# Keep only widows
marital_idx = np.argmax(varnames == 'marital.status')
widowed_idx = np.argmax(
le_dict['marital.status'].classes_ == 'Widowed')
authorized_ranges[:, 0,
marital_idx] = [widowed_idx,
widowed_idx] # Only widowed
else:
raise RuntimeError('Not implemented yet')
#*****************************************************************
# Run MIAMI
#*****************************************************************
init = dim_reduce_init(train, n_clusters, k, r, nj, var_distrib, seed = None,\
use_famd=True)
out = MIAMI(train, n_clusters, r, k, init, var_distrib, nj, authorized_ranges, nb_pobs, it,\
eps, maxstep, seed, perform_selec = False, dm = dm, max_patience = 0)
print('MIAMI has kept one observation over', round(1 / out['share_kept_pseudo_obs']),\
'observations generated')
acceptance_rate[design].append(out['share_kept_pseudo_obs'])
pred = pd.DataFrame(out['y_all'], columns=train.columns)
#================================================================
# Inverse transform the datasets
#================================================================
for j, colname in enumerate(train.columns):
if colname in le_dict.keys():
r = np.array([2, 1])
numobs = len(full_contra)
k = [n_clusters]
seed = 1
init_seed = 2
eps = 1E-05
it = 10
maxstep = 100
#===========================================#
# MI2AMI initialisation
#===========================================#
init_full = dim_reduce_init(full_contra, n_clusters, k, r, nj, var_distrib, seed = None,\
use_famd=True)
out_full = MI2AMI(full_contra.astype(float), n_clusters, r, k, init_full, var_distrib, nj, nan_mask,\
nb_pobs, it, eps, maxstep, seed, dm = dm3, perform_selec = False)
completed_y3 = pd.DataFrame(out_full['completed_y'].round(0), columns = full_contra.columns)
#===========================================#
# Comparison
#===========================================#
#======================================
# mu_s
#======================================
out['mu'][0]
out2['mu'][0]
# Defining distances over the features
cat_features = pd.Series(var_distrib).isin(
['categorical', 'bernoulli']).to_list()
dm = gower_matrix(train.astype(np.object), cat_features=cat_features)
dtype = {train.columns[j]: np.float64 if (var_distrib[j] != 'bernoulli') and \
(var_distrib[j] != 'categorical') else np.str for j in range(p_new)}
train = train.astype(dtype, copy=True)
numobs = len(train)
#*****************************************************************
# Run MIAMI
#*****************************************************************
prince_init = dim_reduce_init(train, 2, k, r, nj, var_distrib, seed = None,\
use_famd=True)
out = MIAMI(train_np, 'auto', r, k, prince_init, var_distrib, nj, authorized_ranges, nb_pobs, it,\
eps, maxstep, seed, perform_selec = False, dm = dm, max_patience = 0)
print('MIAMI has kept one observation over', round(1 / out['share_kept_pseudo_obs']),\
'observations generated')
acceptance_rate[design].append(out['share_kept_pseudo_obs'])
#*****************************************************************
# Visualisation result
#*****************************************************************
train_new_np = out['y_all'][len(train):]
train_new = pd.DataFrame(train_new_np, columns=train.columns)
le_dict = {**categ_dict, **bin_dict, **k_dict}
def MDGMM(y, n_clusters, r, k, init, var_distrib, nj, it = 50, \
eps = 1E-05, maxstep = 100, seed = None, perform_selec = True):
''' Fit a Generalized Linear Mixture of Latent Variables Model (GLMLVM)
y (numobs x p ndarray): The observations containing mixed variables
n_clusters (int or str): The number of clusters to look for in the data or the use mode of the MDGMM
r (dict): The dimension of latent variables through the first 2 layers
k (dict): The number of components of the latent Gaussian mixture layers
init (dict): The initialisation parameters for the algorithm
var_distrib (p 1darray): An array containing the types of the variables in y
nj (p 1darray): For binary/count data: The maximum values that the variable can take.
