import sys
sys.path.append("..")
import utility.Util as Util
credit_data = Util.open_file("../data/credit_data.csv")
# create lists of types of features
numerical = ["Duration", 'InstallmentRatePecnt', 'PresentResidenceTime', 'Age']
# categorical = ["CheckingAcctStat", "CreditHistory", "Purpose", 'Savings', 'Employment', 'Property', 'Telephone']
target = ['CreditStatus']
positive_class, negative_class = Util.decompose_classes(
credit_data, 'CreditStatus')
# get numerical, categorical and labels for each class
positive_numerical, positive_target = Util.pre_process_data(
positive_class, numerical, target)
negative_numerical, negative_target = Util.pre_process_data(
negative_class, numerical, target)
# cluster data and get cluster labels
positive_cluster = Util.cluster_data(
positive_numerical) # .join(positive_categorical)
negative_cluster = Util.cluster_data(negative_numerical)
negative_cluster = np.array([x + 3 for x in negative_cluster])
# give the new cluster label column a name
positive_cluster_labels = pd.DataFrame(positive_cluster,
columns=['Cluster Label'])
negative_cluster_labels = pd.DataFrame(negative_cluster,
columns=['Cluster Label'])
import tensorflow as tf
import numpy as np
import sys
sys.path.append("..")
import utility.Util as Util
credit_data = Util.open_file("../data/credit_data.csv")
# create lists of types of features
numerical = ["Duration", 'InstallmentRatePecnt', 'PresentResidenceTime', 'Age']
target = ['CreditStatus']
credit_data = credit_data.sample(frac=1).reset_index(drop=True)
train_x, train_y = Util.pre_process_data(credit_data, numerical, target)
# dividing the dataset into training and test sets
x_train, y_train, x_test, y_test = Util.split_data(0.8, train_x, train_y)
n_hidden_1 = 8
n_input = train_x.shape[1]
n_classes = train_y.shape[1]
weights = {
'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),
'out': tf.Variable(tf.random_normal([n_hidden_1, n_classes]))
}
biases = {
'b1': tf.Variable(tf.random_normal([n_hidden_1])),
'out': tf.Variable(tf.random_normal([n_classes]))
}