"""
Hyperparameters overview
"""
import numpy as np
import matplotlib.pyplot as plt
import helper as h
import csm
np.set_printoptions(precision=3)
p = 0.05
# Select streams and methods
streams = h.streams()
clfs = h.clfs()
# Stream Variables
drift_types = ["incremental", "sudden"]
ldistributions = [[0.1, 0.9], [0.2, 0.8], [0.3, 0.7], [0.4, 0.6]]
random_states = [1337, 666, 42]
label_noises = [0.0, 0.1, 0.2, 0.3]
# Prepare storage for results
chunk_size = next(iter(streams.values())).chunk_size
n_chunks = next(iter(streams.values())).n_chunks
score_points = list(range(chunk_size, chunk_size * n_chunks, chunk_size))
def gather_and_present(title, filename, streams, what, e):
results_hypercube = np.zeros((len(streams), len(clfs), n_chunks - 1))
for i, stream_n in enumerate(streams):
results = np.load("results/experiment_%i/%s.npy" % (e, stream_n))
specificity)
import sys
from sklearn.base import clone
from sklearn.tree import DecisionTreeClassifier
from skmultiflow.trees import HoeffdingTree
if len(sys.argv) != 2:
print("PODAJ RS")
exit()
else:
random_state = int(sys.argv[1])
print(random_state)
# Select streams and methods
streams = h.streams(random_state)
print(len(streams))
ob = OnlineBagging(n_estimators=20, base_estimator=GaussianNB())
oob = OOB(n_estimators=20, base_estimator=GaussianNB())
uob = UOB(n_estimators=20, base_estimator=GaussianNB())
ros_knorau2 = SEA(base_estimator=StratifiedBagging(base_estimator=GaussianNB(),
random_state=42,
oversampler="ROS"),
oversampled="ROS",
des="KNORAU2")
cnn_knorau2 = SEA(base_estimator=StratifiedBagging(base_estimator=GaussianNB(),
random_state=42,
oversampler="CNN"),
oversampled="CNN",