def setUp(self):
self.dataframe = data_reader(POINTS_DATA_FILE, resample=True)
data_size = len(self.dataframe)
train_size = int(data_size * 0.8)
self.test_size = data_size - train_size
self.train = self.dataframe[0:train_size:]
self.test = self.dataframe[train_size:data_size:]
self.model = HoltWinters(self.dataframe, season_period=2)
#: -*- coding: utf-8 -*-
import logging
from ipredictor.models import HoltWinters
from ipredictor.tools import data_reader
from ipredictor.plotter import Plotter
if __name__ == "__main__":
logging.basicConfig(level=logging.DEBUG)
data = data_reader('../ipredictor/tests/assets/points.csv')
train, test = data[:-5], data[-5:]
model = HoltWinters(train, season_period=2)
prediction = model.predict(steps=5)
plotter = Plotter()
plotter.add(train, color='b')
plotter.add(test, color='g')
plotter.add(prediction, color='r')
plot = plotter.prepare()
plot.show()
def test_if_data_is_resampled_when_necessary(self):
resampled = data_reader(POINTS_DATA_FILE, resample=True)
delta = resampled.index[1] - resampled.index[0]
self.assertEqual(delta.components.hours, 1)
def test_if_can_read_intervals(self):
dataframe = data_reader(INTERVALS_DATA_FILE, intervals=True)
self.assertIn('values', dataframe)
self.assertEqual(dataframe['values'].values[0].shape[1], 1)
def test_if_data_properly_formatted(self):
dataframe = data_reader(POINTS_DATA_FILE)
value = dataframe['values'][0]
self.assertEqual(type(value), np.float32)
def test_if_data_reader_returns_dataframe(self):
dataframe = data_reader(POINTS_DATA_FILE)
self.assertIsInstance(dataframe, pd.DataFrame)