For ordinal data: the number of different existing categories for each variable
For categorical data: the number of different existing categories for each variable
it (int): The maximum number of MCEM iterations of the algorithm
eps (float): If the likelihood increase by less than eps then the algorithm stops
maxstep (int): The maximum number of optimisation step for each variable
seed (int): The random state seed to set (Only for numpy generated data for the moment)
perform_selec (Bool): Whether to perform architecture selection or not
------------------------------------------------------------------------------------------------
returns (dict): The predicted classes, the likelihood through the EM steps
and a continuous representation of the data
'''
# Break the reference link
k = deepcopy(k)
r = deepcopy(r)
best_k = deepcopy(k)
best_r = deepcopy(r)
# Add other checks for the other variables
check_inputs(k, r)
prev_lik = - 1E15
best_lik = -1E15
tol = 0.01
max_patience = 1
patience = 0
#====================================================
# Initialize the parameters
#====================================================
eta_c, eta_d, H_c, H_d, psi_c, psi_d = dispatch_dgmm_init(init)
lambda_bin, lambda_ord, lambda_categ = dispatch_gllvm_init(init)
w_s_c, w_s_d = dispatch_paths_init(init)
numobs = len(y)
likelihood = []
it_num = 0
ratio = 1000
np.random.seed = seed
#====================================================
# Dispatch variables between categories
#====================================================
y_bin = y[:, np.logical_or(var_distrib == 'bernoulli',\
var_distrib == 'binomial')]
nj_bin = nj[np.logical_or(var_distrib == 'bernoulli',\
var_distrib == 'binomial')]
nj_bin = nj_bin.astype(int)
nb_bin = len(nj_bin)
y_ord = y[:, var_distrib == 'ordinal']
nj_ord = nj[var_distrib == 'ordinal']
nj_ord = nj_ord.astype(int)
nb_ord = len(nj_ord)
y_categ = y[:, var_distrib == 'categorical']
nj_categ = nj[var_distrib == 'categorical'].astype(int)
nb_categ = len(nj_categ)
yc = y[:, var_distrib == 'continuous']
ss = StandardScaler()
yc = ss.fit_transform(yc)
nb_cont = yc.shape[1]
# *_1L standsds for quantities going through all the network (head + tail)
k_1L, L_1L, L, bar_L, S_1L = nb_comps_and_layers(k)
r_1L = {'c': r['c'] + r['t'], 'd': r['d'] + r['t'], 't': r['t']}
best_sil = [-1.1 for l in range(L['t'] - 1)] if n_clusters == 'multi' else -1.1
new_sil = [-1.1 for l in range(L['t'] - 1)] if n_clusters == 'multi' else -1.1
M = M_growth(1, r_1L, numobs)
if nb_bin + nb_ord + nb_categ == 0: # Create the InputError class and change this
raise ValueError('Input does not contain discrete variables,\
consider using a regular DGMM')
if nb_cont == 0: # Create the InputError class and change this
raise ValueError('Input does not contain continuous values,\
consider using a DDGMM')
# Compute the Gower matrix
cat_features = np.logical_or(var_distrib == 'categorical', var_distrib == 'bernoulli')
dm = gower_matrix(y, cat_features = cat_features)
while (it_num < it) & ((ratio > eps) | (patience <= max_patience)):
print(it_num)
# The clustering layer is the one used to perform the clustering
# i.e. the layer l such that k[l] == n_clusters
if not(isnumeric(n_clusters)):
if n_clusters == 'auto':
clustering_layer = 0
elif n_clusters == 'multi':
clustering_layer = list(range(L['t'] - 1))
else:
raise ValueError('Please enter an int, auto or multi for n_clusters')
else:
assert (np.array(k['t']) == n_clusters).any()
clustering_layer = np.argmax(np.array(k['t']) == n_clusters)
#####################################################################################
