import os
import numpy as np
import pandas as pd
import utils
class DeviceSessionClassifier:
""" Classifier used for determining whether a given session originated from a specific device or not """
def __init__(self,
dev_name,
model,
is_model_pkl=False,
use_cols=None,
y_col=None,
train=None,
is_train_csv=False):
self.dev_name = dev_name
if is_model_pkl:
self.load_model_from_pkl(model)
else:
self.model = model
if use_cols is not None:
self.use_cols = use_cols
else:
self.use_cols = pd.read_csv(os.path.abspath('data/use_cols.csv'))
if y_col:
self.y_col = y_col
else:
self.y_col = 'device_category'
if train:
if is_train_csv:
train = utils.load_data_from_csv(train, use_cols)
elif use_cols:
train = train[use_cols]
self.train(train)
def is_dev(self, dev_name):
return 1 if dev_name == self.dev_name else 0
def get_is_dev_vec(self, dev_names):
return [self.is_dev(dev_name) for dev_name in dev_names]
def train(self, train):
x_train, y_train = utils.split_data(train, y_col=self.y_col)
self.model.fit(x_train, y_train)
def predict(self, sessions):
if isinstance(sessions, pd.DataFrame):
return self.model.predict(sessions.values)
else:
return self.model.predict(sessions)
def load_model_from_pkl(self, pkl):
self.model = utils.load_model_from_pkl(pkl)
def split_data(self, data):
x, y = utils.split_data(data, self.y_col)
y = self.get_is_dev_vec(y)
return x, y
def eval_on_dataset(self, dataset, is_dataset_csv=False):
if is_dataset_csv:
dataset = utils.load_data_from_csv(dataset, self.use_cols)
# Split data to features and labels
x, y_true = self.split_data(dataset)
# Classify data
y_pred = self.predict(x)
# Evaluate predictions
return utils.eval_predictions(y_true, y_pred)
