def save_result2(results_array, results: dict, name: str):
results['label'] = name
results_array.append(results)
datasets.pk_save(results_array, RESULTS_FOLDER_PATH, 'results')
datasets.np_double_save(results_array,
RESULTS_FOLDER_PATH,
'results',
as_csv=True)
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()