python - machine - Preprocesamiento de Sklearn-PolinomialFeatures-Cómo mantener los nombres de columna/encabezados de la matriz de salida/dataframe

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Question

TLDR: cómo obtener encabezados para la matriz numpy de salida de la función sklearn.preprocessing.PolynomialFeatures ()?

Digamos que tengo el siguiente código ...

import pandas as pd
import numpy as np
from sklearn import preprocessing as pp

a = np.ones(3)
b = np.ones(3) * 2
c = np.ones(3) * 3

input_df = pd.DataFrame([a,b,c])
input_df = input_df.T
input_df.columns=[''a'', ''b'', ''c'']

input_df

    a   b   c
0   1   2   3
1   1   2   3
2   1   2   3

poly = pp.PolynomialFeatures(2)
output_nparray = poly.fit_transform(input_df)
print output_nparray

[[ 1.  1.  2.  3.  1.  2.  3.  4.  6.  9.]
 [ 1.  1.  2.  3.  1.  2.  3.  4.  6.  9.]
 [ 1.  1.  2.  3.  1.  2.  3.  4.  6.  9.]]

¿Cómo puedo obtener esa matrix / output_nparray 3x10 para llevar las etiquetas a, b, c de la forma en que se relacionan con los datos anteriores?

Answer 1

Ejemplo de trabajo, todo en una línea (supongo que la "legibilidad" no es el objetivo aquí):

target_feature_names = [''x''.join([''{}^{}''.format(pair[0],pair[1]) for pair in tuple if pair[1]!=0]) for tuple in [zip(input_df.columns,p) for p in poly.powers_]]
output_df = pd.DataFrame(output_nparray, columns = target_feature_names)

Answer 2

Esto funciona:

def PolynomialFeatures_labeled(input_df,power):
    ''''''Basically this is a cover for the sklearn preprocessing function. 
    The problem with that function is if you give it a labeled dataframe, it ouputs an unlabeled dataframe with potentially
    a whole bunch of unlabeled columns. 

    Inputs:
    input_df = Your labeled pandas dataframe (list of x''s not raised to any power) 
    power = what order polynomial you want variables up to. (use the same power as you want entered into pp.PolynomialFeatures(power) directly)

    Ouput:
    Output: This function relies on the powers_ matrix which is one of the preprocessing function''s outputs to create logical labels and 
    outputs a labeled pandas dataframe   
    ''''''
    poly = pp.PolynomialFeatures(power)
    output_nparray = poly.fit_transform(input_df)
    powers_nparray = poly.powers_

    input_feature_names = list(input_df.columns)
    target_feature_names = ["Constant Term"]
    for feature_distillation in powers_nparray[1:]:
        intermediary_label = ""
        final_label = ""
        for i in range(len(input_feature_names)):
            if feature_distillation[i] == 0:
                continue
            else:
                variable = input_feature_names[i]
                power = feature_distillation[i]
                intermediary_label = "%s^%d" % (variable,power)
                if final_label == "":         #If the final label isn''t yet specified
                    final_label = intermediary_label
                else:
                    final_label = final_label + " x " + intermediary_label
        target_feature_names.append(final_label)
    output_df = pd.DataFrame(output_nparray, columns = target_feature_names)
    return output_df

output_df = PolynomialFeatures_labeled(input_df,2)
output_df

    Constant Term   a^1 b^1 c^1 a^2 a^1 x b^1   a^1 x c^1   b^2 b^1 x c^1   c^2
0               1   1   2   3   1           2           3   4           6   9
1               1   1   2   3   1           2           3   4           6   9
2               1   1   2   3   1           2           3   4           6   9

Answer 3

scikit-learn 0.18 agregó un ingenioso método get_feature_names() !

>> input_df.columns
Index([''a'', ''b'', ''c''], dtype=''object'')

>> poly.fit_transform(input_df)
array([[ 1.,  1.,  2.,  3.,  1.,  2.,  3.,  4.,  6.,  9.],
       [ 1.,  1.,  2.,  3.,  1.,  2.,  3.,  4.,  6.,  9.],
       [ 1.,  1.,  2.,  3.,  1.,  2.,  3.,  4.,  6.,  9.]])

>> poly.get_feature_names(input_df.columns)
[''1'', ''a'', ''b'', ''c'', ''a^2'', ''a b'', ''a c'', ''b^2'', ''b c'', ''c^2'']

Tenga en cuenta que debe proporcionarle los nombres de las columnas, ya que sklearn no lo lee por separado del DataFrame.