def calc():
if not os.path.exists(RESULTS_FOLDER_PATH):
os.makedirs(RESULTS_FOLDER_PATH)
logfile = os.path.join(RESULTS_FOLDER_PATH, "log.log")
if os.path.exists(logfile):
os.remove(logfile)
# logging stuff
level = logging.INFO
formats = {
"console": '\u001b[37m %(message)s\033[0m',
"file": '%(message)s'
}
file_handler, console_handler = logging.FileHandler(
logfile, "x"), logging.StreamHandler()
console_handler.setFormatter(logging.Formatter(formats["console"]))
file_handler.setFormatter(logging.Formatter(formats["file"]))
logger = logging.getLogger(__name__)
logger.addHandler(console_handler)
logger.addHandler(file_handler)
logger.setLevel(logging.INFO)
# begin calc
loaded_dataset = load_data(logger)
logger.info("{} {}".format("loaded_dataset shape", loaded_dataset.shape))
# loaded_dataset["Label"] = DATASET_NAME.upper()
logger.info(loaded_dataset.head())
loaded_dataset.info()
dataset = None
logger.info("{} {}".format("Dataset shape BEFORE preparation",
loaded_dataset.shape))
dataset = datasets.prepare_dataset(loaded_dataset,
drop_columns=[
"Flow Bytes/s", "Flow Packets/s",
"Flow ID", "Source IP",
"Destination IP", "Timestamp",
"Fwd Header Length.1"
],
shuffle=True,
dropna_axis=[0, 1])
loaded_dataset = None
logger.info("{} {}".format("Dataset shape AFTER preparation",
dataset.shape))
xTest, yTest = datasets.separate_labels(dataset, encode=True)
dataset = None
logger.info('Scaling dataset')
xTest = datasets.drop_variance(xTest)
# standardScaler = StandardScaler()
# xTestScaled = standardScaler.fit_transform(xTest)
results = []
clf = RandomForestClassifier(random_state=42, n_jobs=1)
param_name = "n_estimators"
param_range = [i**2 for i in range(4, 24, 4)]
logger.info(param_name)
logger.info(param_range)
training_score, test_score = validation_curve(clf,
xTest,
yTest,
param_name=param_name,
param_range=param_range,
scoring="roc_auc",
cv=4,
verbose=1,
n_jobs=-1)
results.append([param_name, param_range, training_score, test_score])
datasets.np_double_save(results,
RESULTS_FOLDER_PATH,
"results",
as_csv=True,
as_npy=True)
param_name = "max_depth"
param_range = [2**i for i in range(1, 8)]
logger.info(param_name)
logger.info(param_range)
training_score, test_score = validation_curve(clf,
xTest,
yTest,
param_name=param_name,
param_range=param_range,
scoring="roc_auc",
cv=4,
verbose=1,
n_jobs=-1)
results.append([param_name, param_range, training_score, test_score])
datasets.np_double_save(results,
RESULTS_FOLDER_PATH,
"results",
as_csv=True,
as_npy=True)
param_name = "min_samples_leaf"
param_range = [2 * i for i in range(1, 21)]
logger.info(param_name)
logger.info(param_range)
training_score, test_score = validation_curve(clf,
xTest,
yTest,
param_name=param_name,
param_range=param_range,
scoring="roc_auc",
cv=4,
verbose=1,
n_jobs=-1)
results.append([param_name, param_range, training_score, test_score])
datasets.np_double_save(results,
RESULTS_FOLDER_PATH,
"results",
as_csv=True,
as_npy=True)
# plot.plt_validation_curve(training_score, test_score, param_range)
console_handler.close()
file_handler.close()
def calc():
if not os.path.exists(RESULTS_FOLDER_PATH):
os.makedirs(RESULTS_FOLDER_PATH)
logfile = os.path.join(RESULTS_FOLDER_PATH, "log.log")
if os.path.exists(logfile):
os.remove(logfile)
# logging stuff
level = logging.INFO
formats = {
"console": '\u001b[37m %(message)s\033[0m',
"file": '%(message)s'
}
file_handler, console_handler = logging.FileHandler(
logfile, "x"), logging.StreamHandler()
console_handler.setFormatter(logging.Formatter(formats["console"]))
file_handler.setFormatter(logging.Formatter(formats["file"]))
logger = logging.getLogger(__name__)
