def apply_aec(data_type, with_noise): # auto-encode cluster
# Global initialization
kmeans_ms = {} # K-means results
agg_ms = {} # Agglomerative results
gmm_ms = {} # Gaussian Mixture Model
GT_ms = {} # Ground Truth
if setting_ != 'all':
for setting, repeats in DATA.items():
if str(setting) == setting_:
print("setting:", setting, )
kmeans_ms[setting] = {}
agg_ms[setting] = {}
gmm_ms[setting] = {}
GT_ms[setting] = {}
for repeat, matrices in repeats.items():
print("repeat:", repeat)
GT = matrices['GT']
Y = matrices['Y'].astype("float32")
P = matrices['P'].astype("float32")
Yn = matrices['Yn']
if len(Yn) != 0:
Yn = Yn.astype('float32')
N, V = Y.shape
# Quantitative case
if type_of_data == 'Q' or name.split('(')[-1] == 'r':
_, _, Yz, _, Yrng, _, = ds.preprocess_Y(Yin=Y, data_type='Q')
if with_noise == 1:
Yn, _, Ynz, _, Ynrng, _, = ds.preprocess_Y(Yin=Yn, data_type='Q')
# Because there is no Yn in the case of categorical features.
if type_of_data == 'C':
enc = OneHotEncoder(sparse=False, categories='auto')
Y_oneHot = enc.fit_transform(Y) # .astype("float32") # oneHot encoding
# for WITHOUT follow-up rescale Y_oneHot and for WITH follow-up
# Y_oneHot should be replaced with Y
Y, _, Yz, _, Yrng, _, = ds.preprocess_Y(Yin=Y_oneHot, data_type='C')
if type_of_data == 'M':
Vq = int(np.ceil(V / 2)) # number of quantitative features -- Y[:, :Vq]
Vc = int(np.floor(V / 2)) # number of categorical features -- Y[:, Vq:]
Y_q, _, Yz_q, _, Yrng_q, _, = ds.preprocess_Y(Yin=Y[:, :Vq], data_type='Q')
enc = OneHotEncoder(sparse=False, categories='auto',)
Y_oneHot = enc.fit_transform(Y[:, Vq:]) # oneHot encoding
# for WITHOUT follow-up rescale Y_oneHot and for WITH follow-up
# Y_oneHot should be replaced with Y
Y_c, _, Yz_c, _, Yrng_c, _, = ds.preprocess_Y(Yin=Y_oneHot, data_type='C')
Y = np.concatenate([Y[:, :Vq], Y_oneHot], axis=1)
Yrng = np.concatenate([Yrng_q, Yrng_c], axis=1)
Yz = np.concatenate([Yz_q, Yz_c], axis=1)
if with_noise == 1:
Vq = int(np.ceil(V / 2)) # number of quantitative features -- Y[:, :Vq]
Vc = int(np.floor(V / 2)) # number of categorical features -- Y[:, Vq:]
Vqn = (Vq + Vc) # the column index of which noise model1 starts
_, _, Ynz_q, _, Ynrng_q, _, = ds.preprocess_Y(Yin=Yn[:, :Vq], data_type='Q')
enc = OneHotEncoder(sparse=False, categories='auto',)
Yn_oneHot = enc.fit_transform(Yn[:, Vq:Vqn]) # oneHot encoding
# for WITHOUT follow-up rescale Yn_oneHot and for WITH
# follow-up Yn_oneHot should be replaced with Y
Yn_c, _, Ynz_c, _, Ynrng_c, _, = ds.preprocess_Y(Yin=Yn_oneHot, data_type='C')
Y_ = np.concatenate([Yn[:, :Vq], Yn_c], axis=1)
Yrng = np.concatenate([Ynrng_q, Ynrng_c], axis=1)
Yz = np.concatenate([Ynz_q, Ynz_c], axis=1)
_, _, Ynz_, _, Ynrng_, _, = ds.preprocess_Y(Yin=Yn[:, Vqn:], data_type='Q')
Yn_ = np.concatenate([Y_, Yn[:, Vqn:]], axis=1)
Ynrng = np.concatenate([Yrng, Ynrng_], axis=1)
Ynz = np.concatenate([Yz, Ynz_], axis=1)
P, _, _, Pu, _, _, Pm, _, _, Pl, _, _ = ds.preprocess_P(P=P)
# Pre-processing - Without Noise
if data_type == "NP".lower() and with_noise == 0:
print("NP")
kmeans_labels, agg_labels, gmm_labels, y_test = run_cluster_latents(
Y, P, GT, n_epochs, latent_dim_ratio=latent_dim_ratio,
repeat=repeat, name=data_name, setting=setting)
elif data_type == "z-u".lower() and with_noise == 0:
print("z-u")
kmeans_labels, agg_labels, gmm_labels, y_test = run_cluster_latents(
