def main():
# load and split data
start = time()
# check if file exists
data_file = "data/creditcard.csv"
if not isfile(data_file):
try:
# download the data set
# Note: it is around 180MB
data_url = "https://github.com/nsethi31/Kaggle-Data-Credit-Card-Fraud-Detection/raw/master/creditcard.csv"
urlretrieve(data_url, data_file)
print("download data file to %s" % data_file)
except Error:
print("can't access or download the data set")
print("please try to download it manually and put into data/creditcard.csv")
sys.exit()
dataset, target = load_dataset(data_file)
print("Loaded data in %.4f seconds" % (time() - start))
start = time()
x_train, x_test, y_train, y_test = train_test_split(
dataset, target, test_size=.2, random_state=42)
print("Training set size:%d, Testing set size: %d" %
(len(x_train), len(x_test)))
print("Prepared data for models in %.4f seconds" % (time() - start))
scores = []
models = {"GNB": GaussianNB(),
"DT": DecisionTreeClassifier(max_depth=5),
"MLP": MLPClassifier(alpha=1.0),
#"LSVC": SVC(kernel="linear", C=0.025), # very slow as there is too much data
"NN": KNeighborsClassifier(),
"RF": RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),
"ABC": AdaBoostClassifier(),
"SGD": SGDClassifier(),
}
names = []
for k, model in models.items():
print("Running %s" % k)
start = time()
fitted_model = model.fit(x_train, y_train)
print("Training time: %.4f seconds" % (time() - start))
start = time()
y_predicted = fitted_model.predict(x_test)
print("Testing time: %.4f seconds" % (time() - start))
scores.append(display(y_test, y_predicted,
save="figures/" + k + ".png"))
names.append(k)
# scatter plot scores of all the models
plot_scores(scores, names, save="figures/scores.png")
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