logger.addHandler(console_handler)
logger.addHandler(file_handler)
logger.setLevel(logging.INFO)
# begin calc
loaded_dataset = load_data()
logger.info("{} {}".format("loaded_dataset shape", loaded_dataset.shape))
# loaded_dataset["Label"] = DATASET_NAME.upper()
logger.info(loaded_dataset.head())
loaded_dataset.info()
logger.info(loaded_dataset['class'].value_counts())
dataset = None
logger.info("{} {}".format("Dataset shape BEFORE preparation",
loaded_dataset.shape))
dataset = datasets.prepare_dataset(
loaded_dataset,
# drop_columns=["Flow Bytes/s", "Flow Packets/s", "Fwd Header Length.1"],
shuffle=True,
dropna_axis=[0, 1])
loaded_dataset = None
logger.info("{} {}".format("Dataset shape AFTER preparation",
dataset.shape))
xTest, yTest = datasets.separate_labels(dataset,
encode=True,
column_name="class")
xTest = xTest.select_dtypes(exclude=['object'])
dataset = None
logger.info('Scaling dataset')
xTest = datasets.drop_variance(xTest)
standardScaler = StandardScaler()
xTestScaled = standardScaler.fit_transform(xTest)
logger.info("Performing PCA")
pca = PCA(random_state=42, n_components=0.95)
xTestPCA = pca.fit_transform(xTest)
logger.info("Dataset shape with PCA {}".format(xTestPCA.shape))
results_array = []
logger.info("Logistic Regression")
log_reg = LogisticRegression(verbose=0,
n_jobs=-1,
random_state=42,
max_iter=1000)
results = scoring.cross_validate_scoring(log_reg,
xTest,
yTest,
cv=10,
scoring=[
'roc_auc', 'f1', 'roc',
'precision', 'recall',
'confusion_matrix'
],
return_train_score=True)
save_result2(results_array, results, "Logistic Regression")
logger.info(results)
logger.info(results['confusion_matrix'])
logger.info("Logistic Regression Scaled")
log_reg = LogisticRegression(verbose=0,
n_jobs=-1,
random_state=42,
max_iter=1000)
results = scoring.cross_validate_scoring(log_reg,
xTestScaled,
yTest,
cv=10,
scoring=[
'roc_auc', 'f1', 'roc',
'precision', 'recall',
'confusion_matrix'
],
return_train_score=True)
save_result2(results_array, results, "Logistic Regression Scaled")
logger.info(results)
logger.info(results['confusion_matrix'])
logger.info("Logistic Regression PCA")
log_reg = LogisticRegression(verbose=0,
n_jobs=-1,
random_state=42,
max_iter=1000)
results = scoring.cross_validate_scoring(log_reg,
xTestPCA,
yTest,
cv=10,
scoring=[
'roc_auc', 'f1', 'roc',
'precision', 'recall',
'confusion_matrix'
],
return_train_score=True)
save_result2(results_array, results, "Logistic Regression PCA")
logger.info(results)
logger.info(results['confusion_matrix'])
logger.info("Naive Bayes")
gaussian_nb = GaussianNB()
results = scoring.cross_validate_scoring(gaussian_nb,
xTest,
yTest,
cv=10,
scoring=[
'roc_auc', 'f1', 'roc',
'precision', 'recall',
'confusion_matrix'
],
return_train_score=True)
save_result2(results_array, results, "Naive Bayes")
logger.info(results)
logger.info(results['confusion_matrix'])
logger.info("Naive Bayes Scales")
gaussian_nb = GaussianNB()
results = scoring.cross_validate_scoring(gaussian_nb,
xTestScaled,
yTest,
cv=10,
scoring=[
'roc_auc', 'f1', 'roc',
'precision', 'recall',
'confusion_matrix'
],
return_train_score=True)
save_result2(results_array, results, "Naive Bayes Scaled")
logger.info(results)
logger.info(results['confusion_matrix'])
logger.info("Naive Bayes DR")
gaussian_nb = GaussianNB()
results = scoring.cross_validate_scoring(gaussian_nb,
xTestPCA,
yTest,
cv=10,
scoring=[
'roc_auc', 'f1', 'roc',
'precision', 'recall',
'confusion_matrix'
],
return_train_score=True)
save_result2(results_array, results, "Naive Bayes PCA")
logger.info(results)
logger.info(results['confusion_matrix'])
logger.info("SVC Classifier")
