min_max_scaler = preprocessing.MinMaxScaler(feature_range=(-1, 1)) # Normalizing data between -1 and 1
X = pd.DataFrame(min_max_scaler.fit_transform(X))
y = data.iloc[:,-1].copy()
y[y == 0] = -1
# Filtering data:
X_new, y_new = remove_noise(X, y)
# Comparing methods:
method = ["nn_clas", "parallel", "extreme_search"]
print("Dataset: {}".format(data_name))
f = open("results.txt", "a")
f.write("Dataset: %s\r\n" % data_name)
for model in method:
y_hat, y_test, result, runtime = chip_clas(X_new, y_new, method = model, kfold = 10)
print(" \n Method: {0} \n Avarege AUC: {1:.4f} \n Std. Deviation {2:.4f} \n Avarege Runtime: {3:.4f} \n".format(model, result.mean()[0], result.std()[0], runtime.mean()[0]))
results = {'Method': model,
'Avarege AUC': result.mean()[0],
'Std deviation': result.std()[0],
'Avarege runtime': runtime.mean()[0]}
json.dump(results, f)
f.write('\n\n')
f.close()
X = data.iloc[:,:-1]
y = data.iloc[:,-1]
'''
data_name = "Banknote Auth."
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/00267/data_banknote_authentication.txt"
data = pd.read_csv(url, header=None)
X = data.iloc[:, :-1]
min_max_scaler = preprocessing.MinMaxScaler(
feature_range=(-1, 1)) # Normalizing data between -1 and 1
X = pd.DataFrame(min_max_scaler.fit_transform(X))
y = data.iloc[:, -1].copy()
y[y == 0] = -1
# Filtering data:
X_new, y_new = remove_noise(X, y)
y_hat, y_test, result, runtime = chip_clas(X_new,
y_new,
method="extreme_search",
kfold=10)
print(
" \n Method: {0} \n Avarege AUC: {1:.4f} \n Std. Deviation {2:.4f} \n Avarege Runtime: {3:.4f} \n"
.format("pseudo_support_edges",
result.mean()[0],
result.std()[0],
runtime.mean()[0]))