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from joblib import Parallel, delayed
import functools
import pandas as pd

def parallel_apply(partition_col=None, n_partitions=None):
    """
    This decorator wraps any transformer function that takes in a pandas dataframe as some keyword argument, "data",
    and returns a pandas dataframe. 
    
    Signature must be:

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from sklearn.linear_model import LogisticRegression
import numpy as np
import scipy.stats as stat
from scipy.sparse import issparse

class ActiveLearningLogisticRegression(LogisticRegression):
    """ Wrapper class for scikit-learn's Logistic Regression classifier.

    New Attributes:
    --------------- 

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from sklearn import linear_model
import numpy as np
import scipy.stats as stat

class LogisticReg:
    """
    Wrapper Class for Logistic Regression which has the usual sklearn instance 
    in an attribute self.model, and pvalues, z scores and estimated 
    errors for each coefficient in 
    

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# test