linearSvc = LinearSVC(random_state=42, verbose=0,
dual=False) # svc classifier
results = scoring.cross_validate_scoring(linearSvc,
xTest,
yTest,
cv=10,
scoring=[
'roc_auc', 'f1', 'roc',
'precision', 'recall',
'confusion_matrix'
],
return_train_score=True)
save_result2(results_array, results, "SVC Normal")
logger.info(results)
logger.info(results['confusion_matrix'])
logger.info("SVC Classifier PCA")
linearSvc = LinearSVC(random_state=42, verbose=0,
dual=False) # svc classifier
results = scoring.cross_validate_scoring(linearSvc,
xTestPCA,
yTest,
cv=10,
scoring=[
'roc_auc', 'f1', 'roc',
'precision', 'recall',
'confusion_matrix'
],
return_train_score=True)
save_result2(results_array, results, "SVC Normal PCA")
logger.info(results)
logger.info(results['confusion_matrix'])
logger.info("SVC Classifier Scaled")
linearSvc = LinearSVC(random_state=42, verbose=0,
dual=False) #svc classifier
results = scoring.cross_validate_scoring(linearSvc,
xTestScaled,
yTest,
cv=10,
scoring=[
'roc_auc', 'f1', 'roc',
'precision', 'recall',
'confusion_matrix'
],
return_train_score=True)
save_result2(results_array, results, "SVC Scaled")
logger.info(results)
logger.info(results['confusion_matrix'])
logger.info("Decision Tree")
dec_tree = DecisionTreeClassifier(random_state=42)
results = scoring.cross_validate_scoring(dec_tree,
xTest,
yTest,
cv=10,
scoring=[
'roc_auc', 'f1', 'roc',
'precision', 'recall',
'confusion_matrix'
],
return_train_score=True)
save_result2(results_array, results, "Decision Tree")
logger.info(results)
logger.info(results['confusion_matrix'])
logger.info("Decision Tree Scaled")
dec_tree = DecisionTreeClassifier(random_state=42)
results = scoring.cross_validate_scoring(dec_tree,
xTestScaled,
yTest,
cv=10,
scoring=[
'roc_auc', 'f1', 'roc',
'precision', 'recall',
'confusion_matrix'
],
return_train_score=True)
save_result2(results_array, results, "Decision Tree Scaled")
logger.info(results)
logger.info(results['confusion_matrix'])
logger.info("Decision Tree PCA")
dec_tree = DecisionTreeClassifier(random_state=42)
results = scoring.cross_validate_scoring(dec_tree,
xTestPCA,
yTest,
cv=10,
scoring=[
'roc_auc', 'f1', 'roc',
'precision', 'recall',
'confusion_matrix'
],
return_train_score=True)
save_result2(results_array, results, "Decision Tree PCA")
logger.info(results)
logger.info(results['confusion_matrix'])
logger.info("Random Forest")
rnd_forest = RandomForestClassifier(n_estimators=100,
random_state=42,
verbose=0,
n_jobs=-1)
results = scoring.cross_validate_scoring(rnd_forest,
xTest,
yTest,
cv=10,
scoring=[
'roc_auc', 'f1', 'roc',
'precision', 'recall',
'confusion_matrix'
],
return_train_score=True)
save_result2(results_array, results, "Random Forest")
logger.info(results)
logger.info(results['confusion_matrix'])
logger.info("Random Forest Scaled")
rnd_forest = RandomForestClassifier(n_estimators=100,
random_state=42,
verbose=0,
n_jobs=-1)
results = scoring.cross_validate_scoring(rnd_forest,
xTestScaled,
yTest,
cv=10,
scoring=[
'roc_auc', 'f1', 'roc',
'precision', 'recall',
'confusion_matrix'
],
return_train_score=True)
save_result2(results_array, results, "Random Forest Scaled")
logger.info(results)
logger.info(results['confusion_matrix'])
logger.info("Random Forest PCA")
rnd_forest = RandomForestClassifier(n_estimators=100,
random_state=42,
verbose=0,
n_jobs=-1)
results = scoring.cross_validate_scoring(rnd_forest,
xTestPCA,
yTest,
cv=10,
scoring=[
'roc_auc', 'f1', 'roc',
'precision', 'recall',
'confusion_matrix'
],
return_train_score=True)
save_result2(results_array, results, "Random Forest PCA")
logger.info(results)
logger.info(results['confusion_matrix'])
