## csv files required to run the program ## ############################## path_dataset_matrix = 'bl_data_MCI_v1.csv' path_mask_X1 = 'mask_age_nolab.csv' path_mask_X2 = 'mask_sex_nolab.csv' path_mask_X3 = 'mask_agesex_nolab.csv' path_mask_X4 = 'mask_nosignificance_nolab.csv' path_dataset_affinity_matrix_age = 'affinity_mat_1.csv' path_dataset_affinity_matrix_sex = 'affinity_mat_2.csv' path_dataset_affinity_matrix_agesex = 'affinity_mat_3.csv' labels_path = 'labels_comma.csv' #load the .csv files M_str, nrRows, nrCols = read_tadpole.load_csv_no_header(path_dataset_matrix) mask_age, _, _ = read_tadpole.load_csv_no_header(path_mask_X1) mask_sex, _, _ = read_tadpole.load_csv_no_header(path_mask_X2) mask_agesex, _, _ = read_tadpole.load_csv_no_header(path_mask_X3) mask_nosignificance, _, _ = read_tadpole.load_csv_no_header(path_mask_X4) A1, _, _ = read_tadpole.load_csv_no_header(path_dataset_affinity_matrix_age) A2, _, _ = read_tadpole.load_csv_no_header(path_dataset_affinity_matrix_sex) A3, _, _ = read_tadpole.load_csv_no_header(path_dataset_affinity_matrix_agesex) A_age = preprocessing_dataset.str_to_float(A1) A_sex = preprocessing_dataset.str_to_float(A2) A_sexage = preprocessing_dataset.str_to_float(A3) labels, _, _ = read_tadpole.load_csv_no_header(labels_path) labels = preprocessing_dataset.str_to_float(labels)
#import rbfopt #uncomment when doing optimization of hyperparameters
from preprocessing import sparse_to_tuple, load_data_monti_tadpole
from model import MG_GAE
from utils import construct_feed_dict
import read_tadpole
import preprocessing_dataset
VERBOSE = True
path_dataset_matrix = "synthetic_data_noteasy.csv"
labels_path = "labels_synthetic_data_noteasy.csv"
#load the .csv files
M_str, nrRows, nrCols = read_tadpole.load_csv_no_header(path_dataset_matrix)
labels, _, _ = read_tadpole.load_csv_no_header(labels_path)
#parameters/preprocessing step that do not change during the running
labels = preprocessing_dataset.str_to_float(labels)
M_init = np.concatenate((M_str, labels), axis=1)
M_support = np.concatenate((M_str, labels), axis=1)
M = preprocessing_dataset.normalization_gae(M_init)
M_sup = preprocessing_dataset.normalization_for_supportreal(M_support)
def run(seed, gamma, beta, hidden, lr, NB_EPOCH=300):
"""
Main function. Run the architecture for the initialization defined by seed and by the hyperparameters gamma, beta, hidden, lr
def run(seed, gamma, beta, hidden, lr, NB_EPOCH=300):
"""
Main function. Run the architecture for the initialization defined by seed and by the hyperparameters gamma, beta, hidden, lr
Inputs:
seed : seed to defined the initialization of the training/testing/validation split,
gamma, beta, hidden, lr: hyperparameters of the architecture
NB_EPOCH: number of runs to do of the same architecture with different weight initializations. Default: 1000
Outputs:
auc_test, auc_train, auc_val: AUC on the test, train and validation sets
"""
tf.reset_default_graph()
training_set_mask, testing_set_mask, idx_training, idx_testing = preprocessing_dataset.split_train_test(
0.8, M_str, seed, labels)
#create a training and test mask on the data
Otraining = preprocessing_dataset.load_mask(training_set_mask, M_str,
nrRows, nrCols)
Otest = preprocessing_dataset.load_mask(testing_set_mask, M_str, nrRows,
nrCols)
new_labels_train = np.copy(labels)
new_labels_train[idx_testing] = -1
#split train set into 4 parts to create a validation set
training_set_mask, validation_set_mask, idx_training, idx_validation = preprocessing_dataset.split_train_validation_4(