################################# MC step ############################################
#####################################################################################
#=====================================================================
# Draw from f(z^{l} | s, Theta) for both heads and tail
#=====================================================================
mu_s_c, sigma_s_c = compute_path_params(eta_c, H_c, psi_c)
sigma_s_c = ensure_psd(sigma_s_c)
mu_s_d, sigma_s_d = compute_path_params(eta_d, H_d, psi_d)
sigma_s_d = ensure_psd(sigma_s_d)
z_s_c, zc_s_c, z_s_d, zc_s_d = draw_z_s_all_network(mu_s_c, sigma_s_c,\
mu_s_d, sigma_s_d, yc, eta_c, eta_d, S_1L, L, M)
#========================================================================
# Draw from f(z^{l+1} | z^{l}, s, Theta) for l >= 1
#========================================================================
# Create wrapper as before and after
chsi_c = compute_chsi(H_c, psi_c, mu_s_c, sigma_s_c)
chsi_c = ensure_psd(chsi_c)
rho_c = compute_rho(eta_c, H_c, psi_c, mu_s_c, sigma_s_c, zc_s_c, chsi_c)
chsi_d = compute_chsi(H_d, psi_d, mu_s_d, sigma_s_d)
chsi_d = ensure_psd(chsi_d)
rho_d = compute_rho(eta_d, H_d, psi_d, mu_s_d, sigma_s_d, zc_s_d, chsi_d)
# In the following z2 and z1 will denote z^{l+1} and z^{l} respectively
z2_z1s_c, z2_z1s_d = draw_z2_z1s_network(chsi_c, chsi_d, rho_c, \
rho_d, M, r_1L, L)
#=======================================================================
# Compute the p(y^D| z1) for all discrete variables
#=======================================================================
py_zl1_d = fy_zl1(lambda_bin, y_bin, nj_bin, lambda_ord, y_ord, nj_ord,\
lambda_categ, y_categ, nj_categ, z_s_d[0])
#========================================================================
# Draw from p(z1 | y, s) proportional to p(y | z1) * p(z1 | s) for all s
#========================================================================
zl1_ys_d = draw_zl1_ys(z_s_d, py_zl1_d, M['d'])
#####################################################################################
################################# E step ############################################
#####################################################################################
#=====================================================================
# Compute quantities necessary for E steps of both heads and tail
#=====================================================================
# Discrete head quantities
pzl1_ys_d, ps_y_d, py_d = E_step_GLLVM(z_s_d[0], mu_s_d[0], sigma_s_d[0], w_s_d, py_zl1_d)
py_s_d = ps_y_d * py_d / w_s_d[n_axis]
# Continuous head quantities
ps_y_c, py_s_c, py_c = continuous_lik(yc, mu_s_c[0], sigma_s_c[0], w_s_c)
pz_s_d = fz_s(z_s_d, mu_s_d, sigma_s_d)
pz_s_c = fz_s(z_s_c, mu_s_c, sigma_s_c)
#=====================================================================
# Compute p(z^{(l)}| s, y). Equation (5) of the paper
#=====================================================================
# Compute pz2_z1s_d and pz2_z1s_d for the tail indices whereas it is useless
pz2_z1s_d = fz2_z1s(t(pzl1_ys_d, (1, 0, 2)), z2_z1s_d, chsi_d, rho_d, S_1L['d'])
pz_ys_d = fz_ys(t(pzl1_ys_d, (1, 0, 2)), pz2_z1s_d)
pz2_z1s_c = fz2_z1s([], z2_z1s_c, chsi_c, rho_c, S_1L['c'])
pz_ys_c = fz_ys([], pz2_z1s_c)
pz2_z1s_t = fz2_z1s([], z2_z1s_c[bar_L['c']:], chsi_c[bar_L['c']:], \
rho_c[bar_L['c']:], S_1L['t'])
# Junction layer computations
# Compute p(zC |s)
py_zs_d = fy_zs(pz_ys_d, py_s_d)
py_zs_c = fy_zs(pz_ys_c, py_s_c)
# Compute p(zt | yC, yD, sC, SD)
pzt_yCyDs = fz_yCyDs(py_zs_c, pz_ys_d, py_s_c, M, S_1L, L)
