le_dict[colname] = deepcopy(le)
# Encode ordinal data, modalities have been sorted (at best)
ord_le = LabelEncoder()
train['education.num'] = ord_le.fit_transform(
train['education.num'])
le_dict['education.num'] = deepcopy(ord_le)
# Encode capital.gain and capital.loss and capital.gain as ordinal variables
for col in ['capital.gain', 'capital.loss']:
le = LabelEncoder()
train[col] = le.fit_transform(train[col])
le_dict[col] = deepcopy(le)
nj, nj_bin, nj_ord, nj_categ = compute_nj(train, var_distrib)
nb_cont = np.sum(var_distrib == 'continuous')
# Feature category (cf)
dtype = {
train.columns[j]: dtypes_dict[var_distrib[j]]
for j in range(p)
}
train = train.astype(dtype, copy=True)
numobs = len(train)
# Defining distances over the features
dm = gower_matrix(train, cat_features=cat_features)
#*****************************************************************
for col_idx, colname in enumerate(full_contra.columns):
if var_distrib[col_idx] == 'bernoulli':
le = LabelEncoder()
full_contra[colname] = le.fit_transform(full_contra[colname])
#le_dict[colname] = deepcopy(le)
# Encode ordinal data
for col_idx, colname in enumerate(full_contra.columns):
if var_distrib[col_idx] == 'ordinal':
le = LabelEncoder()
full_contra[colname] = le.fit_transform(full_contra[colname])
#le_dict[colname] = deepcopy(le)
#y = y.where(~nan_mask, np.nan)
nj, nj_bin, nj_ord, nj_categ = compute_nj(full_contra, var_distrib)
nb_cont = np.sum(var_distrib == 'continuous')
p_new = full_contra.shape[1]
# Feature category (cf)
dtype = {full_contra.columns[j]: dtypes_dict[var_distrib[j]] for j in range(p_new)}
full_contra = full_contra.astype(dtype, copy=True)
# Feature category (cf)
cat_features = var_distrib == 'categorical'
# Defining distance matrix
dm3 = gower_matrix(full_contra, cat_features = cat_features)
#===========================================#
#===========================================#
var_distrib = np.array(['ordinal', 'continuous', 'continuous', 'continuous',\
'continuous', 'continuous', 'continuous', 'continuous'])
# Ordinal data already encoded
y_categ_non_enc = deepcopy(y)
vd_categ_non_enc = deepcopy(var_distrib)
# No categ data
# No binary data
enc = OneHotEncoder(sparse=False, drop='first')
labels_oh = enc.fit_transform(np.array(labels).reshape(-1, 1)).flatten()
nj, nj_bin, nj_ord, n_categ = compute_nj(y, var_distrib)
y_np = y.values
nb_cont = np.sum(var_distrib == 'continuous')
p_new = y.shape[1]
# Feature category (cf)
cf_non_enc = np.logical_or(vd_categ_non_enc == 'categorical',
vd_categ_non_enc == 'bernoulli')
# Non encoded version of the dataset:
y_nenc_typed = y_categ_non_enc.astype(np.object)
y_np_nenc = y_nenc_typed.values
# Defining distances over the non encoded features
dm = gower_matrix(y_nenc_typed, cat_features=cf_non_enc)