3, M_str, seed, new_labels_train)
Otraining = preprocessing_dataset.load_mask(training_set_mask, M_str,
nrRows, nrCols)
Ovalidation = preprocessing_dataset.load_mask(validation_set_mask, M_str,
nrRows, nrCols)
Otraining = np.concatenate((Otraining, training_set_mask), axis=1)
Ocol = np.zeros((Otest.shape[0], 1))
Otest_support = np.concatenate((Otest, Ocol), axis=1)
Ovalidation_support = np.concatenate((Ovalidation, Ocol), axis=1)
Osupport_t = Otraining + Otest_support + Ovalidation_support
Ovalidation = np.concatenate((Ovalidation, validation_set_mask), axis=1)
Otest = np.concatenate((Otest, testing_set_mask), axis=1)
u_features, v_features, train_labels, train_u_indices, train_v_indices, val_labels, val_u_indices, val_v_indices, test_labels, test_u_indices, test_v_indices = load_data_monti_tadpole(
M, Otraining, Otest, Ovalidation)
m, n = M.shape
# global normalization
support = []
support_t = []
path_support_women = "women_synth_noteasy.csv"
women_support, _, _ = read_tadpole.load_csv_no_header(path_support_women)
women_support = preprocessing_dataset.str_to_float(women_support)
women_support = women_support * M_sup
women_support = sp.csr_matrix(women_support, dtype=np.float32)
support.append(women_support)
support_t.append(women_support.T)
path_support_men = "men_synth_noteasy.csv"
men_support, _, _ = read_tadpole.load_csv_no_header(path_support_men)
men_support = preprocessing_dataset.str_to_float(men_support)
men_support = men_support * M_sup
men_support = sp.csr_matrix(men_support, dtype=np.float32)
support.append(men_support)
support_t.append(men_support.T)
path_support_women_84 = "age_84_92_women_synth_noteasy.csv"
women_84_support, _, _ = read_tadpole.load_csv_no_header(
path_support_women_84)
women_84_support = preprocessing_dataset.str_to_float(women_84_support)
women_84_support = women_84_support * M_sup
women_84_support = sp.csr_matrix(women_84_support, dtype=np.float32)
support.append(women_84_support)
support_t.append(women_84_support.T)
path_support_men_84 = "age_84_92_men_synth_noteasy.csv"
men_84_support, _, _ = read_tadpole.load_csv_no_header(path_support_men_84)
men_84_support = preprocessing_dataset.str_to_float(men_84_support)
men_84_support = men_84_support * M_sup
men_84_support = sp.csr_matrix(men_84_support, dtype=np.float32)
support.append(men_84_support)
support_t.append(men_84_support.T)
path_support_84 = "age_84_92_synth_noteasy.csv"
age84_support, _, _ = read_tadpole.load_csv_no_header(path_support_84)
age84_support = preprocessing_dataset.str_to_float(age84_support)
age84_support = age84_support * M_sup
age84_support = sp.csr_matrix(age84_support, dtype=np.float32)
support.append(age84_support)
support_t.append(age84_support.T)
path_support_women_79 = "age_79_84_women_synth_noteasy.csv"
women_79_support, _, _ = read_tadpole.load_csv_no_header(
path_support_women_79)
women_79_support = preprocessing_dataset.str_to_float(women_79_support)
women_79_support = women_79_support * M_sup
women_79_support = sp.csr_matrix(women_79_support, dtype=np.float32)
support.append(women_79_support)
support_t.append(women_79_support.T)
path_support_men_79 = "age_79_84_men_synth_noteasy.csv"
men_79_support, _, _ = read_tadpole.load_csv_no_header(path_support_men_79)
men_79_support = preprocessing_dataset.str_to_float(men_79_support)
men_79_support = men_79_support * M_sup
men_79_support = sp.csr_matrix(men_79_support, dtype=np.float32)
support.append(men_79_support)
support_t.append(men_79_support.T)
path_support_79 = "age_79_84_synth_noteasy.csv"
