human
I am working on the file "Supervised_Classification.ipynb".
The first task is:
Entre toutes les variables : ACM
Nous cherchons un graphique qui serait plus parlant. Vu le nombre de variable catégorielles, nous pensons à effectuer une analsye des correspondances multiples. Malheureusement il n'y a pas de modules évidents pour pouvoir en effectuer une. Nous sommes donc aller prendre le code source de Emre Safak sur Github. Il faut dans un second temps aussi transformer toutes nos variables en variables binaires pour pouvoir appliquer une acm
Can you write Python program for it?
 ... Give me a the Python program you requested?
assistant
#Here is the Python program you requested.

#de l'art du copier/coller
from scipy.linalg import diagsvd
import numpy as np
import pandas as pd
import functools

def process_df(DF, cols, ncols):
    if cols:  # if you want us to do the dummy coding
        K = len(cols)  # the number of categories
        X = dummy(DF, cols)
    else:  # if you want to dummy code it yourself or do all the cols
        K = ncols
        if ncols is None:  # be sure to pass K if you didn't multi-index
            K = len(DF.columns)  # ... it with mca.dummy()
            if not K:
                raise ValueError("Your DataFrame has no columns.")
        elif not isinstance(ncols, int) or ncols <= 0 or \
                        ncols > len(DF.columns):  # if you dummy coded it yourself
            raise ValueError("You must pass a valid number of columns.")
        X = DF
    J = X.shape[1]
    return X, K, J

def dummy(DF, cols=None):
    """Dummy code select columns of a DataFrame."""
    return pd.concat((pd.get_dummies(DF[col])
        for col in (DF.columns if cols is None else cols)),
        axis=1, keys=DF.columns)

def _mul(*args):
    """An internal method to multiply matrices."""
    return functools.reduce(np.dot, args)

class MCA(object):
    """Run MCA on selected columns of a pd DataFrame.
    
    If the column are specified, assume that they hold
    categorical variables that need to be replaced with
    dummy indicators, otherwise process the DataFrame as is.
    'cols': The columns of the DataFrame to process.
    'ncols': The number of columns before dummy coding. To be passed if cols isn't.
    'benzecri': Perform Benzécri correction (default: True)
    'TOL': value below which to round eigenvalues to zero (default: 1e-4)
    """

    def __init__(self, DF, cols=None, ncols=None, benzecri=True, TOL=1e-4):

        X, self.K, self.J = process_df(DF, cols, ncols)
        S = X.sum().sum()
        Z = X / S  # correspondence matrix
        self.r = Z.sum(axis=1)
        self.c = Z.sum()
        self._numitems = len(DF)
        self.cor = benzecri
  ...