import matplotlib.pyplot as plt
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import encoders as en
import scipy as sp
from detector import ChangeDetector
from online_simulator import OnlineSimulator
from ddm_detector import DDMDetector
from utils import *
#Numerical data
df = pd.read_csv('sequences/sequence_2017_11_28-18.07.57.csv')
seq = np.array(df['attr_1'])
detector = DDMDetector(lambd=25)
simulator = OnlineSimulator(None,
[detector],
[seq],
['attr_1'])
simulator.run(plot=True, detect_rules=False)
detected_change_points = np.array(simulator.get_detected_changes())
print_detected_change_points(detected_change_points)
plt.show()
import sys
sys.path.append('./detectors/')
sys.path.insert(0, './rules_mining/')
import numpy as np
import pandas as pd
import encoders as en
from utils import *
from online_simulator import OnlineSimulator
from adwin_detector import AdwinDetector
from rules_detector import RulesDetector
#Numerical data
#df = pd.read_csv('sequences/sequence_2017_11_24-20.16.00.csv')
df = pd.read_csv('sequences/sequence_2018_04_15-22.22.16.csv')
seq_1 = np.array(df['attr_1'])
seq_2 = np.array(df['attr_4'])
detector_1 = AdwinDetector(delta=0.01)
detector_2 = AdwinDetector(delta=0.01)
rules_detector = RulesDetector(target_seq_index=1)
simulator = OnlineSimulator(rules_detector, [detector_1, detector_2],
[seq_1, seq_2], ['attr_1', 'attr_2'])
simulator.run(plot=True, detect_rules=True)
print_rules(simulator.get_rules_sets(), 1)
detector2 = ZScoreDetector(window_size=25, threshold=4)
# detector1 = ZScoreDetector(window_size = 30, threshold=5)
detector1 = ZScoreDetector(window_size=30, threshold=4.5)
detector3 = ZScoreDetector(window_size=30, threshold=4)
target_seq_index = 1
rules_detector = RulesDetector(target_seq_index=target_seq_index,
window_size=1440,
round_to=60,
type="all",
combined=False)
simulator = OnlineSimulator(rules_detector, [detector1, detector2, detector3],
sequences,
seq_names,
round_to=60,
predict_ratio=predict_ratio)
simulator.label_encoder = lb
start_time = time.time()
simulator.run(plot=True, detect_rules=True, predict_seq=True)
discovered_rules = simulator.get_rules_sets()
# print_detected_change_points(simulator.get_detected_changes())
# print_rules(simulator.get_rules_sets(), 3)
print_best_rules(discovered_rules)
# print_rules(simulator.get_rules_sets(), 1)
#print_rules_for_attr(discovered_rules, 'light', 1)
end_time = time.time()
print(end_time - start_time)
from zscore_detector import ZScoreDetector
from utils import *
#Numerical data
df = pd.read_csv('sequences/sequence_2018_07_14-18.24.58.csv')
df = pd.read_csv('sequences/sequence_2018_07_21-22.24.18.csv')
seq_1 = np.array(df['attr_1'])
seq_2 = np.array(df['attr_2'])
seq_3 = np.array(df['attr_3'])
seq_4 = np.array(df['attr_4'])
win_size = 30
detector_1 = ZScoreDetector(window_size = win_size, threshold=3.5)
detector_2 = ZScoreDetector(window_size = win_size, threshold=4.5)
#detector_3 = ZScoreDetector(window_size = win_size, threshold=3.5)
detector_4 = ZScoreDetector(window_size = win_size, threshold=4.5)
simulator = OnlineSimulator(None,
[detector_1, detector_2, detector_4],
[seq_1, seq_2, seq_4],
["attr_1", "attr_2", "attr_4"])
simulator.run(plot=True, detect_rules=False)
detected_change_points = simulator.get_detected_changes()
#print(np.array(detected_change_points))
print_detected_change_points(detected_change_points)
plt.show()
detector1 = ZScoreDetector(window_size=30, threshold=5)
detector2 = ZScoreDetector(window_size=win_size, threshold=4)