console_handler.close()
file_handler.close()
def calc():
if not os.path.exists(RESULTS_FOLDER_PATH):
os.makedirs(RESULTS_FOLDER_PATH)
logfile = os.path.join(RESULTS_FOLDER_PATH, "log.log")
if os.path.exists(logfile):
os.remove(logfile)
# logging stuff
level = logging.INFO
formats = {
"console": '\u001b[37m %(message)s\033[0m',
"file": '%(message)s'
}
file_handler, console_handler = logging.FileHandler(
logfile, "x"), logging.StreamHandler()
console_handler.setFormatter(logging.Formatter(formats["console"]))
file_handler.setFormatter(logging.Formatter(formats["file"]))
logger = logging.getLogger(__name__)
logger.addHandler(console_handler)
logger.addHandler(file_handler)
logger.setLevel(logging.INFO)
# begin calc
benign = datasets.load_all(os.path.join(
"datasets", DATASET_NAME_2)) # load dataset from csv
scareware = datasets.load_all(os.path.join(
"datasets", DATASET_NAME)) # load dataset from csv
logger.info("{} {}".format("benign shape", benign.shape))
logger.info("{} {}".format("scareware shape", scareware.shape))
benign = datasets.prepare_dataset(benign, shuffle=True)
scareware = datasets.prepare_dataset(scareware, shuffle=True)
n_elements = min(benign.shape[0], scareware.shape[0], 150000)
benign = benign.head(n_elements)
scareware = scareware.head(n_elements)
logger.info("{} {}".format("benign shape after balancing", benign.shape))
logger.info("{} {}".format("scareware shape after balancing",
scareware.shape))
scareware["Label"] = DATASET_NAME.upper()
loaded_dataset = pd.concat([benign, scareware],
ignore_index=True) # union dataset
logger.info(loaded_dataset.head())
loaded_dataset.info()
benign = None
scareware = None
logger.info("{} {}".format("Dataset shape BEFORE preparation",
loaded_dataset.shape))
dataset = datasets.prepare_dataset(loaded_dataset,
drop_columns=[
"Flow Bytes/s", "Flow Packets/s",
"Flow ID", "Source IP",
"Destination IP", "Timestamp",
"Fwd Header Length.1"
],
shuffle=True,
dropna_axis=True)
loaded_dataset = None
logger.info("{} {}".format("Dataset shape AFTER preparation",
dataset.shape))
xTest, yTest = datasets.separate_labels(dataset, encode=True)
dataset = None
xTest = datasets.drop_variance(xTest)
roc_auc_scores = []
roc_fpr_tpr_thres = []
# Estimators number test
logger.info("Estimators number test")
for i in range(4, 30, 4):
n_estimators = i**2
logger.info("Training random forest with {} estimators ({})".format(
n_estimators, i))
clf = RandomForestClassifier(
n_estimators=n_estimators, n_jobs=-1,
random_state=42) # Random Forest Classifier
roc, auc_score = random_forest.fit_and_roc(clf, xTest, yTest)
save_result(roc, auc_score, "estimators", n_estimators,
roc_fpr_tpr_thres, roc_auc_scores)
# Max depth number test
roc_auc_scores = []
roc_fpr_tpr_thres = []
logger.info("max depth number test")
for i in range(1, 11):
max_depth = 2**i
logger.info("Training random forest with {} max depth ({})".format(
max_depth, i))
rnd_forest = RandomForestClassifier(
n_estimators=144, max_depth=max_depth, n_jobs=-1,
random_state=42) # Random Forest Classifier
roc, auc_score = random_forest.fit_and_roc(rnd_forest, xTest, yTest)
save_result(roc, auc_score, "max_depth", max_depth, roc_fpr_tpr_thres,
roc_auc_scores)
# Min Sample Leaf number test
roc_auc_scores = []
roc_fpr_tpr_thres = []
logger.info("Min Sample Leaf number test")
for i in range(1, 11):
min_sample_leaf = i
logger.info(
"Training random forest with {} min sample leaf ({})".format(
min_sample_leaf, i))
rnd_forest = RandomForestClassifier(
n_estimators=144,
max_depth=32,
min_samples_leaf=min_sample_leaf,
n_jobs=-1,
random_state=42) # Random Forest Classifier