age79_support, _, _ = read_tadpole.load_csv_no_header(path_support_79)
age79_support = preprocessing_dataset.str_to_float(age79_support)
age79_support = age79_support * M_sup
age79_support = sp.csr_matrix(age79_support, dtype=np.float32)
support.append(age79_support)
support_t.append(age79_support.T)
path_support_women_74 = "age_74_79_women_synth_noteasy.csv"
women_74_support, _, _ = read_tadpole.load_csv_no_header(
path_support_women_74)
women_74_support = preprocessing_dataset.str_to_float(women_74_support)
women_74_support = women_74_support * M_sup
women_74_support = sp.csr_matrix(women_74_support, dtype=np.float32)
support.append(women_74_support)
support_t.append(women_74_support.T)
path_support_men_74 = "age_74_79_men_synth_noteasy.csv"
men_74_support, _, _ = read_tadpole.load_csv_no_header(path_support_men_74)
men_74_support = preprocessing_dataset.str_to_float(men_74_support)
men_74_support = men_74_support * M_sup
men_74_support = sp.csr_matrix(men_74_support, dtype=np.float32)
support.append(men_74_support)
support_t.append(men_74_support.T)
path_support_74 = "age_74_79_synth_noteasy.csv"
age74_support, _, _ = read_tadpole.load_csv_no_header(path_support_74)
age74_support = preprocessing_dataset.str_to_float(age74_support)
age74_support = age74_support * M_sup
age74_support = sp.csr_matrix(age74_support, dtype=np.float32)
support.append(age74_support)
support_t.append(age74_support.T)
path_support_women_69 = "age_69_74_women_synth_noteasy.csv"
women_69_support, _, _ = read_tadpole.load_csv_no_header(
path_support_women_69)
women_69_support = preprocessing_dataset.str_to_float(women_69_support)
women_69_support = women_69_support * M_sup
women_69_support = sp.csr_matrix(women_69_support, dtype=np.float32)
support.append(women_69_support)
support_t.append(women_69_support.T)
path_support_men_69 = "age_69_74_men_synth_noteasy.csv"
men_69_support, _, _ = read_tadpole.load_csv_no_header(path_support_men_69)
men_69_support = preprocessing_dataset.str_to_float(men_69_support)
men_69_support = men_69_support * M_sup
men_69_support = sp.csr_matrix(men_69_support, dtype=np.float32)
support.append(men_69_support)
support_t.append(men_69_support.T)
path_support_69 = "age_69_74_synth_noteasy.csv"
age69_support, _, _ = read_tadpole.load_csv_no_header(path_support_69)
age69_support = preprocessing_dataset.str_to_float(age69_support)
age69_support = age69_support * M_sup
age69_support = sp.csr_matrix(age69_support, dtype=np.float32)
support.append(age69_support)
support_t.append(age69_support.T)
path_support_women_64 = "age_64_69_women_synth_noteasy.csv"
women_64_support, _, _ = read_tadpole.load_csv_no_header(
path_support_women_64)
women_64_support = preprocessing_dataset.str_to_float(women_64_support)
women_64_support = women_64_support * M_sup
women_64_support = sp.csr_matrix(women_64_support, dtype=np.float32)
support.append(women_64_support)
support_t.append(women_64_support.T)
path_support_men_64 = "age_64_69_men_synth_noteasy.csv"
men_64_support, _, _ = read_tadpole.load_csv_no_header(path_support_men_64)
men_64_support = preprocessing_dataset.str_to_float(men_64_support)
men_64_support = men_64_support * M_sup
men_64_support = sp.csr_matrix(men_64_support, dtype=np.float32)
support.append(men_64_support)
support_t.append(men_64_support.T)
path_support_64 = "age_64_69_synth_noteasy.csv"
age64_support, _, _ = read_tadpole.load_csv_no_header(path_support_64)
age64_support = preprocessing_dataset.str_to_float(age64_support)
age64_support = age64_support * M_sup
age64_support = sp.csr_matrix(age64_support, dtype=np.float32)
