Using mca package in Python

Question

I am trying to use the mca package to do multiple correspondence analysis in Python.

I am a bit confused as to how to use it. With PCA I would expect to fit some data (i.e. find principal components for those data) and then later I would be able to use the principal components that I found to transform unseen data.

Based on the MCA documentation, I cannot work out how to do this last step. I also don't understand what any of the weirdly cryptically named properties and methods do (i.e. .E, .L, .K, .k etc).

So far if I have a DataFrame with a column containing strings (assume this is the only column in the DF) I would do something like

import mca
ca = mca.MCA(pd.get_dummies(df, drop_first=True))

from what I can gather

ca.fs_r(1)

is the transformation of the data in df and

ca.L

is supposed to be the eigenvalues (although I get a vector of 1s that is one element fewer that my number of features?).

now if I had some more data with the same features, let's say df_new and assuming I've already converted this correctly to dummy variables, how do I find the equivalent of ca.fs_r(1) for the new data

Answer 1

One other method is to use the library prince which enables easy usage of tools such as:

1. Multiple correspondence analysis (MCA)
2. Principal component analysis (PCA)
3. Multiple factor analysis (MFA)

You can begin first by installing with:

pip install --user prince

To use MCA, it is fairly simple and can be done in a couple of steps (just like sklearn PCA method.) We first build our dataframe.

import pandas as pd 
import prince

X = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/balloons/adult+stretch.data')
X.columns = ['Color', 'Size', 'Action', 'Age', 'Inflated']

print(X.head())

mca = prince.MCA()

# outputs
>>     Color   Size   Action    Age Inflated
   0  YELLOW  SMALL  STRETCH  ADULT        T
   1  YELLOW  SMALL  STRETCH  CHILD        F
   2  YELLOW  SMALL      DIP  ADULT        F
   3  YELLOW  SMALL      DIP  CHILD        F
   4  YELLOW  LARGE  STRETCH  ADULT        T

Followed by calling the fit and transform method.

mca = mca.fit(X) # same as calling ca.fs_r(1)
mca = mca.transform(X) # same as calling ca.fs_r_sup(df_new) for *another* test set.
print(mca)

# outputs
>>         0             1
0   0.705387  8.373126e-15
1  -0.386586  8.336230e-15
2  -0.386586  6.335675e-15
3  -0.852014  6.726393e-15
4   0.783539 -6.333333e-01
5   0.783539 -6.333333e-01
6  -0.308434 -6.333333e-01
7  -0.308434 -6.333333e-01
8  -0.773862 -6.333333e-01
9   0.783539  6.333333e-01
10  0.783539  6.333333e-01
11 -0.308434  6.333333e-01
12 -0.308434  6.333333e-01
13 -0.773862  6.333333e-01
14  0.861691 -5.893240e-15
15  0.861691 -5.893240e-15
16 -0.230282 -5.930136e-15
17 -0.230282 -7.930691e-15
18 -0.695710 -7.539973e-15

You can even print out the picture diagram of it, since it incorporates matplotlib library.

ax = mca.plot_coordinates(
     X=X,
     ax=None,
     figsize=(6, 6),
     show_row_points=True,
     row_points_size=10,
     show_row_labels=False,
     show_column_points=True,
     column_points_size=30,
     show_column_labels=False,
     legend_n_cols=1
     )

ax.get_figure().savefig('images/mca_coordinates.svg')