class_docstr: Transforms continuous data into bucketed binary data.
  
  This is a scikit-learn transformer that transform an input
  pandas DataFrame X of shape (n_samples, n_features) into a binary
  matrix of size (n_samples, n_new_features).
  Continous features are modified and extended into binary features, using
  linearly or inter-quantiles spaced bins.
  Discrete features are binary encoded with K columns, where K is the number
  of modalities.
  Other features (none of the above) are left unchanged.
  
  Parameters
  ----------
  n_cuts : `int`, default=10
      Number of cut points for continuous features.
  
  method : "quantile" or "linspace", default="quantile"
      * If ``"quantile"`` quantile-based cuts are used.
      * If ``"linspace"`` linearly spaced cuts are used.
      * If ``"given"`` bins_boundaries needs to be provided.
  
  detect_column_type : "auto" or "column_names", default="auto"
      * If ``"auto"`` feature type detection done automatically.
      * If ``"column_names"`` feature type detection done using column names.
        In this case names ending by ":continuous" means continuous
        while ":discrete" means a discrete feature
  
  remove_first : `bool`
      If `True`, first column of each binarized continuous feature block is
      removed.
  
  bins_boundaries : `list`, default="none"
      Bins boundaries for continuous features.
  
  Attributes
  ----------
  one_hot_encoder : `OneHotEncoder`
      OneHotEncoders for continuous and discrete features.
  
  bins_boundaries : `list`
      Bins boundaries for continuous features.
  
  mapper : `dict`
      Map modalities to column indexes for categorical features.
  
  feature_type : `dict`
      Features type.
  
  blocks_start : `list`
      List of indices of the beginning of each block of binarized features
  
  blocks_length : `list`
      Length of each block of binarized features
  
  References
  ----------
  http://scikit-learn.org/stable/modules/preprocessing.html#preprocessing
  