support.append(age64_support)
support_t.append(age64_support.T)
path_support_women_59 = "age_59_64_women_synth_noteasy.csv"
women_59_support, _, _ = read_tadpole.load_csv_no_header(
path_support_women_59)
women_59_support = preprocessing_dataset.str_to_float(women_59_support)
women_59_support = women_59_support * M_sup
women_59_support = sp.csr_matrix(women_59_support, dtype=np.float32)
support.append(women_59_support)
support_t.append(women_59_support.T)
path_support_men_59 = "age_59_64_men_synth_noteasy.csv"
men_59_support, _, _ = read_tadpole.load_csv_no_header(path_support_men_59)
men_59_support = preprocessing_dataset.str_to_float(men_59_support)
men_59_support = men_59_support * M_sup
men_59_support = sp.csr_matrix(men_59_support, dtype=np.float32)
support.append(men_59_support)
support_t.append(men_59_support.T)
path_support_59 = "age_59_64_synth_noteasy.csv"
age59_support, _, _ = read_tadpole.load_csv_no_header(path_support_59)
age59_support = preprocessing_dataset.str_to_float(age59_support)
age59_support = age59_support * M_sup
age59_support = sp.csr_matrix(age59_support, dtype=np.float32)
support.append(age59_support)
support_t.append(age59_support.T)
path_support_women_54 = "age_54_59_women_synth_noteasy.csv"
women_54_support, _, _ = read_tadpole.load_csv_no_header(
path_support_women_54)
women_54_support = preprocessing_dataset.str_to_float(women_54_support)
women_54_support = women_54_support * M_sup
women_54_support = sp.csr_matrix(women_54_support, dtype=np.float32)
support.append(women_54_support)
support_t.append(women_54_support.T)
path_support_men_54 = "age_54_59_men_synth_noteasy.csv"
men_54_support, _, _ = read_tadpole.load_csv_no_header(path_support_men_54)
men_54_support = preprocessing_dataset.str_to_float(men_54_support)
men_54_support = men_54_support * M_sup
men_54_support = sp.csr_matrix(men_54_support, dtype=np.float32)
support.append(men_54_support)
support_t.append(men_54_support.T)
path_support_54 = "age_54_59_synth_noteasy.csv"
age54_support, _, _ = read_tadpole.load_csv_no_header(path_support_54)
age54_support = preprocessing_dataset.str_to_float(age54_support)
age54_support = age54_support * M_sup
age54_support = sp.csr_matrix(age54_support, dtype=np.float32)
support.append(age54_support)
support_t.append(age54_support.T)
num_support = len(support)
mask_support_t = []
Osupport_t = sp.csr_matrix(Osupport_t, dtype=np.int)
for i in range(num_support):
mask_support_t.append(Osupport_t.T)
mask_support_t = sp.hstack(mask_support_t, format='csr')
support = sp.hstack(support, format='csr')
support_t = sp.hstack(support_t, format='csr')
# Collect all user and item nodes for test set
test_u = list(set(test_u_indices))
test_v = list(set(test_v_indices))
test_u_dict = {n: i for i, n in enumerate(test_u)}
test_v_dict = {n: i for i, n in enumerate(test_v)}
test_u_indices = np.array([test_u_dict[o] for o in test_u_indices])
test_v_indices = np.array([test_v_dict[o] for o in test_v_indices])
test_support = support[np.array(test_u)]
for i in range(test_support.shape[0]):
for j in range(563, test_support.shape[1], 564):
test_support[i, j] = 0.0
test_support_t = sp.csr_matrix.multiply(support_t, mask_support_t)
# Collect all user and item nodes for validation set
val_u = list(set(val_u_indices))
val_v = list(set(val_v_indices))
val_u_dict = {n: i for i, n in enumerate(val_u)}
val_v_dict = {n: i for i, n in enumerate(val_v)}
val_u_indices = np.array([val_u_dict[o] for o in val_u_indices])
val_v_indices = np.array([val_v_dict[o] for o in val_v_indices])