Y=Yz, P=Pu, GT=GT, n_epochs=n_epochs, latent_dim_ratio=latent_dim_ratio,
repeat=repeat, name=data_name, setting=setting)
elif data_type == "z-m".lower() and with_noise == 0:
kmeans_labels, agg_labels, gmm_labels, y_test = run_cluster_latents(
Y=Yz, P=Pm, GT=GT, n_epochs=n_epochs, latent_dim_ratio=latent_dim_ratio,
repeat=repeat, name=data_name, setting=setting)
elif data_type == "z-l".lower() and with_noise == 0:
kmeans_labels, agg_labels, gmm_labels, y_test = run_cluster_latents(
Y=Yz, P=Pl, GT=GT, n_epochs=n_epochs, latent_dim_ratio=latent_dim_ratio,
repeat=repeat, name=data_name, setting=setting)
elif data_type == "rng-u".lower() and with_noise == 0:
kmeans_labels, agg_labels, gmm_labels, y_test = run_cluster_latents(
Y=Yrng, P=Pu, GT=GT, n_epochs=n_epochs,
latent_dim_ratio=latent_dim_ratio,
repeat=repeat, name=data_name,
setting=setting)
elif data_type == "rng-m".lower() and with_noise == 0:
kmeans_labels, agg_labels, gmm_labels, y_test = run_cluster_latents(
Y=Yrng, P=Pm, GT=GT, n_epochs=n_epochs, latent_dim_ratio=latent_dim_ratio,
repeat=repeat, name=data_name, setting=setting)
elif data_type == "rng-l".lower() and with_noise == 0:
kmeans_labels, agg_labels, gmm_labels, y_test = run_cluster_latents(
Y=Yrng, P=Pl, GT=GT, n_epochs=n_epochs, latent_dim_ratio=latent_dim_ratio,
repeat=repeat, name=data_name, setting=setting)
# Pre-processing - With Noise
if data_type == "NP".lower() and with_noise == 1:
kmeans_labels, agg_labels, gmm_labels, y_test = run_cluster_latents(
Y=Yn, P=P, GT=GT, n_epochs=n_epochs, latent_dim_ratio=latent_dim_ratio,
repeat=repeat, name=data_name, setting=setting)
elif data_type == "z-u".lower() and with_noise == 1:
kmeans_labels, agg_labels, gmm_labels, y_test = run_cluster_latents(
Y=Ynz, P=Pu, GT=GT, n_epochs=n_epochs, latent_dim_ratio=latent_dim_ratio,
repeat=repeat, name=data_name, setting=setting)
elif data_type == "z-m".lower() and with_noise == 1:
kmeans_labels, agg_labels, gmm_labels, y_test = run_cluster_latents(
Y=Ynz, P=Pm, GT=GT, n_epochs=n_epochs,
latent_dim_ratio=latent_dim_ratio, repeat=repeat,
name=data_name, setting=setting)
elif data_type == "z-l".lower() and with_noise == 1:
kmeans_labels, agg_labels, gmm_labels, y_test = run_cluster_latents(
Y=Ynz, P=Pl, GT=GT, n_epochs=n_epochs, latent_dim_ratio=latent_dim_ratio,
repeat=repeat, name=data_name, setting=setting)
elif data_type == "rng-u".lower() and with_noise == 1:
kmeans_labels, agg_labels, gmm_labels, y_test = run_cluster_latents(
Y=Ynrng, P=Pu, GT=GT, n_epochs=n_epochs, latent_dim_ratio=latent_dim_ratio,
repeat=repeat, name=data_name, setting=setting)
elif data_type == "rng-m".lower() and with_noise == 1:
kmeans_labels, agg_labels, gmm_labels, y_test = run_cluster_latents(
Y=Ynrng, P=Pm, GT=GT, n_epochs=n_epochs,
latent_dim_ratio=latent_dim_ratio, repeat=repeat,
name=data_name, setting=setting)
elif data_type == "rng-l".lower() and with_noise == 1:
kmeans_labels, agg_labels, gmm_labels, y_test = run_cluster_latents(
Y=Ynrng, P=Pl, GT=GT, n_epochs=n_epochs, latent_dim_ratio=latent_dim_ratio,
repeat=repeat, name=data_name, setting=setting)
kmeans_ms[setting][repeat] = kmeans_labels
agg_ms[setting][repeat] = agg_labels
gmm_ms[setting][repeat] = gmm_labels
GT_ms[setting][repeat] = y_test
print("Algorithm is applied on the" + setting_ + "data set!")
