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)