#=====================================================================
# Compute MFA expectations
#=====================================================================
# Discrete head.
Ez_ys_d, E_z1z2T_ys_d, E_z2z2T_ys_d, EeeT_ys_d = \
E_step_DGMM_d(zl1_ys_d, H_d, z_s_d, zc_s_d, z2_z1s_d, pz_ys_d,\
pz2_z1s_d, S_1L['d'], L['d'])
# Continuous head
Ez_ys_c, E_z1z2T_ys_c, E_z2z2T_ys_c, EeeT_ys_c = \
E_step_DGMM_c(H_c, z_s_c, zc_s_c, z2_z1s_c, pz_ys_c,\
pz2_z1s_c, S_1L['c'], L['c'])
# Junction layers
Ez_ys_t, E_z1z2T_ys_t, E_z2z2T_ys_t, EeeT_ys_t = \
E_step_DGMM_t(H_c[bar_L['c']:], \
z_s_c[bar_L['c']:], zc_s_c[bar_L['c']:], z2_z1s_c[bar_L['c']:],\
pzt_yCyDs, pz2_z1s_t, S_1L, L, k_1L)
# Error here for the first two terms: p(y^h | z^t, s^C) != p(y^h | z^t, s^{1C:L})
pst_yCyD = fst_yCyD(py_zs_c, py_zs_d, pz_s_d, w_s_c, w_s_d, k_1L, L)
###########################################################################
############################ M step #######################################
###########################################################################
#=======================================================
# Compute DGMM Parameters
#=======================================================
# Discrete head
w_s_d = np.mean(ps_y_d, axis = 0)
eta_d_barL, H_d_barL, psi_d_barL = M_step_DGMM(Ez_ys_d, E_z1z2T_ys_d, E_z2z2T_ys_d, \
EeeT_ys_d, ps_y_d, H_d, k_1L['d'][:-1],\
L_1L['d'], r_1L['d'])
# Add dispatching function here
eta_d[:bar_L['d']] = eta_d_barL
H_d[:bar_L['d']] = H_d_barL
psi_d[:bar_L['d']] = psi_d_barL
# Continuous head
w_s_c = np.mean(ps_y_c, axis = 0)
eta_c_barL, H_c_barL, psi_c_barL = M_step_DGMM(Ez_ys_c, E_z1z2T_ys_c, E_z2z2T_ys_c, \
EeeT_ys_c, ps_y_c, H_c, k_1L['c'][:-1],\
L_1L['c'] + 1, r_1L['c'])
eta_c[:bar_L['c']] = eta_c_barL
H_c[:bar_L['c']] = H_c_barL
psi_c[:bar_L['c']] = psi_c_barL
# Common tail
eta_t, H_t, psi_t, Ezst_y = M_step_DGMM_t(Ez_ys_t, E_z1z2T_ys_t, E_z2z2T_ys_t, \
EeeT_ys_t, ps_y_c, ps_y_d, pst_yCyD, \
H_c[bar_L['c']:], S_1L, k_1L, \
L_1L, L, r_1L['t'])
eta_d[bar_L['d']:] = eta_t
H_d[bar_L['d']:] = H_t
psi_d[bar_L['d']:] = psi_t
eta_c[bar_L['c']:] = eta_t
H_c[bar_L['c']:] = H_t
psi_c[bar_L['c']:] = psi_t
#=======================================================
# Identifiability conditions
#=======================================================
w_s_t = np.mean(pst_yCyD, axis = 0)
eta_d, H_d, psi_d, AT_d, eta_c, H_c, psi_c, AT_c = network_identifiability(eta_d, \
H_d, psi_d, eta_c, H_c, psi_c, w_s_c, w_s_d, w_s_t, bar_L)
#=======================================================
# Compute GLLVM Parameters
#=======================================================
# We optimize each column separately as it is faster than all column jointly
# (and more relevant with the independence hypothesis)
lambda_bin = bin_params_GLLVM(y_bin, nj_bin, lambda_bin, ps_y_d, \
pzl1_ys_d, z_s_d[0], AT_d[0], tol = tol, maxstep = maxstep)
lambda_ord = ord_params_GLLVM(y_ord, nj_ord, lambda_ord, ps_y_d, \
pzl1_ys_d, z_s_d[0], AT_d[0], tol = tol, maxstep = maxstep)
lambda_categ = categ_params_GLLVM(y_categ, nj_categ, lambda_categ, ps_y_d,\
pzl1_ys_d, z_s_d[0], AT_d[0], tol = tol, maxstep = maxstep)
###########################################################################
################## Clustering parameters updating #########################
###########################################################################
new_lik = np.sum(np.log(py_d) + np.log(py_c))
likelihood.append(new_lik)
ratio = (new_lik - prev_lik)/abs(prev_lik)
if n_clusters == 'multi':
temp_classes = []
z_tail = []