detector3 = ZScoreDetector(window_size=win_size, threshold=4)
detector4 = ZScoreDetector(window_size=win_size, threshold=5)
target_seq_index = 3
rules_detector = RulesDetector(target_seq_index=target_seq_index,
window_size=0,
round_to=100,
type="all",
combined=False)
simulator = OnlineSimulator(rules_detector,
[detector1, detector2, detector3, detector4],
sequences,
seq_names,
predict_ratio=predict_ratio)
simulator.random_subsequences = False
start_time = time.time()
simulator.run(plot=True, detect_rules=True, predict_seq=True)
#print_detected_change_points(simulator.get_detected_changes())
#print_rules(simulator.get_rules_sets(), 0)
end_time = time.time()
print("Rules used for prediction:")
for br in simulator.best_rules:
# round_to=100)
rules_detector = AllRulesGenerator(target_seq_index=target_seq_index,
window_size=0,
round_to=100)
# rules_detector = ClosedRulesGenerator(target_seq_index=target_seq_index,
# window_size=0,
# round_to=100)
rules_detector = DiscretizedDatasetGenerator(target_seq_index=target_seq_index,
window_size=0,
round_to=100)
simulator = OnlineSimulator(rules_detector,
[detector1, detector2, detector3, detector4],
sequences, seq_names)
# simulator.label_encoder = lb
start_time = time.time()
simulator.run(plot=True, detect_rules=True, predict_seq=False)
discovered_rules = simulator.get_rules_sets()
#print_detected_change_points(simulator.get_detected_changes())
# print_rules(simulator.get_rules_sets(), 5)
# print_combined_rules(simulator.get_combined_rules(), 0)
# print_rules(discovered_rules, 1)
#print_best_rules(discovered_rules)
# print_rules_for_attr(discovered_rules, 'light', 1)
import sys
sys.path.append('./detectors/')
sys.path.insert(0, './rules_mining/')
import pandas as pd
import numpy as np
from online_simulator import OnlineSimulator
from page_hinkley_detector import PageHinkleyDetector
from utils import *
#Numerical data
df = pd.read_csv('sequences/sequence_2018_03_25-17.24.22.csv')
seq1 = np.array(df['attr_1'])
seq2 = np.array(df['attr_2'])
seq3 = np.array(df['attr_3'])
seq4 = np.array(df['attr_4'])
detector1 = PageHinkleyDetector(delta=0.001, lambd=20, alpha=0.99)
detector2 = PageHinkleyDetector(delta=0.001, lambd=20, alpha=0.99)
detector3 = PageHinkleyDetector(delta=0.001, lambd=30, alpha=0.99)
detector4 = PageHinkleyDetector(delta=0.001, lambd=20, alpha=0.99)
simulator = OnlineSimulator(None, [detector1, detector2, detector3, detector4],
[seq1, seq2, seq3, seq4],
["attr_1", "attr_2", "attr_3", "attr_4"])
simulator.run(plot=True, detect_rules=False)
detected_change_points = np.array(simulator.get_detected_changes())
print_detected_change_points(detected_change_points)
sequences[i] = np.concatenate((seq, base_seqs[i]))
win_size = 20
detector1 = ZScoreDetector(window_size = 30, threshold=5)
detector2 = ZScoreDetector(window_size = win_size, threshold=4.5)
detector3 = ZScoreDetector(window_size = win_size, threshold=5.5)
detector4 = ZScoreDetector(window_size = win_size, threshold=4)
rules_detector = RulesDetector(target_seq_index=3,
window_size=0,
round_to=100,
type="all",
combined=True)
simulator = OnlineSimulator(rules_detector,
[detector1, detector2, detector3, detector4],
sequences,
seq_names)
start_time = time.time()
simulator.run(plot=False, detect_rules=True, predict_seq=False)
# print_detected_change_points(simulator.get_detected_changes())
# generate_classical_dataset(simulator.get_detected_changes())
# print_rules(simulator.get_rules_sets(), 0)
print_combined_rules(simulator.get_combined_rules(), 0)
# for cr in simulator.get_combined_rules():
# print(cr)