val_support = support[np.array(val_u)]
for i in range(val_support.shape[0]):
for j in range(563, val_support.shape[1], 564):
val_support[i, j] = 0.0
val_support_t = sp.csr_matrix.multiply(support_t, mask_support_t)
# Collect all user and item nodes for train set
train_u = list(set(train_u_indices))
train_v = list(set(train_v_indices))
train_u_dict = {n: i for i, n in enumerate(train_u)}
train_v_dict = {n: i for i, n in enumerate(train_v)}
train_u_indices = np.array([train_u_dict[o] for o in train_u_indices])
train_v_indices = np.array([train_v_dict[o] for o in train_v_indices])
train_support = support[np.array(train_u)]
train_support_t = sp.csr_matrix.multiply(support_t, mask_support_t)
placeholders = {
'u_features':
tf.sparse_placeholder(tf.float32,
shape=np.array(u_features.shape,
dtype=np.int64)),
'v_features':
tf.sparse_placeholder(tf.float32,
shape=np.array(v_features.shape,
dtype=np.int64)),
'u_features_nonzero':
tf.placeholder(tf.int32, shape=()),
'v_features_nonzero':
tf.placeholder(tf.int32, shape=()),
'labels':
tf.placeholder(tf.float32, shape=(None, )),
'indices_labels':
tf.placeholder(tf.int32, shape=(None, )),
'user_indices':
tf.placeholder(tf.int32, shape=(None, )),
'item_indices':
tf.placeholder(tf.int32, shape=(None, )),
'dropout':
tf.placeholder_with_default(0., shape=()),
'weight_decay':
tf.placeholder_with_default(0., shape=()),
'support':
tf.sparse_placeholder(tf.float32, shape=(None, None)),
'support_t':
tf.sparse_placeholder(tf.float32, shape=(None, None)),
}
div = hidden[0] // num_support
if hidden[0] % num_support != 0:
print(
"""\nWARNING: HIDDEN[0] (=%d) of stack layer is adjusted to %d such that
it can be evenly split in %d splits.\n""" %
(hidden[0], num_support * div, num_support))
hidden[0] = num_support * div
# create model
model = MG_GAE(placeholders,
input_dim=u_features.shape[1],
num_support=num_support,
hidden=hidden,
num_users=m,
num_items=n,
learning_rate=lr,
gamma=gamma,
beta=beta,
logging=True)
# Convert sparse placeholders to tuples to construct feed_dict
test_support = sparse_to_tuple(test_support)
test_support_t = sparse_to_tuple(test_support_t)
val_support = sparse_to_tuple(val_support)
val_support_t = sparse_to_tuple(val_support_t)
train_support = sparse_to_tuple(train_support)
train_support_t = sparse_to_tuple(train_support_t)
u_features = sparse_to_tuple(u_features)
v_features = sparse_to_tuple(v_features)
assert u_features[2][1] == v_features[2][
1], 'Number of features of users and items must be the same!'
num_features = u_features[2][1]
u_features_nonzero = u_features[1].shape[0]
v_features_nonzero = v_features[1].shape[0]
indices_labels = [563] * train_labels.shape[0]
indices_labels_val = [563] * val_labels.shape[0]
indices_labels_test = [563] * test_labels.shape[0]
# Feed_dicts for validation and test set stay constant over different update steps
train_feed_dict = construct_feed_dict(placeholders, u_features, v_features,
u_features_nonzero,
v_features_nonzero, train_support,
train_support_t, train_labels,
indices_labels, train_u_indices,
train_v_indices, 0.)
# No dropout for validation and test runs
val_feed_dict = construct_feed_dict(placeholders, u_features, v_features,
u_features_nonzero, v_features_nonzero,
val_support, val_support_t, val_labels,
indices_labels_val, val_u_indices,
val_v_indices, 0.)
test_feed_dict = construct_feed_dict(placeholders, u_features, v_features,
u_features_nonzero,
v_features_nonzero, test_support,
test_support_t, test_labels,
indices_labels_test, test_u_indices,
test_v_indices, 0.)
