class_docstr: Vowpal Wabbit Scikit-learn Base Estimator wrapper
class_docstr_tok: ["Vowpal","Wabbit","Scikit-learn","Base","Estimator","wrapper"]
docstr: Predict with Vowpal Wabbit model
  
  Args:
      X ({array-like, sparse matrix}, shape \(n_samples, n_features or 1\)):
          Training vector, where n_samples in the number of samples and
          n_features is the number of features.
          if not using convert_to_vw, X is expected to be a list of vw formatted feature vector strings with labels
  
  Returns:
      array-like, shape (n_samples, 1 or n_classes): y. Output vector relative to X.
code: def predict(self, X):
      """Predict with Vowpal Wabbit model
  
      Args:
          X ({array-like, sparse matrix}, shape \(n_samples, n_features or 1\)):
              Training vector, where n_samples in the number of samples and
              n_features is the number of features.
              if not using convert_to_vw, X is expected to be a list of vw formatted feature vector strings with labels
  
      Returns:
          array-like, shape (n_samples, 1 or n_classes): y. Output vector relative to X.
      """
  
      # check_is_fitted
      if self.vw_ is None:
          msg = (
              "This %(name)s instance is not fitted yet. Call 'fit' with "
              "appropriate arguments before using this method."
          )
          raise NotFittedError(msg % {"name": self.__class__.__name__})
  
      if self.convert_to_vw:
          X = tovw(X)
  
      if hasattr(X, "__len__"):
          num_samples = len(X)
      elif hasattr(X, "__shape__"):
          num_samples = X.shape[0]
      elif hasattr(X, "__array__"):
          num_samples = np.asarray(X).shape[0]
      else:
          raise Exception("Invalid type for input X")
  
      model = self.get_vw()
  
      shape = [num_samples]
      classes = getattr(self, "classes_", None)
      if classes is None:
          for estimator in ["csoaa", "ect", "oaa", "wap"]:
              n_classes = getattr(self, estimator)
              if n_classes is not None:
                  break
      else:
          n_classes = len(classes)
      if n_classes is not None and getattr(
          self, "probabilities", False
      ):
          shape.append(n_classes)
      y = np.empty(shape)
  
      # predict examples
      for idx, x in enumerate(X):
          y[idx] = model.predict(ec=x)
  
      return y
  
imports: import io
  import os
  import re
  from tempfile import NamedTemporaryFile
  from typing import Dict
  import numpy
  from scipy.sparse import csr_matrix
  from scipy.special import logit
  from sklearn.exceptions import NotFittedError
  from sklearn.base import BaseEstimator, RegressorMixin
  from sklearn.utils.extmath import log_logistic
  from sklearn.linear_model import LogisticRegression
  from sklearn.datasets import dump_svmlight_file
  from sklearn.utils import check_array, check_X_y, shuffle
  from vowpalwabbit import Workspace
label_desc: Use image node_id 4 for calling the VW obj's underlying member method code with example usage: obj.predict(X) and returns: y