experiment5_GNB.py

import csm
import numpy as np
import helper as h
from tqdm import tqdm
import multiprocessing
from csm import SEA, StratifiedBagging, REA, LearnppCDS, LearnppNIE, OUSE, KMeanClustering
from strlearn.evaluators import TestThenTrain
from sklearn.naive_bayes import GaussianNB
from strlearn.metrics import (
    balanced_accuracy_score,
    f1_score,
    geometric_mean_score_1,
    precision,
    recall,
    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))

rea = REA(base_classifier=StratifiedBagging(base_estimator=GaussianNB(
), random_state=42), number_of_classifiers=5)
cds = LearnppCDS(base_classifier=StratifiedBagging(base_estimator=GaussianNB(
), random_state=42), number_of_classifiers=5)
nie = LearnppNIE(base_classifier=StratifiedBagging(base_estimator=GaussianNB(
), random_state=42), number_of_classifiers=5)
ouse = OUSE(base_classifier=StratifiedBagging(base_estimator=GaussianNB(
), random_state=42), number_of_classifiers=5)
kmc = KMeanClustering(base_classifier=StratifiedBagging(base_estimator=GaussianNB(
), random_state=42), number_of_classifiers=5)
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", des="KNORAU2")
ros_knorae2 = SEA(base_estimator=StratifiedBagging(base_estimator=GaussianNB(
), random_state=42, oversampler="ROS"), oversampled="ROS", des="KNORAE2")
cnn_knorae2 = SEA(base_estimator=StratifiedBagging(base_estimator=GaussianNB(), random_state=42, oversampler = "CNN"), oversampled="CNN" ,des="KNORAE2")

clfs = (rea, ouse, kmc, cds, nie, ros_knorau2, cnn_knorau2, ros_knorae2, cnn_knorae2)

# Define worker
def worker(i, stream_n):
    stream = streams[stream_n]
    cclfs = [clone(clf) for clf in clfs]

    print("Starting stream %i/%i" % (i + 1, len(streams)))

    eval = TestThenTrain(metrics=(
        balanced_accuracy_score,
        geometric_mean_score_1,
        f1_score,
        precision,
        recall,
        specificity
    ))
    eval.process(
        stream,
        cclfs
    )

    print("Done stream %i/%i" % (i + 1, len(streams)))

    results = eval.scores

    np.save("results/experiment5_GNB/%s" % stream, results)


jobs = []
for i, stream_n in enumerate(streams):
    p = multiprocessing.Process(target=worker, args=(i, stream_n))
    jobs.append(p)
    p.start()