# Collect all variables to be logged into summary
merged_summary = tf.summary.merge_all()
sess = tf.Session()
sess.run(tf.global_variables_initializer())
auc_train = []
auc_test = []
auc_val = []
test_pred = []
for epoch in range(NB_EPOCH):
t = time.time()
# Run single weight update
outs = sess.run([
model.training_op, model.loss, model.indices, model.labels,
model.outputs, model.labels_class, model.classification,
model.inputs, model.gcn_u, model.gcn_v, model.loss_frob,
model.binary_entropy, model.u_inputs, model.v_inputs, model.weight,
model.input_u, model.input_v, model.u_indices, model.v_indices
],
feed_dict=train_feed_dict)
train_avg_loss = outs[1]
label_train = outs[5]
output_train = outs[6]
fpr_train, tpr_train, thresholds_train = roc_curve(
label_train, output_train, pos_label=label_train.max())
roc_auc_train = auc(fpr_train, tpr_train)
auc_train.append(roc_auc_train)
val_avg_loss, val_classification, val_labels_corres = sess.run(
[model.loss, model.classification, model.labels_class],
feed_dict=val_feed_dict) #test_feed_dict)#
fpr_val, tpr_val, thresholds_train = roc_curve(
val_labels_corres, val_classification, pos_label=label_train.max())
roc_auc_val = auc(fpr_val, tpr_val)
auc_val.append(roc_auc_val)
test_avg_loss, test_classification, test_labels_corres = sess.run(
[model.loss, model.classification, model.labels_class],
feed_dict=test_feed_dict)
fpr_test, tpr_test, thresholds_test = roc_curve(
test_labels_corres,
test_classification,
pos_label=label_train.max())
roc_auc_test = auc(fpr_test, tpr_test)
auc_test.append(roc_auc_test)
test_pred.append(test_classification)
if VERBOSE:
print("[*] Epoch:", '%04d' % (epoch + 1), "train_loss=",
"{:.5f}".format(train_avg_loss), "train_auc=",
"{:.5f}".format(roc_auc_train), "val_loss=",
"{:.5f}".format(val_avg_loss), "val_auc=",
"{:.5f}".format(roc_auc_val), "\t\ttime=",
"{:.5f}".format(time.time() - t))
print('test auc = ', roc_auc_test)
sess.close()
return auc_test, auc_train, auc_val
# Juliette Valenchon 04/2019
import read_tadpole
import numpy as np
import preprocessing_dataset
path_dataset_matrix = 'stat_results_v1_notext.csv' #matrix to complete
stat, nrRows, nrCols = read_tadpole.load_csv_no_header(path_dataset_matrix)
res_stat = stat.astype(np.float64)
path_dataset_matrix = 'bl_data_MCI_v1.csv'
M_str, nrRows, nrCols = read_tadpole.load_csv_no_header(path_dataset_matrix)
M_init = preprocessing_dataset.normalization(M_str)
def remove_column(column_remove, M):
"""
Function to remove the column of M which are indexed by an indice in column_remove
Input:
column_remove: array with indices of the column to remove
M: matrix with we want to remove the columns that are indexed by an indice in column_remove
Output:
M: matrix where we removed the columns
"""
column_remove.sort(reverse=True)
for i in range(len(column_remove)):
M = np.hstack((M[:, :column_remove[i]], M[:, column_remove[i] + 1:]))
return M
if attributes[k][l] == attributes[j][l]:
adj[k, j] += 1
adj[j, k] += 1
else:
print(
"we only use two attributes here to built the adjacency matrix "
)
return adj
if __name__ == '__main__':
age_file_path = 'age.csv'
gender_file_path = 'gender.csv'
age_column, nrRowsa, nrColsa = read_tadpole.load_csv_no_header(
age_file_path)
gender_column, nrRowsg, nrColsg = read_tadpole.load_csv_no_header(
gender_file_path)
"""meta_data_graph = np.concatenate((age_column, gender_column), axis = 1)
a3=adjacency_agesex(meta_data_graph)
a2=adjacency_sex(gender_column)
