import itertools as it
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
import numpy as np
from src.dataloaders import trainingset
from src.utils2 import get_path, c, L
L = list(L[4:])
D = trainingset()
path = get_path(__file__) + '/..'
savepath_template = '{0}/plots/scatterplots/{1}-{2}.pdf'
num_points = 100
rows = np.random.random_integers(0,D.shape[0]-1, num_points)
data = D[rows,:]
colors = map(lambda x: 'blue' if x==1 else 'red', data[:,c('IsAlert')])
blue = Rectangle((0,0),1,1,fc='b')
red = Rectangle((0,0),1,1,fc='r')
exclude = ['V7', 'V9', 'P8', 'E3', 'E7', 'E8', 'E9', 'V3', 'V5', 'V10']
features = [f for f in L if f not in exclude]
for f1, f2 in it.combinations(features, 2):
plt.title('Feature {0} vs {1} ({2} points)'.format(f1, f2, num_points),
{'size': 20})
plt.legend((blue, red), ('Alert', 'Not Alert'))
import numpy as np
import src.dataloaders as d
from src.utils2 import create_extended_dataset_window
TrnD_ex = create_extended_dataset_window(d.trainingset())
TstD_ex = create_extended_dataset_window(d.testset())
np.save('data/trainingset_extended_window_30.npy', TrnD_ex)
np.save('data/testset_extended_window_30.npy', TstD_ex)
