_meta: pd.Series, pd.DataFrame, np.array deafult: None(infer)
      An empty ``pd.Series``, ``pd.DataFrame``, ``np.array`` that matches the output
      type of the estimators ``transform`` call.
      This meta is necessary for  for some estimators to work with
      ``dask.dataframe`` and ``dask.array``
  
  Attributes
  ----------
  estimator_ : Estimator
      A clone of `estimator` that was actually fit during the ``.fit`` call.
  
  See Also
  --------
  ParallelPostFit
  dask_ml.model_selection.IncrementalSearchCV
  
  Examples
  --------
  >>> from dask_ml.wrappers import Incremental
  >>> from dask_ml.datasets import make_classification
  >>> import sklearn.linear_model
  >>> X, y = make_classification(chunks=25)
  >>> est = sklearn.linear_model.SGDClassifier()
  >>> clf = Incremental(est, scoring='accuracy')
  >>> clf.fit(X, y, classes=[0, 1])
  
  When used inside a grid search, prefix the underlying estimator's
  parameter names with ``estimator__``.
  
  >>> from sklearn.model_selection import GridSearchCV
  >>> param_grid = {"estimator__alpha": [0.1, 1.0, 10.0]}
  >>> gs = GridSearchCV(clf, param_grid)
  >>> gs.fit(X, y, classes=[0, 1])
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code: def __init__(
      self,
      estimator=None,
      scoring=None,
      shuffle_blocks=True,
      random_state=None,
      assume_equal_chunks=True,
      predict_meta=None,
      predict_proba_meta=None,
      transform_meta=None,
  ):
      self.shuffle_blocks = shuffle_blocks
      self.random_state = random_state
      self.assume_equal_chunks = assume_equal_chunks
      super(Incremental, self).__init__(
          estimator=estimator,
          scoring=scoring,
          predict_meta=predict_meta,
          predict_proba_meta=predict_proba_meta,
          transform_meta=transform_meta,
      )
  
imports: import logging
  import warnings
  import dask.array
  import dask.dataframe
  import dask.delayed
  import numpy
  import sklearn.base
  import sklearn.metrics
  from sklearn.utils.validation import check_is_fitted
  from dask_ml.utils import _timer
  from ._partial import fit
  from ._utils import copy_learned_attributes
  from .metrics import check_scoring, get_scorer
label_desc: Use image node_id 1 to create a new Incremental object from inherited base classes: ParallelPostFit with example: obj = Incremental(estimator, scoring, shuffle_blocks, random_state, assume_equal_chunks, predict_meta, predict_proba_meta, transform_meta)