Rob Speare rspeare

## parallel_pandas_apply.py
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:

## Active Learning through Fisher Information Gain.ipynb

      
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                rspeare
                / Active Learning through Fisher Information Gain.ipynb
            
            
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              January 16, 2019 05:38
            
          
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## p_values_for_logreg
# test

## logistic_reg.py
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:
    ---------------

## p_values_for_logreg.py
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
	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:
	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:
	---------------
	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