Transform with FAMD - Is it correct ?

Hello,

I’d like to:

1. Build a FAMD model from a dataframe
2. Project a single record using this model

In the example below, I have a dataframe that I use to fit a model. I then pick a single row from the original dataframe and attempt to project it with the created FAMD model.

Why do I obtain different values between:

• Projecting the whole dataframe, then selecting the first row
• Selecting the first row of the dataframe, then projecting with the FAMD model

Is there something I missed ?

Thanks !

Running code here: https://repl.it/repls/WearyMajorNature

import prince
import pandas as pd

df = pd.DataFrame(
    {'variable_1': [4, 5, 6, 7, 11, 2, 52],
    'variable_2': [10, 20, 30, 40, 10, 74, 10],
    'variable_3': [100, 50, -30, -50, -19, -29, -20],
    'color': ['red', 'blue', 'green', 'blue', 'red', 'red', 'blue']
    })

model = prince.FAMD(
            n_components = df.shape[1],
            copy = True,
            check_input = True,
            engine = 'auto',
            random_state = 1
        ).fit(df)

print(model.row_coordinates(df))

# Let's say we want to transform a single row
row = pd.DataFrame(df.iloc[0]).transpose()

print(model.transform(row))

# Why is this transform very different than the projection of this same record in the first dataframe ?

print(model.row_coordinates(df).iloc[0])

[snthibaud]

@MaxHalford I think it does not get much more minimal than this:

import pandas as pd
from prince import FAMD

famd = FAMD(n_components=1)
df = pd.DataFrame({"A": [1, 2], "B": [3, 4]})
df["A"] = df["A"].astype("category")
famd.fit(df)
print(famd.transform(df[0:1]))
print(famd.transform(df)[0:1])

Output:

     0
0 -0.5
          0
0 -1.414214

[snthibaud]

I also noticed that PCA and MCA both work well independently. Looking at the code, I think it might be related to this line -> https://github.com/MaxHalford/prince/blob/ba8a66b6575320832b118186745ecfd85c896bdc/prince/mfa.py#L98

The data is normalized before transforming there, but it should be normalized based on the fitted data.