if setting_ == 'all':
for setting, repeats in DATA.items():
print("setting:", setting, )
kmeans_ms[setting] = {}
agg_ms[setting] = {}
gmm_ms[setting] = {}
GT_ms[setting] = {}
for repeat, matrices in repeats.items():
print("repeat:", repeat)
GT = matrices['GT']
Y = matrices['Y'].astype('float32')
P = matrices['P'].astype('float32')
Yn = matrices['Yn']
if len(Yn) != 0:
Yn = Yn.astype('float32')
N, V = Y.shape
# Quantitative case
if type_of_data == 'Q' or name.split('(')[-1] == 'r':
_, _, Yz, _, Yrng, _, = ds.preprocess_Y(Yin=Y, data_type='Q')
if with_noise == 1:
Yn, _, Ynz, _, Ynrng, _, = ds.preprocess_Y(Yin=Yn, data_type='Q')
# Because there is no Yn in the case of categorical features.
if type_of_data == 'C':
enc = OneHotEncoder() # categories='auto')
Y = enc.fit_transform(Y) # oneHot encoding
Y = Y.toarray()
# Boris's Theory
Y, _, Yz, _, Yrng, _, = ds.preprocess_Y(Yin=Y, data_type='C')
if type_of_data == 'M':
Vq = int(np.ceil(V / 2)) # number of quantitative features -- Y[:, :Vq]
Vc = int(np.floor(V / 2)) # number of categorical features -- Y[:, Vq:]
Y_, _, Yz_, _, Yrng_, _, = ds.preprocess_Y(Yin=Y[:, :Vq], data_type='M')
enc = OneHotEncoder(sparse=False, ) # categories='auto', )
Y_oneHot = enc.fit_transform(Y[:, Vq:]) # oneHot encoding
Y = np.concatenate([Y_oneHot, Y[:, :Vq]], axis=1)
Yrng = np.concatenate([Y_oneHot, Yrng_], axis=1)
Yz = np.concatenate([Y_oneHot, Yz_], axis=1)
if with_noise == 1:
Vq = int(np.ceil(V / 2)) # number of quantitative features -- Y[:, :Vq]
Vc = int(np.floor(V / 2)) # number of categorical features -- Y[:, Vq:]
Vqn = (Vq + Vc) # the column index of which noise model1 starts
_, _, Yz_, _, Yrng_, _, = ds.preprocess_Y(Yin=Yn[:, :Vq], data_type='M')
enc = OneHotEncoder(sparse=False, ) # categories='auto',)
Yn_oneHot = enc.fit_transform(Yn[:, Vq:Vqn]) # oneHot encoding
Y_ = np.concatenate([Yn_oneHot, Yn[:, :Vq]], axis=1)
Yrng = np.concatenate([Yn_oneHot, Yrng_], axis=1)
Yz = np.concatenate([Yn_oneHot, Yz_], axis=1)
_, _, Ynz_, _, Ynrng_, _, = ds.preprocess_Y(Yin=Yn[:, Vqn:], data_type='M')
Yn_ = np.concatenate([Y_, Yn[:, Vqn:]], axis=1)
Ynrng = np.concatenate([Yrng, Ynrng_], axis=1)
Ynz = np.concatenate([Yz, Ynz_], axis=1)
P, _, _, Pu, _, _, Pm, _, _, Pl, _, _ = ds.preprocess_P(P=P)
# Pre-processing - Without Noise
if data_type == "NP".lower() and with_noise == 0:
kmeans_labels, agg_labels, gmm_labels, y_test = run_cluster_latents(
Y=Y, P=P, GT=GT, n_epochs=n_epochs, latent_dim_ratio=latent_dim_ratio,
repeat=repeat, name=data_name, setting=setting)
elif data_type == "z-u".lower() and with_noise == 0:
kmeans_labels, agg_labels, gmm_labels, y_test = run_cluster_latents(
Y=Yz, P=Pu, GT=GT, n_epochs=n_epochs, latent_dim_ratio=latent_dim_ratio,
repeat=repeat, name=data_name, setting=setting)
elif data_type == "z-m".lower() and with_noise == 0:
kmeans_labels, agg_labels, gmm_labels, y_test = run_cluster_latents(
Y=Yz, P=Pm, GT=GT, n_epochs=n_epochs, latent_dim_ratio=latent_dim_ratio,