a1=adjacency_age(age_column)
affinity_matrix = adjacency_age_sex_agesex(meta_data_graph)
np.savetxt('affinity_mat_parisot_v1.csv', affinity_matrix, fmt='%f', delimiter=',')
np.savetxt('affinity_mat_1.csv', a1, fmt='%f', delimiter=',')
np.savetxt('affinity_mat_2.csv', a2, fmt='%f', delimiter=',')
np.savetxt('affinity_mat_3.csv', a3, fmt='%f', delimiter=',')"""
meta_data_graph = np.concatenate((age_column, gender_column), axis=1)
adja = adjacency_age_sex_agesex(meta_data_graph)
import csv from preprocessing import sparse_to_tuple, load_data_monti_tadpole from model import MG_GAE from utils import construct_feed_dict import read_tadpole import preprocessing_dataset VERBOSE = True path_dataset_matrix ="synthetic_data_noteasy.csv" labels_path = "labels_synthetic_data_noteasy.csv" #load the .csv files M_str, nrRows, nrCols = read_tadpole.load_csv_no_header(path_dataset_matrix) labels, _, _ = read_tadpole.load_csv_no_header(labels_path) gender_file_path = 'sexs_synthetic_data_noteasy.csv' sex, nrRowsg, nrColsg = read_tadpole.load_csv_no_header(gender_file_path) gender_column=sex.reshape(-1) age_file_path = 'ages_synthetic_data_noteasy.csv' age_column, nrRowsa, nrColsa = preprocessing_dataset.load_csv_no_header_float(age_file_path) age_column=age_column.reshape(-1) labels = preprocessing_dataset.str_to_float(labels) labels_save=labels.reshape(-1) M_init= np.concatenate((M_str, labels), axis=1) M_support = np.concatenate((M_str, labels), axis=1)
attribute_mat[j][i]=0
for j in range(len(attribute)):
attribute_mat[j][-1]=1
return attribute_mat
sex=[]
SYNTH=False
if __name__ == '__main__':
if SYNTH:
path_feat_dep = 'feat_dependence_synthetic_data_noteasy.csv'
feat_dep, _, _ = read_tadpole.load_csv_no_header(path_feat_dep)
feat_dep=preprocessing_dataset.str_to_float(feat_dep)
age_file_path = 'ages_synthetic_data_noteasy.csv'
gender_file_path = 'sexs_synthetic_data_noteasy.csv'
age_column, nrRowsa, nrColsa = read_tadpole.load_csv_no_header(age_file_path)
gender_column, nrRowsg, nrColsg = read_tadpole.load_csv_no_header(gender_file_path)
age_column=preprocessing_dataset.str_to_float(age_column)
gender_column=preprocessing_dataset.str_to_float(gender_column)
mask_age = mask_synth(feat_dep, age_column, 0)
mask_sex = mask_synth(feat_dep, gender_column, 1)
mask_agesex = mask_synth(feat_dep, age_column, 2)
np.savetxt('mask_age_synthnoteasy.csv', mask_age, fmt='%f', delimiter=',')
np.savetxt('mask_sex_synthnoteasy.csv', mask_sex, fmt='%f', delimiter=',')
font = {'family': 'Times New Roman', 'weight': 'medium', 'size': 20}
import matplotlib
matplotlib.rc('font', **font)
import numpy as np
from pylab import plot, show, savefig, xlim, figure, \
hold, ylim, legend, boxplot, setp, axes
import pandas as pd
import seaborn
import read_tadpole
import preprocessing_dataset
path_dataset_matrix = 'bl_data_MCI_v1.csv' #matrix to complete
M_str, nrRows, nrCols = read_tadpole.load_csv_no_header(path_dataset_matrix)
path_dataset_header = 'header.csv' #labels of the different columns of features of M_str
header_str_col, _, _ = read_tadpole.load_csv_no_header(path_dataset_header)
gender_file_path = 'gender.csv'
gender_column, nrRowsg, nrColsg = read_tadpole.load_csv_no_header(
gender_file_path)
age_file_path = 'age.csv'
age_column, nrRowsa, nrColsa = read_tadpole.load_csv_no_header(age_file_path)
M_str = preprocessing_dataset.normalization_gae(M_str)
#create a matrix X with the three covariates AGE, SEX, AGE&SEX
age = age_column.astype(np.float64)