classes = [[] for l in range(L['t'] - 1)]
for l in clustering_layer:
idx_to_sum = tuple(set(range(1, L['t'] + 1)) -\
set([clustering_layer[l] + 1]))
psl_y = pst_yCyD.reshape(numobs, *k['t'],\
order = 'C').sum(idx_to_sum)
temp_class_l = np.argmax(psl_y, axis = 1)
sil_l = silhouette_score(dm, temp_class_l, metric = 'precomputed')
temp_classes.append(temp_class_l)
#z_tail.append(Ezst_y[l].sum(1))
new_sil[l] = sil_l
#z_tail = []
for l in range(L['t'] - 1):
zl = Ezst_y[l].sum(1)
z_tail.append(zl)
if best_sil[l] < new_sil[l]:
# Update the quantity if the silhouette score is better
best_sil[l] = deepcopy(new_sil[l])
classes[l] = deepcopy(temp_classes[l])
if zl.shape[-1] == 3:
plot_3d(zl, classes[l])
elif zl.shape[-1] == 2:
plot_2d(zl, classes[l])
else:
idx_to_sum = tuple(set(range(1, L['t'] + 1)) - set([clustering_layer + 1]))
psl_y = pst_yCyD.reshape(numobs, *k['t'], order = 'C').sum(idx_to_sum)
temp_classes = np.argmax(psl_y, axis = 1)
try:
new_sil = silhouette_score(dm, temp_classes, metric = 'precomputed')
except:
new_sil = -1
z_tail = [Ezst_y[l].sum(1) for l in range(L['t'] - 1)]
if best_sil < new_sil:
# Update the quantity if the silhouette score is better
zl = z_tail[clustering_layer]
best_sil = deepcopy(new_sil)
classes = deepcopy(temp_classes)
if zl.shape[-1] == 3:
plot_3d(zl, classes)
elif zl.shape[-1] == 2:
plot_2d(zl, classes)
# Refresh the likelihood if best
if best_lik < new_lik:
best_lik = deepcopy(prev_lik)
if prev_lik < new_lik:
patience = 0
M = M_growth(it_num + 1, r_1L, numobs)
else:
patience += 1
###########################################################################
######################## Parameter selection #############################
###########################################################################
min_nb_clusters = 2
is_not_min_specif = not(is_min_architecture_reached(k, r, min_nb_clusters))
if look_for_simpler_network(it_num) & perform_selec & is_not_min_specif:
# To add: selection according to categ
r_to_keep = r_select(y_bin, y_ord, y_categ, yc, zl1_ys_d,\
z2_z1s_d[:bar_L['d']], w_s_d, z2_z1s_c[:bar_L['c']],
z2_z1s_c[bar_L['c']:], n_clusters)
# Check layer deletion
is_c_layer_deletion = np.any([len(rl) == 0 for rl in r_to_keep['c']])
is_d_layer_deletion = np.any([len(rl) == 0 for rl in r_to_keep['d']])
is_head_layer_deletion = np.any([is_c_layer_deletion, is_d_layer_deletion])
if is_head_layer_deletion:
# Restart the algorithm
if is_c_layer_deletion:
r['c'] = [len(rl) for rl in r_to_keep['c'][:-1]]
k['c'] = k['c'][:-1]
if is_d_layer_deletion:
r['d'] = [len(rl) for rl in r_to_keep['d'][:-1]]
k['d'] = k['d'][:-1]
init = dim_reduce_init(pd.DataFrame(y), n_clusters, k, r, nj, var_distrib,\
seed = None)
eta_c, eta_d, H_c, H_d, psi_c, psi_d = dispatch_dgmm_init(init)
lambda_bin, lambda_ord, lambda_categ = dispatch_gllvm_init(init)
w_s_c, w_s_d = dispatch_paths_init(init)
# *_1L standsds for quantities going through all the network (head + tail)
k_1L, L_1L, L, bar_L, S_1L = nb_comps_and_layers(k)
r_1L = {'c': r['c'] + r['t'], 'd': r['d'] + r['t'], 't': r['t']}
M = M_growth(it_num + 1, r_1L, numobs)
prev_lik = deepcopy(new_lik)
it_num = it_num + 1
print(likelihood)
print('Restarting the algorithm')
continue
new_Lt = np.sum([len(rl) != 0 for rl in r_to_keep['t']]) #- 1
# If r_l == 0, delete the last l + 1: layers
new_Lt = np.sum([len(rl) != 0 for rl in r_to_keep['t']]) #- 1
#w_s_t = pst_yCyD.mean(0)
k_to_keep = k_select(w_s_c, w_s_d, w_s_t, k, new_Lt, clustering_layer, n_clusters)
is_selection = check_if_selection(r_to_keep, r, k_to_keep, k, L, new_Lt)
assert new_Lt > 0 # > 1 ?
