experiment1_GNB.py

import csm
import numpy as np
import helper as h
from tqdm import tqdm
import multiprocessing
from csm import OOB, UOB, SampleWeightedMetaEstimator, Dumb, MDET, SEA, StratifiedBagging
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.toystreams(random_state)

print(len(streams))

sea = SEA(base_estimator=StratifiedBagging(base_estimator=GaussianNB(), random_state=42))
knorau1 = SEA(base_estimator=StratifiedBagging(base_estimator=GaussianNB(), random_state=42), des="KNORAU1")
knorau2 = SEA(base_estimator=StratifiedBagging(base_estimator=GaussianNB(), random_state=42), des="KNORAU2")
knorae1 = SEA(base_estimator=StratifiedBagging(base_estimator=GaussianNB(), random_state=42), des="KNORAE1")
knorae2 = SEA(base_estimator=StratifiedBagging(base_estimator=GaussianNB(), random_state=42), des="KNORAE2")

clfs = (sea, knorau1, knorau2, knorae1, 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/experiment1_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()