repeat=repeat, name=data_name, setting=setting)
elif data_type == "z-l".lower() and with_noise == 0:
kmeans_labels, agg_labels, gmm_labels, y_test = run_cluster_latents(
Y=Yz, P=Pl, GT=GT, n_epochs=n_epochs, latent_dim_ratio=latent_dim_ratio,
repeat=repeat, name=data_name, setting=setting)
elif data_type == "rng-u".lower() and with_noise == 0:
kmeans_labels, agg_labels, gmm_labels, y_test = run_cluster_latents(
Y=Yrng, P=Pu, GT=GT, n_epochs=n_epochs, latent_dim_ratio=latent_dim_ratio,
repeat=repeat, name=data_name, setting=setting)
elif data_type == "rng-m".lower() and with_noise == 0:
kmeans_labels, agg_labels, gmm_labels, y_test = run_cluster_latents(
Y=Yrng, P=Pm, GT=GT, n_epochs=n_epochs, latent_dim_ratio=latent_dim_ratio,
repeat=repeat, name=data_name, setting=setting)
elif data_type == "rng-l".lower() and with_noise == 0:
kmeans_labels, agg_labels, gmm_labels, y_test = run_cluster_latents(
Y=Yrng, P=Pl, GT=GT, n_epochs=n_epochs, latent_dim_ratio=latent_dim_ratio,
repeat=repeat, name=data_name, setting=setting)
# Pre-processing - With Noise
if data_type == "NP".lower() and with_noise == 1:
kmeans_labels, agg_labels, gmm_labels, y_test = run_cluster_latents(
Y=Yn, P=P, GT=GT, n_epochs=n_epochs, latent_dim_ratio=latent_dim_ratio,
repeat=repeat, name=data_name, setting=setting)
elif data_type == "z-u".lower() and with_noise == 1:
kmeans_labels, agg_labels, gmm_labels, y_test = run_cluster_latents(
Y=Ynz, P=Pu, GT=GT, n_epochs=n_epochs, latent_dim_ratio=latent_dim_ratio,
repeat=repeat, name=data_name, setting=setting)
elif data_type == "z-m".lower() and with_noise == 1:
kmeans_labels, agg_labels, gmm_labels, y_test = run_cluster_latents(
Y=Ynz, P=Pm, GT=GT, n_epochs=n_epochs, latent_dim_ratio=latent_dim_ratio,
repeat=repeat, name=data_name, setting=setting)
elif data_type == "z-l".lower() and with_noise == 1:
kmeans_labels, agg_labels, gmm_labels, y_test = run_cluster_latents(
Y=Ynz, P=Pl, GT=GT, n_epochs=n_epochs, latent_dim_ratio=latent_dim_ratio,
repeat=repeat, name=data_name, setting=setting)
elif data_type == "rng-u".lower() and with_noise == 1:
kmeans_labels, agg_labels, gmm_labels, y_test = run_cluster_latents(
Y=Ynrng, P=Pu, GT=GT, n_epochs=n_epochs, latent_dim_ratio=latent_dim_ratio,
repeat=repeat, name=data_name, setting=setting)
elif data_type == "rng-m".lower() and with_noise == 1:
kmeans_labels, agg_labels, gmm_labels, y_test = run_cluster_latents(
Y=Ynrng, P=Pm, GT=GT, n_epochs=n_epochs, latent_dim_ratio=latent_dim_ratio,
repeat=repeat, name=data_name, setting=setting)
elif data_type == "rng-l".lower() and with_noise == 1:
kmeans_labels, agg_labels, gmm_labels, y_test = run_cluster_latents(
Y=Ynrng, P=Pl, GT=GT, n_epochs=n_epochs, latent_dim_ratio=latent_dim_ratio,
repeat=repeat, name=data_name, setting=setting)
kmeans_ms[setting][repeat] = kmeans_labels
agg_ms[setting][repeat] = agg_labels
gmm_ms[setting][repeat] = gmm_labels
GT_ms[setting][repeat] = y_test
print("Algorithm is applied on the entire data set!")