  Examples
  --------
  >>> import numpy as np
  >>> from tick.preprocessing import FeaturesBinarizer
  >>> features = np.array([[0.00902084, 0., 'z'],
  ...                      [0.46599565, 0., 2.],
  ...                      [0.52091721, 1., 2.],
  ...                      [0.47315496, 1., 1.],
  ...                      [0.08180209, 0., 0.],
  ...                      [0.45011727, 0., 0.],
  ...                      [2.04347947, 1., 20.],
  ...                      [-0.9890938, 0., 0.],
  ...                      [-0.3063761, 1., 1.],
  ...                      [0.27110903, 0., 0.]])
  >>> binarizer = FeaturesBinarizer(n_cuts=3)
  >>> binarized_features = binarizer.fit_transform(features)
  >>> # output comes as a sparse matrix
  >>> binarized_features.__class__
  <class 'scipy.sparse.csr.csr_matrix'>
  >>> # column type is automatically detected
  >>> sorted(binarizer.feature_type.items())
  [('0', 'continuous'), ('1', 'discrete'), ('2', 'discrete')]
  >>> # features is binarized (first column is removed to avoid colinearity)
  >>> binarized_features.toarray()
  array([[1., 0., 0., 0., 1., 0., 0., 0., 0., 0., 1.],
         [0., 0., 1., 0., 1., 0., 0., 0., 1., 0., 0.],
         [0., 0., 0., 1., 0., 1., 0., 0., 1., 0., 0.],
         [0., 0., 1., 0., 0., 1., 0., 1., 0., 0., 0.],
         [0., 1., 0., 0., 1., 0., 1., 0., 0., 0., 0.],
         [0., 0., 1., 0., 1., 0., 1., 0., 0., 0., 0.],
         [0., 0., 0., 1., 0., 1., 0., 0., 0., 1., 0.],
         [1., 0., 0., 0., 1., 0., 1., 0., 0., 0., 0.],
         [1., 0., 0., 0., 0., 1., 0., 1., 0., 0., 0.],
         [0., 1., 0., 0., 1., 0., 1., 0., 0., 0., 0.]])
class_docstr_tok: ["Transforms","continuous","data","into","bucketed","binary","data",".","This","is","a","scikit-learn","transformer","that","transform","an","input","pandas","DataFrame","X","of","shape","(","n_samples",",","n_features",")","into","a","binary","matrix","of","size","(","n_samples",",","n_new_features",")",".","Continous","features","are","modified","and","extended","into","binary","features",",","using","linearly","or","inter-quantiles","spaced","bins",".","Discrete","features","are","binary","encoded","with","K","columns",",","where","K","is","the","number","of","modalities",".","Other","features","(","none","of","the","above",")","are","left","unchanged",".","Parameters","--","--","--","--","--","n_cuts",":","`","int","`",",","default=10","Number","of","cut","points","for","continuous","features",".","method",":","``","quantile","''","or","``","linspace","''",",","default=","''","quantile","''","*","If","``","``","quantile","''","``","quantile-based","cuts","are","used",".","*","If","``","``","linspace","''","``","linearly","spaced","cuts","are","used",".","*","If","``","``","given","''","``","bins_boundaries","needs","to","be","provided",".","detect_column_type",":","``","auto","''","or","``","column_names","''",",","default=","''","auto","''","*","If","``","``","auto","''","``","feature","type","detection","done","automatically",".","*","If","``","``","column_names","''","``","feature","type","detection","done","using","column","names",".","In","this","case","names","ending","by","``",":","continuous","''","means","continuous","while","``",":","discrete","''","means","a","discrete","feature","remove_first",":","`","bool","`","If","`","True","`",",","first","column","of","each","binarized","continuous","feature","block","is","removed",".","bins_boundaries",":","`","list","`",",","default=","''","none","''","Bins","boundaries","for","continuous","features",".","Attributes","--","--","--","--","--","one_hot_encoder",":","`","OneHotEncoder","`","OneHotEncoders","for","continuous","and","discrete","features",".","bins_boundaries",":","`","list","`","Bins","boundaries","for","continuous","features",".","mapper",":","`","dict","`","Map","modalities","to","column","indexes","for","categorical","features",".","feature_type",":","`","dict","`","Features","type",".","blocks_start",":","`","list","`","List","of","indices","of","the","beginning","of","each","block","of","binarized","features","blocks_length",":","`","list","`","Length","of","each","block","of","binarized","features","References","--","--","--","--","--","http",":","\/\/scikit-learn.org\/stable\/modules\/preprocessing.html","#","preprocessing","Examples","--","--","--","--",">",">",">","import","numpy","as","np",">",">",">","from","tick.preprocessing","import","FeaturesBinarizer",">",">",">","features","=","np.array","(","[","[","0.00902084",",","0.",",","'","z","'","]",",","...","[","0.46599565",",","0.",",","2",".","]",",","...","[","0.52091721",",","1.",",","2",".","]",",","...","[","0.47315496",",","1.",",","1",".","]",",","...","[","0.08180209",",","0.",",","0",".","]",",","...","[","0.45011727",",","0.",",","0",".","]",",","...","[","2.04347947",",","1.",",","20",".","]",",","...","[","-0.9890938",",","0.",",","0",".","]",",","...","[","-0.3063761",",","1.",",","1",".","]",",","...","[","0.27110903",",","0.",",","0",".","]","]",")",">",">",">","binarizer","=","FeaturesBinarizer","(","n_cuts=3",")",">",">",">","binarized_features","=","binarizer.fit_transform","(","features",")",">",">",">","#","output","comes","as","a","sparse","matrix",">",">",">","binarized_features.__class__","<","class","'scipy.sparse.csr.csr_matrix","'",">",">",">",">","#","column","type","is","automatically","detected",">",">",">","sorted","(","binarizer.feature_type.items","(",")",")","[","(","'","0","'",",","'continuous","'",")",",","(","'","1","'",",","'discrete","'",")",",","(","'","2","'",",","'discrete","'",")","]",">",">",">","#","features","is","binarized","(","first","column","is","removed","to","avoid","colinearity",")",">",">",">","binarized_features.toarray","(",")","array","(","[","[","1.",",","0.",",","0.",",","0.",",","1.",",","0.",",","0.",",","0.",",","0.",",","0.",",","1",".","]",",","[","0.",",","0.",",","1.",",","0.",",","1.",",","0.",",","0.",",","0.",",","1.",",","0.",",","0",".","]",",","[","0.",",","0.",",","0.",",","1.",",","0.",",","1.",",","0.",",","0.",",","1.",",","0.",",","0",".","]",",","[","0.",",","0.",",","1.",",","0.",",","0.",",","1.",",","0.",",","1.",",","0.",",","0.",",","0",".","]",",","[","0.",",","1.",",","0.",",","0.",",","1.",",","0.",",","1.",",","0.",",","0.",",","0.",",","0",".","]",",","[","0.",",","0.",",","1.",",","0.",",","1.",",","0.",",","1.",",","0.",",","0.",",","0.",",","0",".","]",",","[","0.",",","0.",",","0.",",","1.",",","0.",",","1.",",","0.",",","0.",",","0.",",","1.",",","0",".","]",",","[","1.",",","0.",",","0.",",","0.",",","1.",",","0.",",","1.",",","0.",",","0.",",","0.",",","0",".","]",",","[","1.",",","0.",",","0.",",","0.",",","0.",",","1.",",","0.",",","1.",",","0.",",","0.",",","0",".","]",",","[","0.",",","1.",",","0.",",","0.",",","1.",",","0.",",","1.",",","0.",",","0.",",","0.",",","0",".","]","]",")"]
docstr: Get the type of a single feature.
  