roc, auc_score = random_forest.fit_and_roc(rnd_forest, xTest, yTest)
save_result(roc, auc_score, "min_sample_leaf", min_sample_leaf,
roc_fpr_tpr_thres, roc_auc_scores)
roc_auc_scores, roc_fpr_tpr_thres = [], []
xTest = None
yTest = None
file_handler.close()
console_handler.close()
def calc():
if not os.path.exists(RESULTS_FOLDER_PATH):
os.makedirs(RESULTS_FOLDER_PATH)
logfile = os.path.join(RESULTS_FOLDER_PATH, "log.log")
if os.path.exists(logfile):
os.remove(logfile)
# logging stuff
level = logging.INFO
formats = {
"console": '\u001b[37m %(message)s\033[0m',
"file": '%(message)s'
}
file_handler, console_handler = logging.FileHandler(
logfile, "x"), logging.StreamHandler()
console_handler.setFormatter(logging.Formatter(formats["console"]))
file_handler.setFormatter(logging.Formatter(formats["file"]))
logger = logging.getLogger(__name__)
logger.addHandler(console_handler)
logger.addHandler(file_handler)
logger.setLevel(logging.INFO)
# begin calc
loaded_dataset = load_data()
logger.info("{} {}".format("loaded_dataset shape", loaded_dataset.shape))
logger.info(loaded_dataset['Label'].value_counts())
# loaded_dataset["Label"] = DATASET_NAME.upper()
logger.info(loaded_dataset.head())
loaded_dataset.info()
dataset = None
logger.info("{} {}".format("Dataset shape BEFORE preparation",
loaded_dataset.shape))
dataset = datasets.prepare_dataset(
loaded_dataset,
drop_columns=["Flow Bytes/s", "Flow Packets/s", "Fwd Header Length.1"],
shuffle=True,
dropna_axis=[0, 1])
loaded_dataset = None
logger.info("{} {}".format("Dataset shape AFTER preparation",
dataset.shape))
xTest, yTest = datasets.separate_labels(dataset, encode=True)
dataset = None
xTest = datasets.drop_variance(xTest)
standardScaler = StandardScaler()
xTestScaled = standardScaler.fit_transform(xTest)
results = []
logger.info("Logistic Regression")
param_name = "C"
param_range = np.logspace(-1, 1, 10)
log_reg = LogisticRegression(verbose=1,
n_jobs=-1,
max_iter=1000,
solver="liblinear",
penalty="l2",
random_state=42)
train_scores, val_scores = validation_curve(log_reg,
xTest,
yTest,
param_name=param_name,
param_range=param_range,
cv=3,
scoring="roc_auc",
verbose=1,
n_jobs=-1)
results.append([param_name, param_range, train_scores, val_scores])
datasets.np_double_save(results,
RESULTS_FOLDER_PATH,
"results",
as_csv=True,
as_npy=True)
console_handler.close()
file_handler.close()
def calc():
if not os.path.exists(RESULTS_FOLDER_PATH):
os.makedirs(RESULTS_FOLDER_PATH)
logfile = os.path.join(RESULTS_FOLDER_PATH, "log.log")
if os.path.exists(logfile):
os.remove(logfile)
# logging stuff
level = logging.INFO
formats = {"console": '\u001b[37m %(message)s\033[0m', "file": '%(message)s'}
file_handler, console_handler = logging.FileHandler(logfile, "x"), logging.StreamHandler()
console_handler.setFormatter(logging.Formatter(formats["console"]))
file_handler.setFormatter(logging.Formatter(formats["file"]))
logger = logging.getLogger(__name__)
logger.addHandler(console_handler)
logger.addHandler(file_handler)
logger.setLevel(logging.INFO)
# begin calc
loaded_dataset = load_data()
logger.info("{} {}".format("loaded_dataset shape", loaded_dataset.shape))
logger.info(loaded_dataset['class1'].value_counts())
# loaded_dataset["Label"] = DATASET_NAME.upper()
logger.info(loaded_dataset['class1'].value_counts())
logger.info(loaded_dataset.head())
loaded_dataset.info()
dataset = None
logger.info("{} {}".format("Dataset shape BEFORE preparation", loaded_dataset.shape))
dataset = datasets.prepare_dataset(loaded_dataset,
# drop_columns=["Flow Bytes/s", "Flow Packets/s", "Fwd Header Length.1"],
shuffle=True, dropna_axis=[1])
loaded_dataset = None