return kmeans_ms, agg_ms, gmm_ms, GT_ms
def apply_the_algorithm(data_type, ): # auto-encode cluster
# Global initialization
alg_ms = {} # the algorithm results
gt_ms = {} # Ground Truth
for setting, repeats in DATA.items():
print(
"setting:",
setting,
)
alg_ms[setting] = {}
gt_ms[setting] = {}
for repeat, matrices in repeats.items():
print("repeat:", repeat)
X_tr = DATA[setting][repeat]['X_tr'].astype('float32')
X_vl = DATA[setting][repeat]['X_vl'].astype('float32')
X_ts = DATA[setting][repeat]['X_ts'].astype('float32')
y_tr = DATA[setting][repeat]['y_tr'].astype('float32')
y_vl = DATA[setting][repeat]['y_vl'].astype('float32')
y_ts = DATA[setting][repeat]['y_ts'].astype('float32')
_, _, Xz_tr, _, Xr_tr, _, = ds.preprocess_Y(Yin=X_tr,
data_type='Q')
_, _, Xz_vl, _, Xr_vl, _, = ds.preprocess_Y(Yin=X_vl,
data_type='Q')
_, _, Xz_ts, _, Xr_ts, _, = ds.preprocess_Y(Yin=X_ts,
data_type='Q')
# Different Pre-processing methods
if data_type == "NP".lower():
print("No Pre-Proc.")
alg_x_test_labels, y_test = run_the_algorithm(
X_train=X_tr,
y_train=y_tr,
X_val=X_vl,
y_val=y_vl,
X_test=X_ts,
y_test=y_ts,
n_epochs=n_epochs,
repeat=repeat,
ds_name=name,
setting=setting)
elif data_type == "z".lower():
print("Z-score")
alg_x_test_labels, y_test = run_the_algorithm(
X_train=Xz_tr,
y_train=y_tr,
X_val=Xz_vl,
y_val=y_vl,
X_test=Xz_ts,
y_test=y_ts,
n_epochs=n_epochs,
repeat=repeat,
ds_name=name,
setting=setting)
elif data_type == "rng".lower():
print("Rng")
alg_x_test_labels, y_test = run_the_algorithm(
X_train=Xr_tr,
y_train=y_tr,
X_val=Xr_vl,
y_val=y_vl,
X_test=Xr_ts,
y_test=y_ts,
n_epochs=n_epochs,
repeat=repeat,
ds_name=name,
setting=setting)
alg_ms[setting][repeat] = alg_x_test_labels
gt_ms[setting][repeat] = y_test
print("Algorithm is applied on the" + str(setting) +
"data set!")
return alg_ms, gt_ms
def apply_aec(data_type, with_noise): # auto-encode cluster
# Global initialization
AE_ms = {} # K-means results
GT_ms = {} # Ground Truth
if setting_ != 'all':
for setting, repeats in DATA.items():
if str(setting) == setting_:
print(
"setting:",
setting,
)
AE_ms[setting] = {}
GT_ms[setting] = {}
for repeat, matrices in repeats.items():
print("repeat:", repeat)
X_tr = DATA[setting][repeat]['X_tr'].astype(
'float32')
X_vl = DATA[setting][repeat]['X_vl'].astype(
'float32')
X_ts = DATA[setting][repeat]['X_ts'].astype(
'float32')
y_tr = DATA[setting][repeat]['y_tr'].astype(
'float32')
y_vl = DATA[setting][repeat]['y_vl'].astype(
'float32')
y_ts = DATA[setting][repeat]['y_ts'].astype(
'float32')
_, _, Xz_tr, _, Xr_tr, _, = ds.preprocess_Y(
Yin=X_tr, data_type='Q')
_, _, Xz_vl, _, Xr_vl, _, = ds.preprocess_Y(
Yin=X_vl, data_type='Q')
_, _, Xz_ts, _, Xr_ts, _, = ds.preprocess_Y(
Yin=X_ts, data_type='Q')
# Different Pre-processing methods
if data_type == "NP".lower() and with_noise == 0:
print("No Pre-Proc.")