  Parameters
  ----------
  feature_name : `str`
      The feature name
  
  feature : `np.ndarray`, shape=(n_samples,)
      The column containing the feature
  
  fit : `bool`
      If `True`, we save the feature type.
      If `False`, we take the corresponding saved feature type.
  
  Returns
  -------
  output : `str`
      The type of the feature (either `continuous` or `discrete`).
docstr_tok: ["Get","the","type","of","a","single","feature",".","Parameters","--","--","--","--","--","feature_name",":","`","str","`","The","feature","name","feature",":","`","np.ndarray","`",",","shape=","(","n_samples",",",")","The","column","containing","the","feature","fit",":","`","bool","`","If","`","True","`",",","we","save","the","feature","type",".","If","`","False","`",",","we","take","the","corresponding","saved","feature","type",".","Returns","--","--","--","-","output",":","`","str","`","The","type","of","the","feature","(","either","`","continuous","`","or","`","discrete","`",")","."]
code: def _get_feature_type(self, feature_name, feature, fit=False):
      """Get the type of a single feature.
  
      Parameters
      ----------
      feature_name : `str`
          The feature name
  
      feature : `np.ndarray`, shape=(n_samples,)
          The column containing the feature
  
      fit : `bool`
          If `True`, we save the feature type.
          If `False`, we take the corresponding saved feature type.
  
      Returns
      -------
      output : `str`
          The type of the feature (either `continuous` or `discrete`).
      """
      if fit:
          feature_type = FeaturesBinarizer._detect_feature_type(
              feature,
              feature_name=feature_name,
              detect_column_type=self.detect_column_type,
          )
          self.feature_type[feature_name] = feature_type
  
      elif self._fitted:
          feature_type = self.feature_type[feature_name]
      else:
          raise ValueError(
              "cannot call method with fit=True if object "
              "has not been fitted"
          )
  
      return feature_type
  
imports: import numpy
  import pandas
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.preprocessing import OneHotEncoder
  from tick.base import Base
label_desc: Use image node_id 11 for calling the FeaturesBinarizer obj's underlying member method code with example usage: obj._get_feature_type(feature_name, feature, fit) and returns: feature_type