if n_clusters == 'multi':
assert new_Lt == L['t']
if is_selection:
# Part to change when update also number of layers on each head
nb_deleted_layers_tail = L['t'] - new_Lt
L['t'] = new_Lt
L_1L = {keys: values - nb_deleted_layers_tail for keys, values in L_1L.items()}
eta_c, eta_d, H_c, H_d, psi_c, psi_d = dgmm_coeff_selection(eta_c,\
H_c, psi_c, eta_d, H_d, psi_d, L, r_to_keep, k_to_keep)
lambda_bin, lambda_ord, lambda_categ = gllvm_coeff_selection(lambda_bin, lambda_ord,\
lambda_categ, r, r_to_keep)
w_s_c, w_s_d = path_proba_selection(w_s_c, w_s_d, k, k_to_keep, new_Lt)
k = {h: [len(k_to_keep[h][l]) for l in range(L[h])] for h in ['d', 't']}
k['c'] = [len(k_to_keep['c'][l]) for l in range(L['c'] + 1)]
r = {h: [len(r_to_keep[h][l]) for l in range(L[h])] for h in ['d', 't']}
r['c'] = [len(r_to_keep['c'][l]) for l in range(L['c'] + 1)]
k_1L, _, L, bar_L, S_1L = nb_comps_and_layers(k)
r_1L = {'c': r['c'] + r['t'], 'd': r['d'] + r['t'], 't': r['t']}
patience = 0
best_r = deepcopy(r)
best_k = deepcopy(k)
#=======================================================
# Identifiability conditions
#=======================================================
eta_d, H_d, psi_d, AT_d, eta_c, H_c, psi_c, AT_c = network_identifiability(eta_d, \
H_d, psi_d, eta_c, H_c, psi_c, w_s_c, w_s_d, w_s_t, bar_L)
print('New architecture:')
print('k', k)
print('r', r)
print('L', L)
print('S_1L', S_1L)
print("w_s_c", len(w_s_c))
print("w_s_d", len(w_s_d))
M = M_growth(it_num + 1, r_1L, numobs)
prev_lik = deepcopy(new_lik)
print(likelihood)
print('Silhouette score:', new_sil)
it_num = it_num + 1
out = dict(likelihood = likelihood, classes = classes, \
best_r = best_r, best_k = best_k)
if n_clusters == 'multi':
out['z'] = z_tail
else:
out['z'] = z_tail[clustering_layer]
return(out)
#===========================================#
# Running the algorithm
#===========================================#
r = np.array([2, 1])
numobs = len(y)
k = [n_clusters]
seed = 1
init_seed = 2
eps = 1E-05
it = 50
maxstep = 100
prince_init = dim_reduce_init(y, n_clusters, k, r, nj, var_distrib, seed = None,\
use_famd=True)
m, pred = misc(labels_oh, prince_init['classes'], True)
print(m)
print(confusion_matrix(labels_oh, pred))
print(silhouette_score(dm, pred, metric='precomputed'))
'''
init = prince_init
seed = None
y = y_np
perform_selec = False
os.chdir('C:/Users/rfuchs/Documents/GitHub/M1DGMM')
'''
out = M1DGMM(y_np, 'auto', r, k, prince_init, var_distrib, nj, it,\
eps, maxstep, seed, perform_selec = False)
seed = 1
init_seed = 2
eps = 1E-05
it = 10
maxstep = 100
nb_runs = 4
for run_idx in range(nb_runs):
#===========================================#
# MI2AMI initialisation
#===========================================#
init = dim_reduce_init(complete_y, n_clusters, k, r, nj, var_distrib, seed = None,\
use_famd=False)
out = MI2AMI(y, n_clusters, r, k, init, var_distrib, nj, nan_mask,\