AE_X_test_labels, y_test = run_ae(
X_train=X_tr,
y_train=y_tr,
X_val=X_vl,
y_val=y_vl,
X_test=X_ts,
y_test=y_ts,
n_epochs=n_epochs,
latent_dim_ratio=latent_dim_ratio,
repeat=repeat,
name=name,
setting=setting)
elif data_type == "z".lower() and with_noise == 0:
print("Z-score")
AE_X_test_labels, y_test = run_ae(
X_train=Xz_tr,
y_train=y_tr,
X_val=Xz_vl,
y_val=y_vl,
X_test=Xz_ts,
y_test=y_ts,
n_epochs=n_epochs,
latent_dim_ratio=latent_dim_ratio,
repeat=repeat,
name=name,
setting=setting)
elif data_type == "rng".lower(
) and with_noise == 0:
print("Rng")
AE_X_test_labels, y_test = run_ae(
X_train=Xr_tr,
y_train=y_tr,
X_val=Xr_vl,
y_val=y_vl,
X_test=Xr_ts,
y_test=y_ts,
n_epochs=n_epochs,
latent_dim_ratio=latent_dim_ratio,
repeat=repeat,
name=name,
setting=setting)
AE_ms[setting][repeat] = AE_X_test_labels
GT_ms[setting][repeat] = y_test
print("Algorithm is applied on the" + setting_ +
"data set!")
if setting_ == 'all':
for setting, repeats in DATA.items():
print(
"setting:",
setting,
)
AE_ms[setting] = {}
GT_ms[setting] = {}
for repeat, matrices in repeats.items():
print("repeat:", repeat)
X_tr = DATA[setting][repeat]['X_tr'].astype('float32')
X_vl = DATA[setting][repeat]['X_vl'].astype('float32')
X_ts = DATA[setting][repeat]['X_ts'].astype('float32')
y_tr = DATA[setting][repeat]['y_tr'].astype('float32')
y_vl = DATA[setting][repeat]['y_vl'].astype('float32')
y_ts = DATA[setting][repeat]['y_ts'].astype('float32')
_, _, Xz_tr, _, Xr_tr, _, = ds.preprocess_Y(
Yin=X_tr, data_type='Q')
_, _, Xz_vl, _, Xr_vl, _, = ds.preprocess_Y(
Yin=X_vl, data_type='Q')
_, _, Xz_ts, _, Xr_ts, _, = ds.preprocess_Y(
Yin=X_ts, data_type='Q')
# Different Pre-processing methods
if data_type == "NP".lower() and with_noise == 0:
print("No Pre-Proc.")
AE_X_test_labels, y_test = run_ae(
X_train=X_tr,
y_train=y_tr,
X_val=X_vl,
y_val=y_vl,
X_test=X_ts,
y_test=y_ts,
n_epochs=n_epochs,
latent_dim_ratio=latent_dim_ratio,
repeat=repeat,
name=name,
setting=setting)
elif data_type == "z".lower() and with_noise == 0:
print("Z-score")
AE_X_test_labels, y_test = run_ae(
X_train=Xz_tr,
y_train=y_tr,
X_val=Xz_vl,
y_val=y_vl,
X_test=Xz_ts,
y_test=y_ts,
n_epochs=n_epochs,
latent_dim_ratio=latent_dim_ratio,
repeat=repeat,
name=name,
setting=setting)
elif data_type == "rng".lower() and with_noise == 0:
print("Rng")
AE_X_test_labels, y_test = run_ae(
X_train=Xr_tr,
y_train=y_tr,
X_val=Xr_vl,
y_val=y_vl,
X_test=Xr_ts,
y_test=y_ts,
n_epochs=n_epochs,
latent_dim_ratio=latent_dim_ratio,
repeat=repeat,
name=name,
setting=setting)
AE_ms[setting][repeat] = AE_X_test_labels
GT_ms[setting][repeat] = y_test
print("Algorithm is applied on the entire data set!")
return AE_ms, GT_ms
