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utils.py
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utils.py
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from skmultiflow.data import DataStream, WaveformGenerator, SEAGenerator, HyperplaneGenerator
import pandas as pd
import numpy as np
import csv
from OAL_classifier.clf_OALE import OALE_strategy
from OAL_classifier.clf_ROALE_DI import ROALE_DI_strategy
from OAL_strategies.str_MTSGQS import MTSGQS_strategy
from OAL_strategies.str_DSA_AI import DSA_AI_strategy
from OAL_strategies.str_US_fix import US_fix_strategy
from OAL_strategies.str_US_var import US_var_strategy
from OAL_strategies.str_CogDQS import CogDQS_strategy
from OAL_strategies.str_RS import RS_strategy
from OAL_strategies.str_DMI_DD import DMI_DD_strategy
from classifier.clf_BLS import BLS
from classifier.clf_SRP import SRP
from classifier.clf_DES import DES_ICD
from skmultiflow.bayes import NaiveBayes
from classifier.clf_ACDWM import ACDWM
from classifier.clf_OLI2DS import OLI2DS
from classifier.clf_BLS_QR import BLS_QR
from OSSL_classifier.clf_OSSBLS import OSSBLS
from OSSL_classifier.clf_ISSBLS import ISSBLS
from OSSL_classifier.clf_SOSELM import SOSELM
from classifier.clf_ARF import ARF
from skmultiflow.meta import LeveragingBaggingClassifier, OnlineUnderOverBaggingClassifier,OzaBaggingClassifier, OzaBaggingADWINClassifier, DynamicWeightedMajorityClassifier
from skmultiflow.meta import OnlineAdaC2Classifier
def get_stream(name):
if name == "Jiaolong":
with open('./datasets/Jiaolong_DSMS_V2.csv', 'r') as csvfile:
csvreader = csv.reader(csvfile)
next(csvreader, None)
X = []
Y = []
for row in csvreader:
data_row = row[:-1]
label = row[-1]
X.append(data_row)
Y.append(label)
X = np.array(X, dtype=float)
Y = np.array(Y, dtype=int)
stream = DataStream(X, Y)
elif name == "Waveform":
stream = WaveformGenerator(random_state=1)
elif name == "SEA":
stream = SEAGenerator(random_state=1)
elif name == "Hyperplane":
stream = HyperplaneGenerator(random_state=1)
else:
data = pd.read_csv('./datasets/' + name + '.csv')
data = data.values
vol, col = data.shape
X = data[:, 0:col - 1]
Y = data[:, col - 1]
Y = np.array([int(i) for i in Y])
stream = DataStream(X, Y)
return stream
def get_pt(stream, n_pt):
data, labels = stream.next_sample(n_pt)
return data, labels
class para_init:
def __init__(self, X_pt_source=np.array([[]]), y_pt_source=np.array([[]]), n_class=2, n_ratio_max=0.2, n_anchor=10, theta=0.2):
self.n_class = n_class
self.X_pt_source = X_pt_source
self.y_pt_source = y_pt_source
self.n_ratio_max = n_ratio_max
self.n_anchor = n_anchor
self.theta = theta
def get_method(self, name):
if name == "ROALE_DI":
return ROALE_DI_strategy(self.X_pt_source, self.y_pt_source, L=self.n_class)
if name == "OALE":
return OALE_strategy(self.X_pt_source, self.y_pt_source, L=self.n_class)
def get_clf(self, name):
if name == "clf_ARF":
return ARF()
elif name == "clf_LB":
return LeveragingBaggingClassifier()
elif name=="clf_OB":
return OzaBaggingClassifier()
elif name=="clf_OBADWIN":
return OzaBaggingADWINClassifier()
elif name=='clf_DWM':
return DynamicWeightedMajorityClassifier()
elif name == "clf_OOB":
return OnlineUnderOverBaggingClassifier()
elif name == "clf_SRP":
return SRP(n_estimators=3, n_class=self.n_class)
elif name == "clf_AdaC2":
return OnlineAdaC2Classifier()
elif name == "clf_BLS_QR":
return BLS_QR(Nf=20,
Ne=10,
N1=20,
N2=10,
M1=1,
M2=5,
E1=1,
E2=100,