nb_pobs, it, eps, maxstep, seed, dm = dm, perform_selec = False)
completed_y = pd.DataFrame(out['completed_y'].round(0),
columns=full_contra.columns)
#===========================================#
# MI2AMI full
#===========================================#
dm2 = gower_matrix(completed_y, cat_features=cat_features)
init2 = dim_reduce_init(completed_y.astype(dtype), n_clusters, k, r, nj, var_distrib, seed = None,\
use_famd=False)
# Running the algorithm
#===========================================#
n_clusters = 2
r = {'c': [nb_cont], 'd': [3], 't': [2, 1]}
k = {'c': [1], 'd': [2], 't': [n_clusters, 1]}
seed = 1
init_seed = 2
eps = 1E-05
it = 15
maxstep = 100
# MCA init
prince_init = dim_reduce_init(y, n_clusters, k, r, nj, var_distrib, seed=None)
out = MDGMM(y_np, n_clusters, r, k, prince_init, var_distrib, nj, it, eps,\
maxstep, seed, perform_selec = False)
m, pred = misc(labels_oh, out['classes'], True)
micro = precision_score(labels_oh, pred, average='micro')
macro = precision_score(labels_oh, pred, average='macro')
print('Silhouette', silhouette_score(dm, pred, metric='precomputed'))
print('Micro', micro)
print('Macro', macro)
#===========================================#
# Final plotting
#===========================================#
cat_features = pd.Series(var_distrib).isin(['categorical',
'bernoulli']).to_list()
dtype = {
y.columns[j]: np.str if cat_features[j] else np.float64
for j in range(p)
}
y = y.astype(dtype, copy=True)
dm = gower_matrix(y, cat_features=cat_features)
#===========================================#
# Running the M1DGMM
#===========================================#
for i in range(nb_trials):
prince_init = dim_reduce_init(y, nb_clusters_start, k, r, nj, var_distrib, seed = None,\
use_famd=True)
out = M1DGMM(y_np, 'auto', r, k, prince_init, var_distrib, nj, it,\
eps, maxstep, seed, perform_selec = True, dm = dm)
mdgmm_res = mdgmm_res.append({'dataset': dataset, 'it_id': i + 1,\
'n_clusters_found': len(set(out['classes'])),\
'r': r, 'k':k,\
'best_r': out['best_r'], 'best_k':out['best_k']},\
ignore_index=True)
#===========================================#
# Running the hierarchical clustering
#===========================================#
hierarch_res = pd.DataFrame(
# Running the algorithm
#===========================================#
r = [3, 2, 1]
numobs = len(y)
k = [n_clusters, 2]
seed = 1
init_seed = 2
eps = 1E-05
it = 15
maxstep = 100
# MCA init
prince_init = dim_reduce_init(y, n_clusters, k, r, nj, var_distrib, seed=None)
m, pred = misc(labels_oh, prince_init['classes'], True)
print(m)
print(confusion_matrix(labels_oh, pred))
'''
init = prince_init
y = y_np
seed = None
'''
out = DDGMM(y_np, n_clusters, r, k, prince_init, var_distrib, nj, it,\
eps, maxstep, seed, perform_selec = False)
m, pred = misc(labels_oh, out['classes'], True)
print(m)
print(confusion_matrix(labels_oh, pred))