logger.info("{} {}".format("Dataset shape AFTER preparation", dataset.shape))
xTest, yTest = datasets.separate_labels(dataset, encode=True, column_name="class1")
dataset = None
xTest = datasets.drop_variance(xTest)
standardScaler = StandardScaler()
xTestScaled = standardScaler.fit_transform(xTest)
results = []
clf = DecisionTreeClassifier(random_state=42)
param_name = "max_depth"
param_range = [2**i for i in range(1, 11)]
training_score, test_score = validation_curve(clf, xTest, yTest, param_name=param_name,
param_range=param_range,
scoring="roc_auc", cv=6, verbose=1, n_jobs=-1)
results.append([param_name, param_range, training_score, test_score])
datasets.np_double_save(results, RESULTS_FOLDER_PATH, "results", as_csv=True, as_npy=True)
param_name = "min_samples_leaf"
param_range = [i for i in range(1, 15)]
print(param_range)
training_score, test_score = validation_curve(clf, xTest, yTest, param_name=param_name,
param_range=param_range,
scoring="roc_auc", cv=6, verbose=1, n_jobs=-1)
results.append([param_name, param_range, training_score, test_score])
datasets.np_double_save(results, RESULTS_FOLDER_PATH, "results", as_csv=True, as_npy=True)
param_name = "max_features"
param_range = [1/i for i in range(1, 11)]
print(param_range)
training_score, test_score = validation_curve(clf, xTest, yTest, param_name=param_name,
param_range=param_range,
scoring="roc_auc", cv=6, verbose=1, n_jobs=-1)
results.append([param_name, param_range, training_score, test_score])
datasets.np_double_save(results, RESULTS_FOLDER_PATH, "results", as_csv=True, as_npy=True)
clf = DecisionTreeClassifier(min_samples_leaf=10)
train_sizes, train_scores, test_scores = learning_curve(clf, xTest, yTest, cv=6, n_jobs=-1,
train_sizes=np.linspace(0.1, 1, 10))
results = [train_sizes, train_scores, test_scores]
datasets.pk_save(results, RESULTS_FOLDER_PATH,
"learning_curves")
console_handler.close()
file_handler.close()
def calc():
if not os.path.exists(RESULTS_FOLDER_PATH):
os.makedirs(RESULTS_FOLDER_PATH)
logfile = os.path.join(RESULTS_FOLDER_PATH, "log.log")
if os.path.exists(logfile):
os.remove(logfile)
# logging stuff
level = logging.INFO
formats = {
"console": '\u001b[37m %(message)s\033[0m',
"file": '%(message)s'
}
file_handler, console_handler = logging.FileHandler(
logfile, "x"), logging.StreamHandler()
console_handler.setFormatter(logging.Formatter(formats["console"]))
file_handler.setFormatter(logging.Formatter(formats["file"]))
logger = logging.getLogger(__name__)
logger.addHandler(console_handler)
logger.addHandler(file_handler)
logger.setLevel(logging.INFO)
# begin calc
loaded_dataset = load_data()
logger.info("{} {}".format("loaded_dataset shape", loaded_dataset.shape))
# loaded_dataset["Label"] = DATASET_NAME.upper()
logger.info(loaded_dataset.head())
loaded_dataset.info()
logger.info(loaded_dataset['URL_Type_obf_Type'].value_counts())
dataset = None
logger.info("{} {}".format("Dataset shape BEFORE preparation",
loaded_dataset.shape))
dataset = datasets.prepare_dataset(
loaded_dataset,
# drop_columns=["Flow Bytes/s", "Flow Packets/s", "Fwd Header Length.1"],
shuffle=True,
dropna_axis=[0, 1])
loaded_dataset = None
logger.info("{} {}".format("Dataset shape AFTER preparation",
dataset.shape))
xTest, yTest = datasets.separate_labels(dataset,
encode=True,
column_name="URL_Type_obf_Type")
dataset = None
xTest = datasets.drop_variance(xTest)
standardScaler = StandardScaler()
xTestScaled = standardScaler.fit_transform(xTest)
results_array = []
logger.info("Logistic Regression")
log_reg = LogisticRegression(verbose=1, n_jobs=-1, max_iter=1000)
console_handler.close()
file_handler.close()