E3=1,
map_function='sigmoid',
enhence_function='sigmoid',
reg=0.001,
n_class=3)
elif name == "clf_BLS":
return BLS(Nf=40,
Ne=10,
N1=60,
N2=10,
map_function='sigmoid',
enhence_function='sigmoid',
reg=0.001)
elif name == "clf_OSSBLS":
return OSSBLS(Nf=10,
Ne=10,
N1=10,
N2=10,
map_function='sigmoid',
enhence_function='sigmoid',
reg=0.001,
gamma=0.005,
n_anchor=10)
elif name == "clf_OSSBLS":
return OSSBLS(Nf=10,
Ne=10,
N1=10,
N2=10,
map_function='sigmoid',
enhence_function='sigmoid',
reg=0.001,
gamma=0.001,
n_anchor=self.n_anchor,
n_class=3)
elif name == "clf_ISSBLS":
return ISSBLS(
Nf=10,
Ne=10,
N1=10,
N2=10,
map_function='sigmoid',
enhence_function='sigmoid',
reg=0.001,
gamma=0.05)
elif name == "clf_SOSELM":
return SOSELM(
Ne=20,
N2=10,
enhence_function='sigmoid',
reg=0.001,
gamma=0.05)
elif name == "clf_NB":
return NaiveBayes()
elif name == "clf_DES":
return DES_ICD(base_classifier=NaiveBayes(), window_size=50, max_classifier=10)
elif name == "clf_DES_5":
return DES_ICD(base_classifier=NaiveBayes(), window_size=50, max_classifier=5)
elif name == "clf_ACDWM":
return ACDWM(chunk_size=0, max_ensemble_size=10)
elif name == "clf_OLI2DS":
return OLI2DS(C=0.0100000, Lambda=30, B=1, theta=8, gama=0, sparse=0, mode="capricious")
raise ValueError("Not valid")
def get_str(self, name, chunk_size, query_size, clf):
name = name + "_str"
if name == "DSA_AI_str":
return DSA_AI_strategy(n_class=self.n_class, X_memory_collection=self.X_pt_source,
y_memory_collection=self.y_pt_source, d=self.X_pt_source.shape[1],
kappa=3, gamma=0.4)
elif name == "Supervised_str":
return None
elif name == "MTSGQS_str":
return MTSGQS_strategy(n_class=self.n_class, kappa=2, gamma=0.4, n_capacity=100)
elif name == "US_fix_str":
return US_fix_strategy(theta=0.5)
elif name == "US_var_str":
return US_var_strategy(theta=0.5)
elif name == "CogDQS_str":
return CogDQS_strategy(B=0.25, n=1, c=3, cw_size=10, window_size=200, s=0.01)
elif name == "RS_str":
return RS_strategy(label_ratio=self.n_ratio_max)
if name == "DMI_DD_str":
return DMI_DD_strategy(n_class=self.n_class, chunk_size=chunk_size, query_size=query_size, clf=clf, X_pt=self.X_pt_source, y_pt=self.y_pt_source)
raise ValueError("Not valid")
# def clf_init(self):
# clf_ARF = ARF()
# clf_SRP = SRP()
# clf_BLS = BLS(Nf=10,
# Ne=10,
# N1=10,
# N2=10,
# map_function='sigmoid',
# enhence_function='sigmoid',
# reg=0.001)
# clf_OSSBLS = OSSBLS(
# Nf=10,
# Ne=10,
# N1=10,
# N2=10,
# map_function='sigmoid',
# enhence_function='sigmoid',
# reg=0.001,
# gamma=0.05,
# n_anchor=10)
# clf_ISSBLS = ISSBLS(
# Nf=10,
# Ne=10,
# N1=10,
# N2=10,
# map_function='sigmoid',
# enhence_function='sigmoid',
# reg=0.001,
# gamma=0.05)
#
# clf_SOSELM = SOSELM(
# Ne=20,
# N2=10,
# enhence_function='sigmoid',
# reg=0.001,
# gamma=0.05)
#
# return clf_ARF, clf_SRP, clf_BLS, clf_OSSBLS, clf_ISSBLS, clf_SOSELM
# def str_init(self):
# DSA_AI_str = DSA_AI_strategy(n_class=self.n_class, X_memory_collection=self.X_pt_source, y_memory_collection=self.y_pt_source, d=self.X_pt_source.shape[1],
# kappa=2, gamma=0.4)
# MTSGQS_str = MTSGQS_strategy(n_class=self.n_class, kappa=2, gamma=0.4, n_capacity=100)
# US_fix_str = US_fix_strategy(theta=0.5)
# US_var_str = US_var_strategy(theta=0.5)
# CogDQS_str = CogDQS_strategy(B=0.25, n=1, c=2, cw_size=10, window_size=200, s=0.01)
# RS_str = RS_strategy(label_ratio=self.n_ratio_max)
# return DSA_AI_str, MTSGQS_str, US_fix_str, US_var_str, CogDQS_str, RS_str